This investigation extends into the intricate fabric of customer-based corporate reputation within the banking industry, applying advanced analytics to decipher the nuances of customer perceptions. By integrating structural equation modeling, particularly through SmartPLS4, we thoroughly examine the interrelations of perceived quality, competence, likeability, and trust, and how they culminate in customer satisfaction and loyalty. Our comprehensive dataset is drawn from a varied demographic of banking consumers, ensuring a holistic view of the sector’s reputation dynamics. The research reveals the profound influence of these constructs on customer decision-making, with likeability emerging as a critical driver of satisfaction and allegiance to the bank. We also rigorously test our model’s internal consistency and convergent validity, establishing its reliability and robustness. While the direct involvement of Business Intelligence (BI) tools in the research design may not be overtly articulated, the analytical techniques and data-driven approach at the core of our methodology are synonymous with BI’s capabilities. The insights garnered from our analysis have direct implications for data-driven decision-making in banking. They inform strategies that could include enhancing service personalization, refining reputation management, and improving customer retention efforts. We acknowledge the need to more explicitly detail the role of BI within the research process. BI’s latent presence is inherent in the analytical processes employed to interpret complex data and generate actionable insights, which are crucial for crafting targeted marketing strategies. In summary, our research not only contributes to academic discourse on marketing and customer perception but also implicitly demonstrates the value that BI methodologies bring to understanding and influencing consumer behavior in the banking sector. It is this blend of analytics and marketing intelligence that equips banks with the strategic leverage necessary to thrive in today’s competitive financial landscape.

This paper analyzes the impact of wage subsidies on lower-skilled formal workers in the Democratic Republic of Congo (DRC). It employs a multi-sectoral, empirically-calibrated general equilibrium model to capture the economy-wide transactions between the formal and informal sectors and assess policy simulations in the DRC. The simulations, both in the short and long run, indicate that when the government provides wage subsidies to lower-skilled workers, it significantly improves the real disposable incomes of both formal and informal households. There is a general increase across formal and informal sectors in real household disposable incomes due to the wage subsidy. The results show that subsidy allocation narrows the income gap between high and low-income households, as well as between formal and informal sectors. The findings are insightful for wage policy simulations, as the wage subsidy targeting lower-skilled formal workers increases real GDP from the expenditure side by 1.19% and 3.19% in the short and long run, respectively, from the baseline economy.

Using the United Nations’ Online Services Indicator (OSI) as a benchmark, the study analyzes Jordan’s e-government performance trends from 2008 to 2022, revealing temporal variations and areas of discontent. The research incorporates diverse testing strategies, considering technological, organizational, and environmental factors, and aligns with global frameworks emphasizing usability, accessibility, and security. The proposed model unfolds in three stages: data collection, performing data operations, and target selection using the Generalized Linear Model (GLM). Leveraging web crawling techniques, the data collection process extracts structured information from the Jordanian e-government portal. Results demonstrate the model’s efficacy in assessing accessibility and predicting web crawler behavior, providing valuable insights for policymakers and officials. This model serves as a practical tool for the enhancement of e-government services, addressing citizen concerns and improving overall service quality in Jordan and beyond.

This paper aims to research the impact of psychological contract fulfilment on employee innovative work behaviour, and the mediating role of work engagement and the moderating role of social support. A quantitative analysis was adopted to address in research. Two-wave data were collected from 332 respondents working in China. Hierarchical regression analyses were conducted to assess the proposed hypotheses. Results revealed that psychological contract fulfilment positively impacted innovative work behaviour. In addition, engagement partially mediated the relationship between psychological contract fulfilment and innovative work behaviour. Furthermore, the findings suggest that social support moderates the relationship between work engagement and innovative work behaviour, and, in turn, moderates the indirect effect of psychological contract fulfilment on innovative work behaviour through work engagement. This research extends the generalizability of findings in the psychological contract literature. The results bear significant implications for the management of employees’ innovative work behaviour.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

Using data from 31 provinces, municipalities, and autonomous regions in mainland China from 2006 to 2019, we employ a double difference (DID) model and a spatial double difference (SDID) model to estimate the impact of the High-speed Railway (HSR) on the income gap between urban and rural residents, as well as its spatial spillover effects. Our research reveals several key findings. Firstly, the introduction of high-speed railways helps to narrow the income gap between urban and rural residents within local areas, but its spatial effects can lead to an increase in the income gap in neighboring provinces. Secondly, from a spatial perspective, intermediate variables such as industrial structure, education, science and technology, and foreign trade can also contribute to balancing the income gap between urban and rural residents, although the impact of population mobility is not significant. Thirdly, further analysis of the spatial effects demonstrates that education plays a significant role in balancing the income gap both within the local province and neighboring provinces. Additionally, adjustments in industrial structure, advancements in science and technology, and foreign trade have stronger spillover effects in reducing the income gap among neighboring provinces compared to their impact at a local level.