Abstract Statista is a business data platform that gathers existing data from over 22,500 sources and conducts its own research to prepare analyses across industries and geographies.
Small businesses do not share the same financial management problems with large businesses. This paper shows that the source of the differences could be traced to several characteristics unique to small businesses. This uniqueness in turn creates a whole new set of financial management issues. The major implication is that, yes, there are new and interesting topics in small business financial management research.
This research examines the istijrar contract as a potential solution to the operational and Shariah compliance challenges commonly encountered in murabahah-based financing, particularly in scenarios involving continuous and repeated purchases. Such flexible purchasing arrangements often generate complexities and elevate compliance risks. To address these issues, the study adopts a structured literature review methodology, implemented in three stages. First, it reviews scholarly debates concerning the Shariah compliance of the istijrar contract. Second, it assesses existing global practices. Third, it analyzes Turkish practices and legal regulations, critically examining the discussions and models at each stage to identify the most suitable adaptation for implementation in Türkiye. Key findings highlight the absence of standardized frameworks in both global and Turkish contexts, along with the limited practical application of istijrar in Türkiye’s Islamic banking sector. In response, this research synthesizes these insights to develop a structured and independent istijrar model specifically tailored to the Turkish Islamic banking environment. It is anticipated that this model will enable Islamic banks to more effectively finance customers engaged in production and trade activities, thereby supporting the growth of the real economy.
Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg
et al.
By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.
Marketing. Distribution of products, Management. Industrial management
Bilal Ahmed Memon, Faheem Aslam, Shakhnoza Asadova
et al.
The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.
В статье раскрывается тема создания музыки в союзе
композитора и балетмейстера. Рассматривается продуктивное
творческое воздействие результата такой работы на казахскую
мелодичность, традиционную этнокультуру. Автор изучает
характер совместной работы казахстанских и зарубежных деятелей
искусств над сочинением национальных балетов, результаты
творческих объединенных проектов области танцевального и
музыкального искусства Востока и Запада.
The performing arts. Show business, Dramatic representation. The theater
Paulo Duarte, Cristina Estevão, Ana María Campón-Cerro
et al.
The hospitality and travel sector has been one of the most affected sectors by Covid-19, which has resulted in a significant increase in the literature addressing the impact of the health crisis on tourism activities and tourists’ perceptions and behaviours. Traditionally, socio-demographic variables have been instrumental in understanding consumers’ needs and desires. However, during the pandemic, it has been unveiled that social and economic profiles have started to influence how tourists make decisions. Since studies on the changes in hotel choice during and after Covid-19 are still scarce, this article aims to assess the influence of socio-demographic variables on hotel choice based on data collected during the peak phase of the Covid-19 pandemic. A quantitative study was conducted using an online questionnaire that reached an international sample of 1113 individuals. The ANOVA and the t-test analysis results point out that socio-demographic variables under study are responsible for several differences in the evaluation of hotels. These findings reinforce socio-demographic attributes’ capability to understand customers’ preferences and decision-making despite the context.