This paper focuses on the knowledge problem of economics by discussing its current status in light of digitalization. This problem highlights the paradox of not having the necessary knowledge to take an economic decision, but pretending to have it and act, hence questioning the legitimacy of governmental decision-making and its impacts on the economy. Current technological developments are challenging this problem. Big Data has been a neglected phenomenon when it comes to its impact on the nature of knowledge and the decision-making processes associated with it, and it is easy to think that Big Data solves this problem. This research gap is evaluated by re-visiting the knowledge problem and evaluating whether the knowledge problem can still be valid in the digital era. The digital governance issue has been largely covered by literature in terms of technical possibilities. However, the main challenge is not the technical one, but rather how to create governance structures to involve people in decision-making processes, and at the same not fall into the trap of the knowledge problem. The sustainable transition from digital government to digital governance is a transition from a technical structure to multiple processes on different levels, and these processes have their own limits.

This paper examines the time-frequency relationship between the number of confirmed COVID-19 cases, temperature, exchange rates and stock market return in the top-15 most affected countries by the COVID-19 pandemic. We employ Wavelet Coherence and Partial Wavelet Coherence on the daily data from 1st February, 2020 to 13th May, 2020. This study adds to the literature by implementing the Wavelet Coherence technique to explore the unexpected outbreak effects of the global pandemic on temperature, exchange rates and stock market returns. Our results reveal (i) there is evidence of cyclicality between temperature and COVID-19 cases, implying that average daily temperature has a significant impact on the spread of the COVID-19 disease in most of the countries; (ii) strong connectedness at low frequencies display that COVID-19 cases have a significant long-term impact on the exchange rate returns and stock markets returns of the most affected countries under study; (iii) after controlling for the effect of stock market returns and temperature, the co-movements between the confirmed COVID-19 cases and exchange rate returns becomes stronger; (iv) after controlling for the effect of exchange rate returns and temperature, the co-movements between the confirmed COVID-19 cases and stock market returns become stronger. Apart from theoretical contribution, this paper offers value to investors and policymakers as they attempt to combat the coronavirus risk and shape the economy and stock market behavior.

The World Economic Forum in Davos suggested that promoting entrepreneurship could be a solution to the problem of youth employment, which is a serious issue with 75 million young people out of work around the world. The entrepreneurial decisions of young people are especially important in regions where economic opportunities are restricted and people choose to move from that region. Two of such regions are East Germany and Northern Cyprus. In this article, the authors are interested in analyzing entrepreneurial tendencies of business and economics students from Northern Cyprus and East Germany in order to provide context-specific knowledge. For this purpose, a survey was conducted at the Technische Universität Dresden and the Near East University with n = 293 participants in 2016. The results were analyzed by using a logistic regression analysis. The study includes with policy implications on the transformation towards entrepreneurial universities to meet the different needs of small and poor cities.

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.

In this empirical research, the author focuses on the enterprise resource planning (ERP) software market. Based on a contingent perspective of how markets emerge and can be shaped, the author asks the research question of whether the emergence of the ERP market was a necessary, strong, or weak consequence of the product innovation of Systems, Applications & Products in Data Processing (SAP), which was the pioneer innovator in this specific market. This question is answered with a graph theoretical model of contingency and causality in order to measure the causality between events occurring over historical time. In this sense, the research article provides an application of the method proposed by Lehmann-Waffenschmidt. The author finds that the emergence of the ERP software market is contingent and was not predetermined; path dependencies play a big role in the way how this market segment emerged. With respect to both entrepreneurial and economic factors of relevance, the case of the SAP is far from a predetermined success story. The results are relevant for a number of reasons. First, the results indicate that instead of talking about success stories, a new perspective in market shaping can highlight a more realistic way of the contingent nature of entrepreneurial activity and product innovations. Second, the results aim to bridge the gap between marketing and the emergence of markets, as it was indicated as a research gap by recent contributions in marketing science. Third, the introduction of counterfactual events in the business history of SAP indicate a methodological innovation that has not yet been considered by marketing and entrepreneurship scholars, which may be helpful regarding recognizing patterns from the past and also regarding contingent planning for the future.