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The oil and gas industry, as one of the most complex industries with unique characteristics, plays a vital role in the world's energy supply. In order to be present in this field, gain and maintain a competitive advantage, the actors of this industry are condemned to create, develop or acquire innovative technologies. With the change in the approach of companies active in the oil and gas industry compared to the past, intellectual achievements have been considered as a valuable asset and their support has become inevitable. Companies active in the field of oil and gas are well aware of the fact that obtaining maximum profits requires managing and supporting intellectual achievements and defining an intellectual property strategy aligned with the company's business goals. Therefore, each company defines its intellectual property strategy according to its role in the energy supply chain and operates based on it. Investigating the cost and profit of intellectual assets are two important criteria in determining the appropriate strategy of an oil company.

In this research, due to the importance of investing and especially investing in the stock market, we predicted the stock price return on the stock exchange through the Auto-Regressive Integrated Moving Average (ARIMA) and Recursive Neural Network (RNN) of long-short term memory (LSTM). Then, to reduce the risk of decision-making, we compared the predictive power of these two models to determine a better model. The research variable is the stock price of the top 20 (in market cap) companies on the stock exchange for the period of the 11th Feb 2015 to 22th Jan 2022. We considered the data of the last 10 days as experimental data and the previous data as educational data. Initially, we calculated the mean and standard deviation of the prediction error of both models; these criteria had less value for the LSTM recursive neural network model than the ARIMA model. To measure the significance of this difference in predictive power, we used Harvey, Liborne, and New Bold tests. The results showed that in predicting the stock prices of the top 20 companies of the stock exchange, the predictive power of the LSTM recursive neural network model was statistically and significantly higher than the ARIMA model which means better predition of stock prices and higher return for investors. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing.

EXTENDED ABSTRACT INTRODUCTION The number of revealed corruption-related crimes is one of the major challenges of Iran which has been significantly increased in recent years. According to official reports, although only two cases of embezzlement were reported in 1990s and 2000s with a total value up to $ 800 million, administrative corruption has significantly increased in 2010s with 13 large corruption cases and a total value up to $ 14 billion and a growth of over 1500% compared to 1990s and 2000s (The Iranian Students News Agency[1] (ISNA), 2017). The spread of such amount of corruption in administrative and bureaucratic system of the country can have irreparable economic and social consequences. The statistic investigations have shown that macroeconomic indicators including economic growth, employment, unemployment, poverty, inflation and income distribution have become less favorable in 2010 compared to previous decades (Statistical Center of Iran, Iran Statistical Yearbook, 2017). In this regard, the following questions can be raised: is there a relationship between corruption and the indicators of income distribution and poverty in the country? Since the volume of international and unilateral sanctions on Iran has increased in the 2010s, does such a widespread growth in the volume and value of corruption cases in Iran have a relationship with sanctions?   METHODOLOGY Therefore, the purpose of the present study is to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during 1984 to 2020. For this purpose, the indices of per capita income, poverty line, administrative corruption and control of corruption, Atkinson and Gini have been utilized to investigate their interactions through Toda-Yamamoto Causality Test.   FINDINGS Findings of research show that:   sanctions have affected the causality of per capita income on administrative corruption. sanctions have affected the causality of corruption control on per capita income. sanctions have affected the causality of income distribution on per capita income. sanctions had no effect on causality of per capita income, on Atkinson Index and vice versa. sanctions had no effect on causality of per between poverty line, on administrative corruption and vice versa. sanctions had an effect on causality of per between poverty line, on corruption control and vice versa. sanctions had a significant positive effect on poverty line, but had no significant effect on GINI index. In other words, sanctions have affected the causality of income distribution on poverty line. sanctions had no effect on causality of poverty line, on Atkinson Index and vice versa. sanctions had no effect on causality of GINI index, on administrative corruption and vice versa. sanctions had no effect on causality of GINI index, on corruption control and vice versa. sanctions had a significant positive effect on administrative corruption, but had no significant effect on Atkinson index. In other words, sanctions have affected the causality of income distribution on administrative corruption. sanctions had a significant positive effect on Atkinson index, but had no significant effect on corruption control. In other words, sanctions have affected the causality of corruption control on income distribution.     CONCLUSION The purpose of the present study was to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during1984 to 2020. For this purpose, the indices of per capita income p, poverty line, Atkinson, GINI, administrative corruption and corruption control were investigated. In general, the following results were obtained from the present study:   Income distribution is not an effective variable for poverty in Iran. Corruption is an effective variable for causality of poverty in Iran and its significance level is higher under sanctions condition. Corruption and poverty cannot properly explain the income distribution in Iran. However, the corruption control can be the cause of income distribution and poverty line is a proper representative for the cause of income distribution under sanctions conditions. Income distribution is a strong variable for causality of corruption in Iran. Poverty can properly explain the causality of corruption in Iran under sanctions condition, but is not the cause of corruption under normal condition.   According to the obtained results, it seems that sanctions condition is an effective variable for the relationship between variables of income distribution, corruption and poverty. However, the effective factors of income distribution need further investigations in future.  

ChatGPT is an AI model designed for conversational purposes. This field has gained significant popularity in digital marketing, providing organizations with a vital tool for interacting with customers and enhancing their marketing efforts. The primary objective of this research is to examine the impact of ChatGPT on customer experience in digital marketing. Additionally,this study aims to investigate the moderating effects of business type, familiarity and comfort with technology on customer experience. Furthermore, the research explores the moderating roles of gender, age, and education level. Data for this study was collected electronically from 394 customers who interacted with ChatGPT in digital marketing using an open-access questionnaire. The results confirm the importance of the moderating role of familiarity and comfort with technology, business type, age, and education level in the relationship between customer experience with ChatGPT andOverall Satisfaction (OS)with digital marketing, while gender has no significant impact. The findings of this paper contribute to the existing literature on the use of conversational AI models in digital marketing and customer experience, providing insights and recommendations for future research

In the multifaceted landscape of entrepreneurship, this research delves into the intricate relationship between entrepreneurial orientation and entrepreneurial intention, focusing specifically on the final-year female undergraduates in Nigerian federal universities. Grounded in the Human Capital Theory (HCT), this study uncovers profound insights into the multifaceted relationships between various entrepreneurial traits and their influence on entrepreneurial intentions. The findings reveal a nuanced relationship between traits such as autonomy, competitive aggressiveness, risk-taking, self-esteem, and the sense of inclusion with entrepreneurial intentions. Autonomy and competitive aggressiveness emerge as significant influencers, while risk-taking displays a non-significant association. Self-esteem holds profound implications, while the sense of inclusion exerts a more subdued impact on entrepreneurial intentions. Theoretical implications challenge and expand existing frameworks, notably HCT. While HCT posits that investments in education and training foster entrepreneurial traits, this study suggests a more intricate relationship. The non-significant association of certain traits, despite their emphasis in HCT, underscores the need for a nuanced understanding, possibly integrating other theoretical frameworks. From a practical standpoint, these findings guide policymakers and educators in designing targeted interventions. Specialized training programs, workshops, and courses emphasizing autonomy, competitive aggressiveness, and self-esteem can be developed. Integrating competitive elements, like business plan competitions, can nurture students' competitive drive. Policy recommendations include tailored entrepreneurial education, fostering inclusive environments, promoting a risk-taking culture, supporting longitudinal studies, and enhancing collaboration between educational institutions and industry.