Artificial intelligence and marketing innovation: The mediating role of organizational culture

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.

The essence of this research is to shed light on use and importance of artificial intelligence (AI) in commercial activity. As such, the objective of the present study is to understand the impact of AI tools on the development of business functions and if they can be affirmed as a means of help or as a substitute for these functions. In-depth interviews were conducted with 15 commercial managers from technological SMEs. The results indicate that all the participants use AI systems frequently, that these tools assist in developing of their functions, allowing having more time and better preparing to solve the commercial problems. The findings also indicate that the tools used by commercials are still somewhat limited, and companies should focus on their training and development in AI, as well as the training of their commercials. Furthermore, the results show that firms intend to use the data collection and the analytical tool that enable real-time response and customization according to customer needs.

Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required production companies to adapt production and business models according to the needs. The demand for real-time decision support systems adapted to these raising business needs is continuously growing. Nevertheless, businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to 96% when combining economic and demand information, optimal forecasting period from 10 months to five years, more frequent use of soft indicators in forecasting, the relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and demand-oriented, (III) industry-specific, and (IV) media information indicators, were separated. The analysis forms a suggestion for decision-makers to pay more attention to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling.

Acknowledgment
This work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.