COVID-19, Digital Transformation of Banks, and Operational Capabilities of Commercial Banks

The development of financial technology has promoted the innovation and digital transformation of commercial banks. Through digital transformation, commercial banks can improve bank efficiency and operational capabilities. Through empirical analysis, this study explored the relationship between digital bank transformation and commercial bank operating capabilities and how COVID-19, bank categories, and enterprise life cycles affect the relationship between digital bank transformation and commercial bank operating capabilities. This study selected data from China's commercial banks from 2011 to 2021 and used the regression method of fixed effects to conduct an empirical analysis. The research results show that the digital transformation of banks has improved the operational capabilities of commercial banks. Further analysis showed that the emergence of COVID-19 has negatively affected their relationship. At the same time, compared with rural commercial banks and commercial banks in the recession and phase-out periods, non-rural commercial banks and commercial banks in the growth and maturity stages play a more vital moderating role in the impact of the digital transformation of banks on the financial performance of commercial banks. The main research object of this study is Chinese commercial banks, and this study examines the results of banks' digital transformation and enriches the research on digital transformation. At the same time, this study is helpful to investors who like investment banks and has good practical significance.

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

The Use of AI in Managing Big Data Analysis Demands: Status and Future updates Directions

In the modern age, the context of health, energy, environment, climate crisis, and global Covid-19 pandemic, managing Big Data demands via Sustainable Development Goals and disease mitigation supported by Artificial Intelligence, present significant challenges for a given territory or national boundaries' policies, legal systems, energy infrastructure, societal cohesion, internal and external national security. We look at policy, technical, and legal applications alongside ramifications of relevant policies and practices to highlight key challenges from a global and societal context. This review contributes to developing further awareness of the complexity and demands on policy and technology. In the long term due to these significant changes, inferences of multifaceted policy and data acquisition could present additional compounding challenges regarding civil liberties, data privacy law, and equitable health outcomes, whilst implementing continually evolving policies, practices, and techniques that can weaken infrastructure and present cyber-attack vulnerabilities. As a consequence of local, regional, and international paradigm shifts, Blockchain and Smart Contracts are suggested as part of a solution in providing data protection, transparency, and validity with transactional data to enable further trust between private and public sectors during times of crisis and technological transition processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Market development strategy and process performance of knowledge-intensive SMEs in the epidemic era: a process-oriented perspective

PurposeThis study analyzes in-depth how knowledge-intensive small and medium-sized enterprises (SMEs) can achieve higher new product development (NPD) process performance in the epidemic era and examine the internal development mechanism of knowledge-intensive SMEs in the process of continuous digital transformation.Design/methodology/approachThis issue is tested with partial least squares on data collected via a survey conducted from November 2021 to February 2022. The sample comprises 487 knowledge-intensive SMEs operating in China.FindingsThe results indicate that one form of cross-functional ambidexterity, market development strategy (MDS), plays an important role in process performance from an inside-out financial perspective and an outside-in customer perspective. Simultaneously, product innovation efficiency (PIE) mediates the relationship between MDS and the above results. Big data analytics capabilities (BDACs) positively regulate the relationship between MDS and PIE.Research limitations/implicationsThe authors do not consider other contingency factors. Future research should introduce influential factors such as leadership and competitive intensity to further distinguish the effects of MDS on NPD process performance.Practical implicationsThe study findings offer suggestions to help knowledge-intensive SME managers better manage their NPD process by making better use of their limited resources in developing countries such as China.Originality/valueThis study is one of only a few to adopt a process-oriented perspective to specifically examine how one form of cross-functional ambidexterity, MDS, impacts knowledge-intensive SME process performance in the epidemic era. This study also extends the theoretical framework of cross-functional ambidexterity to BDAC research.

Agricultural Social Networks: An Agricultural Value Chain-Based Digitalization Framework for an Inclusive Digital Economy

Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the industrialization of economies, food security systems are under pressure to sustain communities. This situation calls for the implementation of innovative solutions to increase and sustain efficacy from farm to table. Agricultural social networks (ASNs) are central in agriculture value chain (AVC) management and sustainability and consist of a complex network inclusive of interdependent actors such as farmers, distributors, processors, and retailers. Hence, social network structures (SNSs) and practices are a means to contextualize user scenarios in agricultural value chain digitalization and digital solutions development. Therefore, this research aimed to unearth the roles of agricultural social networks in AVC digitalization, enabling an inclusive digital economy. We conducted automated literature content analysis followed by the application of case studies to develop a conceptual framework for the digitalization of the AVC toward an inclusive digital economy. Furthermore, we propose a transdisciplinary framework that guides the digitalization systematization of the AVC, while articulating resilience principles that aim to attain sustainability. The outcomes of this study offer software developers, agricultural stakeholders, and policymakers a platform to gain an understanding of technological infrastructure capabilities toward sustaining communities through digitalized AVCs.

Guest editorial: Post-COVID-19 sustainable supply chain management in emerging markets

Since the early 2000s, emerging markets have become the heart of global supply chains hosting a large volume of industrial productions. The second article looked into the barriers to attaining sustainability in supply chain of the Bangladeshi pharmaceutical sector and developed a hierarchical structure of those barriers using interpretive structural modeling and MICMAC analysis. The eleventh article explored a new way to assess suppliers' suitability by considering pseudo-resilience factors to achieve SSC in the post-COVID-19 era using an analytical hierarchy process and R. It also provided a case study of three smartphone processor suppliers (Jessin et al., 2023).

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Geo-Economy of the Future: Sustainable Agriculture and Alternative Energy: Volume II

This book presents an international review of the modern geo-economy and a scientific take on the geo-economy of the future. It identifies the challenges of climate change and their impact on the modern geo-economy. Prospects for the geo-economy of the future are outlined based on sustainable agriculture and alternative energy. Policy implications are put forward to develop a geo-economy of the future in response to the challenges of climate change. The book presents management implications for the development of the geo-economy of the future in response to the challenges of climate change at the regional and global scale. It presents the lessons-learned through the COVID-19 pandemic, and applies experiences of countries with different environmental conditions for agriculture and the development of the energy sector. Based on these results, advanced practical recommendations and ready-made frameworks at the national, regional, and enterprise level are provided. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Drivers of digital supply chain transformation in SMEs and large enterprises – a case of COVID-19 disruption risk

PurposeDigital transformation in supply chains (SCs) has emerged as one of the most effective ways to minimize SC disruption risks. Given the unprecedented impact of the COVID-19 pandemic on global SCs, this study aims to identify and provide empirical evidence about the drivers of digital SC transformation, considering the interactivity between environmental dynamism, technology, and organizational capabilities during the pandemic era.Design/methodology/approachUsing partial least squares structural equation modeling (PLS-SEM), this study examines 923 firms in Vietnam to ascertain the drivers of digital SC transformation between small- and medium-sized enterprises (SMEs) and large enterprises, based on the technology–organization–environment (TOE) as an overarching framework.FindingsThis study finds that greater digital SC transformation adoption could be achieved under the interactivity between the TOE components of firms' technological competencies, learning capabilities, and disruptions in socioeconomic environments due to the COVID-19 pandemic. Moreover, a multigroup analysis shows that the drivers of digital SC transformation differ between SMEs and large enterprises. SMEs were found to be more motivated by the COVID-19 disruption risk when adopting digital SC models.Originality/valueThis study represents an original and novel contribution from Vietnam as an emerging market to the literature on the impact of COVID-19 on the global value chain. Apart from the unique dataset at the firm level, the analysis of interactions between external and internal drivers of digital SC transformation could provide crucial managerial implications for SMEs to survive major disruptions, such as those caused by the COVID-19 pandemic.

Optimizing Medical Healthcare Services Using Big Data Analytics

Big data is a very large size of datasets which come from many different sources and are in a wide variety of forms. Due to its enormous potential, big data has gained popularity in recent years. Big data enables us to investigate and reinvent numerous fields, including the healthcare industry, education, and others. Big data specifically in the healthcare sector comes from a variety of sources, including patient medical information, hospital records, findings from physical exams, and the outcomes of medical devices. Covid19 recently, one of the most neglected areas to concentrate on has come under scrutiny due to the pandemic: healthcare management. Patient duration of stay in a hospital is one crucial statistic to monitor and forecast if one wishes to increase the effectiveness of healthcare management in a hospital, even if there are many use cases for data science in healthcare management. At the time of admission, this metric aids hospitals in identifying patients who are at high Length of Stay namely LS risk (patients who will stay longer). Once identified, patients at high risk for LS can have their treatment plans improved to reduce LS and reduce the risk of infection in staff or visitors. Additionally, prior awareness of LS might help with planning logistics like room and bed allotment. The aim of the suggested system is to precisely anticipate the length of stay for each patient on an individual basis so that hospitals can use this knowledge for better functioning and resource allocation using data analytics. This would contribute to improving treatments and services. © 2023 IEEE.

Computational Intelligence Using Big Data for Fight Against Covid-19 Pandemic in Healthcare Environment

In world, COVID-19 disease spread over 214 countries and areas which efficiently affects every aspect of our daily lives. In various areas, motivated by recent applications and advances of big data and computational intelligence (CI), this research aims at increasing their significance in COVID-19 response like prevention of severe effects and outbreaks. To improve diagnosis efforts, assess risk factors from blood tests and deliver medical supplies, CI is used during COVID-19. To forecast future COVID-19 cases, CI is used. To check goodness as high accuracy prediction method, the proposed method is checked with real-world data which focus on CI and big data, method which are used in current pandemic. In upcoming days, to enact necessary protection plans, it is very difficult to detect as well as diagnose. For computational methods with help of big data, this research provides prediction and detection of COVID-19. For predicting and detecting cases of COVID-19, performances of proposed models are used as criteria. To improve detection accuracy of COVID-19 cases, proposed method increases combination of big data analytics and CI models with nature-inspired techniques. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies

Digital transformation, which significantly impacts our personal, social, and economic spheres of life, is regarded by many as the most significant development of recent decades. In an industrial context, based on a systematic literature review of 262 papers selected from the ProQuest database, using the methodology of David and Han, this paper discusses Industry 4.0 technologies as the key drivers and/or enablers of digital transformation for business practices, models, processes, and routines in the current digital age. After carrying out a systematic literature review considering key Industry 4.0 technologies, we discuss the individual and collective ways in which competitiveness in contemporary organizations and institutions is enhanced. Specifically, we discuss how these technologies contribute as antecedents, drivers, and enablers of environmental and social sustainability, corporate growth and diversification, reshoring, mass customization, B2B cooperation, supply chain integration, Lean Six Sigma, quality of governance, innovations, and knowledge related to dealing with challenges arising from global pandemics such as COVID-19. A few challenges related to the effective adoption and implementation of Industry 4.0 are also highlighted, along with some suggestions to overcome them.

Design Principles and a Software Reference Architecture for Big Data Question Answering Systems

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Adapting Regulations Post-Brexit

The UK government is taking advantage of the new regulatory flexibility, afforded by Brexit, to boost the country's competitiveness in pharma On 1 Jan. 2021, the United Kingdom formally left the European Union to become a third country and no longer a member of the Union's single market and customs union. The UK has, for example, decided to draw up its own version of the EU chemicals legislation-called REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals)-which sets technical standards for chemical ingredients for medicines (1). Because the UK is a separate legal entity-a third country-the UK's excipient producers and their raw material suppliers have started to be concerned about procedures like customs declarations and rules of origin. [...]by 18 Feb. 2021 the UK had vaccinated 26% of its population versus 8% in Denmark-the leading EU country for vaccines availability-6% in Germany, and 5% in France (6). NICE needs to change Industry believes that the National Institute for Health and Care Excellence (NICE), the government's health technology assessment (HTA) body, is being too restrictive with its evaluation of digitalization products, which ultimately sets the price paid by the government for them (9).

The relationship between big data capability, supply chain collaborative innovation and operational performance in Chinese manufacturing sector

Big data has now become a hot topic and focus of common concern in academia and practitioners. Data collection, storage, analysis, and processing require enterprises to improve their big data capability (BDC), which are increasingly valued by companies to create greater value. Supply chain collaborative innovation (SCCI) can enable companies to improve products and services. Many scholars have realized the positive effect of BDC on the improvement of enterprise performance, however, most of the extant literature focuses on direct effects of BDC on enterprise performance, and there is a lack of analysis of its impact transmission process. From the perspective of SCCI, and based on the theory of resource-based view (RBV) and theory of dual innovation (DI), this research analyzes the intermediary effect of SCCI as a complementary asset between BDC and enterprise operational performance (EOP), and reveals that BDC affect EOP by way of impact transmission mechanism. All the relationships between the three constructs (BDC, SCCI and EOP) are investigated by the empirical study. Subject to systematical review of existing literature from both China and overseas countries, and based on the theory of RBV, this paper categorizes BDC into three sub-capabilities: big data resource integration (BDRI), big data in-depth analysis (BDIDA) and big data application (BDA);based on the theory of dual innovation, SCCI is divided into two variables: breakthrough innovation (BI) and gradual innovation (GI). A theoretical framework is then developed and three research hypotheses are proposed accordingly: H1. Big data capabilities have a positive effect on supply chain collaborative innovation;H2. Supply chain collaborative innovation has a positive effect on enterprise operational performance;H3. Supply chain collaborative innovation plays a mediating role in the relationship between big data capabilities and enterprise operational performance. Empirical research methods using a large-scale questionnaire collection and analysis of primary data are employed to test whether the research hypothesis are accepted or rejected, and conclusions are drawn subsequently. The research employs questionnaire survey to collect data from Chinese manufacturers in the automotive and electronics sectors as these two sectors are ideal to investigate the impact transmission mechanism between big data capability and enterprise operational performance, as they have relatively good understanding and high level of informatization with good BDC. Sample enterprises are located in both coastal provinces and inland areas. The questionnaire was designed subject to both intensive and extensive literature review. The data collection started in early May 2020 and ended in November 2020. Due to Covid-19, most of the questionnaires were collected by emails or Questionnaire Star. A total of 330 responses were received and 15 responses were discarded due to data missing or reliability judgement, leaving 315 valid responses for further data analysis. Exploratory factor analysis was performed employing SPSS 25. 0. The KMO and Bartlett sphere test results were good, then the principal component analysis was used to extract the factors whose characteristic roots were greater than 1. The results showed that all the items of BDC were aggregated into 3 factors, and the cumulative variance explanation rate reached 76. 548%;all the items in SCCI were aggregated into 2 factors, and the cumulative variance explanation rate reached 83. 757 %;all items of the EOP were aggregated into one factor, and the variance explanation rate was 77. 530%, which is consistent with the dimensions of the scale designed in this study, indicating that the quality of the questionnaire data was good. This was followed by reliability test and validity test and all the test results were satisfying. Structural equation modelling (SEM) was used for the data analysis employing Amos 24. The test results show: χ2 / df =1. 823<3, RMSEA =0. 062<0. 08, CFI = 0. 953>0. 9, NFI = 0. 903>0. 9, TLI = 0. 947>0. 9. All ndicators are within the acceptable range, indicating that the model fits well with the sample data. The results of path analysis show that BDRI has a significant impact on both BI and GI;the BDIDA does not reach the significance level for the path of BI and GI;the BDA has a positive effect on BI and GI respectively;the BI and GI have a positive effect on EOP respectively. Following this, the mediating role of SCCI was tested by Bootstrap. The test results show that BI does not play a mediating role between BDC and EOP;however, GI does play a mediating role between BDC and EOP. The conclusions are drawn as follows by the empirical study. Firstly, BDC can positively promote SCCI;Secondly, SCCI can positively affect EOP;Thirdly, SCCI plays a mediating role between BDC and EOP. Theoretically, this research reveals that intermediary effect of SCCI as a complementary asset between BDC and EOP, which enriches literature by adding mechanism of other influencing factors on the path of BDC positively affecting EOP. Practically, this study has clarified a specific transmission path for BDC to improve EOP, i. e., enterprises should vigorously cultivate big data to improve their EOP, meanwhile focus more on the intermediary effect of SCCI © 2023, Journal of Industrial Engineering and Engineering Management.All Rights Reserved.

A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Research and Practice of 'Trinity' Construction of Emerging Engineering Big Data Course Teaching Quality Assurance System during the Epidemic

The pandemic of the COVID-19, has brought a great impact to the education and teaching, so the teaching can only be carried out online, in order to ensure the monitoring and management of teaching quality during teaching. This project import the latest information intelligent teaching system, multi-directional to ensure the monitoring and control of teaching quality, the deep integration of 'Rain Classroom' technology and teaching in this project, So that the interaction information between teachers and students can be recorded in real time during the online teaching, and organized, analyzed, stored also;Import the online teaching platform, collected the students records that finished homework during the epidemic;Import the online virtual experiment platform, online to finished the remote experimental operation of students. Thus, the trinity builds a three-dimensional teaching quality assurance system to escort high-quality big data course teaching during the epidemic. © 2023 IEEE.

The impact of digital supply chain capabilities on enterprise sustainable competitive performance: an ambidextrous view

PurposeThe construction of digital supply chains to integrate internal and external resources is becoming an important path for manufacturing enterprises to gain competitiveness. However, at present, research on the internal mechanisms of digital supply chain capabilities (DSCC) and enterprise sustainable competitive performance (ESCP) has not been sufficiently studied. Based on contextual ambidexterity theory, this study investigates whether DSCC could enable the realization of supply chain ambidexterity and further explains the mediating role of supply chain ambidexterity on DSCC and ESCP, and the boundary conditions of supply chain governance on supply chain ambidexterity and ESCP.Design/methodology/approachWith a survey data set of 232 Chinese manufacturing enterprises from different industries, the study empirically tests a moderated mediating model and conducts hierarchical linear modeling and bootstrap to test the study's hypotheses.FindingsThe results demonstrate that: (1) DSCC positively enhance ESCP;(2) supply chain ambidexterity, which can be regarded as a synergic ability of supply chain alignment and adaptability, partially mediates the positive relationship between DSCC and ESCP;and (3) supply chain governance such as incentive governance positively moderates the association between supply chain ambidexterity and ESCP, but there is no evidence that relational governance moderates their relationship.Originality/valueThis paper proposes a new interpretive perspective to understand digital supply chains. More importantly, it reveals the importance of DSCC in contributing toward supply chain ambidexterity and ESCP, and demonstrates the differential regulating action of incentive and relational governance on the association between supply chain ambidexterity and ESCP, with implications for both academics and practitioners.

Statistical Literacy—Misuse of Statistics and Its Consequences

Although statistical literacy has become a key competence in today's data-driven society, it is usually not a part of statistics education. To address this issue, we propose an innovative concept for a conference-like seminar on the topic of statistical literacy. This seminar draws attention to the relevance and importance of statistical literacy, and moreover, students are made aware of the process of science communication and are introduced to the peer review process for the assessment of scientific papers. In the summer term 2020, the seminar was conducted as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. In this article, we present the concept of the seminar and our experience with this concept in the summer term 2020.