In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the ...learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring-summer 2020 semester. Our study focused on (1) the students' acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study's results and derive short- and long-term implications for science and practice.
Shortening product development cycles and fully customisable 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 optimisation 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 analyse safety aspects and demonstrate reliability under prevailing conditions.
Digital software platforms allow third parties to develop applications and thus extend their functionality. Platform owners provide platform boundary resources that allow for application development. ...For developers, platform integration, understood as the employment of platform resources, helps to realize application functionality effectively. Simultaneously, it requires integration effort and increases dependencies. Developers are interested to know whether integration contributes to success in hypercompetitive platform settings. While aspects of platform participation have been studied, research on a comprehensive notion of integration and related implications are missing. By proposing a platform integration model, this study supports a better understanding of integration. Concerning dynamics related to integration, effects were tested using information from over 82,000 Apple AppStore applications. Regression model analysis reveals that application success and customer satisfaction is positively influenced by platform integration. To achieve superior results, developers should address multiple aspects of integration, such as devices, data, the operating system, the marketplace as well as other applications, and provide updates. Finally, the study highlights the importance for all platform participants and their possibilities to employ integration as a strategic instrument.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This ...approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Digital platforms, by their design, allow the coordination of multiple entities to achieve a common goal. In the public sector, different understandings of the platform concept prevail. To guide the ...development and further re-search 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 by providing a framework with constitutive elements, that may guide future analysis. Apart from chance regarding coordination, platforms are well suited to support contextual eGovernment targets. Among them is service personalization. Highly individualized service offerings support targets such as No Stop government. To this end, the paper extends the framework for service personalization in the public sector and exemplifies related aspects using a reference case.
Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and ...increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
•Research review reveals increased interest in neural network-based planning and control approaches.•The review includes 129 articles for an architecture-oriented classification of neural network-based production planning and control approaches.•Neural network-based approaches outperform corresponding benchmarks.•A novel taxonomy allows for the classification of architectural characteristics of neural network based approaches and multi-agent interaction.•The applicability to the real-world is low, by far most benchmarks are conducted via simulation.
•We show the prevalence of email tracking in marketing communication.•We propose features that facilitate tracking detection using machine learning.•The new features are resilient against ...manipulation by trackers.•We assess the detection model through out-of-time-and-universe validation.•Tree learning algorithms achieve high detection rates and few false alarms.
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking experiment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders.
Traditional production systems are enhanced by cyber-physical systems (CPSs) and Internet of Things. As kind of next generation systems, those cyber-physical production systems (CPPSs) are able to ...raise the level of autonomy of its production components. To find the optimal degree of autonomy in a given context, a research approach is formulated using a simulation concept. Based on requirements and assumptions, a cyber-physical market is modeled and qualitative hypotheses are formulated, which will be verified with the help of the CPPS of a hybrid simulation environment. Big Data Analytics can be used to extract influence factors which explain the optimal degree of autonomy.
E-mail tracking provides companies with fine-grained behavioral data about e-mail recipients, which can be a threat for individual privacy and enterprise security. This problem is especially severe ...since e-mail tracking techniques often gather data without the informed consent of the recipients. So far e-mail recipients lack a reliable protection mechanism.
This article presents a novel protection framework against e-mail tracking that closes an important gap in the field of enterprise security and privacy-enhancing technologies. We conceptualize, implement and evaluate an anti-tracking mail server that is capable of identifying tracking images in e-mails via machine learning with very high accuracy, and can selectively replace them with arbitrary images containing warning messages for the recipient. Our mail protection framework implements a selective prevention strategy as enterprise-grade software using the design science research paradigm. It is flexibly extensible, highly scalable, and ready to be applied under actual production conditions. Experimental evaluations show that these goals are achieved through solid software design, adoption of recent technologies and the creation of novel flexible software components.
•Conceptualization of a novel protection framework against e-mail tracking.•First server-side implementation of reliable e-mail tracking protection.•Framework shows a high accuracy without any manual user effort.•Framework is developed as enterprise-grade software (extensible, scalable).
The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling ...to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.
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