Business sustainability: Functions of financial behavior, technology, and knowledge

This study investigates the Millennials and Gen Z perception toward Fintech services, their usage intention, and their financial behavior. The study took place in the Palestinian context with a global comparison among these generations. The authors used the questionnaire-based technique to meet the study objective. West Bank respondents were selected for this purpose; the study instrument was distributed through different social media channels. The findings show that reliability/trust and ease of use are the main issues in using a financial service. Millennials are more aware (48%) of Fintech services than Gen Z (38%), which is different from the global view where Gen Z is the highest. The smartphone penetration rate is 100% among both generations, while the financial inclusion ratio in Palestine is around 36.4%; these clear indicators are the main Fintech drivers to promote Fintech services in Palestine, and these are global indicators for Fintech adoption intention. Both generations (84%) intend to use e-wallet services, Millennials (87%) and Gen Z is (70%) prefer using real-time services. Half of the respondents see that Fintech plays a complementary role with banks. The majority see that Fintech services are cheaper than bank services. Wealth management, and robot advisor services, and both generations are looking to acquire them in the long run. The authors revealed that 85% of respondents from both generations trust banks, so it is recommended that banks digitize their financial services to meet the customers’ needs, considering that 90% of respondents see that promotions are a key issue in adopting Fintech services. Promoting e-wallet services by banks is highly recommended due to the massive rivalry with Fintech parties.

Pakistan’s capital market and economy have significant features for examining the dynamics of the dividend policy. The agency conflicts between the management and the investors of the firms are main barriers to the success of the firm. The shareholder is generally taking away all the rights and similarly has a control on the decision concerning the dividend policy. The dividends are conveying better information than any other source regarding the firm’s prospects. The aim of this research is to identify and analyze the influence of shareholder preference and dividend signaling on the dividend policy of the corporations in Pakistan. The respective study presents the analysis of top financial management beliefs by taking eighty listed corporations on Pakistani stock exchanges during 2017–2018. Pearson correlation and multiple regressions are applied on responses to explore whether there is an influence regarding the shareholder preferences and the signaling mechanism on the dividend policy of the listed firms in Pakistan. Through statistical techniques the findings proved that shareholder preferences and dividend signaling have a positive and significant relationship with the dividend policy of listed corporations. Dividend policy is the response of investor preferences and signaling aspect of dividends.

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.