The global supply networks have been disrupted and weak connections exposed to an extent that few people have ever seen in their lifetime due to the COVID-19 epidemic. As a result of the severity of the crisis, every country and industry is feeling the effects, and the massive shifts in demand and supply that have happened throughout the epidemic are easily distinguishable from the effects of previous crises. We looked into the adaptability of alliance management and AI-driven supply chain analytics in the context of an ever-changing external environment. We examined four hypotheses in this area using survey data from the American auto components manufacturing industry. To do the analysis, we used Smart PLS. Alliance management capabilities, mediated by AI-powered supply chain analytics capacity, have been found to increase an organization's operational and financial performance. We also discovered, with environmental dynamics as a moderating factor, that alliance management capability has a substantial impact on AI-powered supply chain analytics capabilities. Based on our findings, we have a deep appreciation for the interplay between dynamic capacities and the relational view of organization. Finally, we pointed up the limitations of our study and offered a number of directions for future investigation that might help address some of the concerns that our results raise.

This paper examines how expert system may negatively or positively influence ethical based decision making process in an organization. Expert system ethical characteristics are chosen; including lack of human intelligence, lack of emotions, accidental bias and lack of values. Depending on quantitative approach; and through distributing a questionnaire on (132) GM, Deputy GM, project manager and officer, it appeared that expert systems' characteristics negatively influenced on the degree of ethics within the organizational setting. According to analysis, it appears that lack of values and human intelligence were among the characteristics that hinder the ethical stream adoption within an organization leading to ethics problems and mal-functioning on the decision making process. On the other hand, lack of emotions appeared to have good impact on the ethical efforts within an organization. Researchers recommended taking extra measures of surveillance in terms of ethics for individuals who are supposed to develop and monitor expert systems.

Chatbot program has evolved in the Indonesian market as the representative of online service customer that provides immediate response and it is able to interact with customers by using Natural Language Processing. This study aims to determine the extent of the customer satisfaction factors that successfully influence chatbot acceptance in Indonesia. A sample of 119 respondents is chosen using Exploratory Factor Analysis method. The findings deliver valuable insight of four factors in satisfying Indonesian customers when using Chatbot. The research conclusion not only gives a new perspective to identify important factors influencing customer satisfaction for chatbot acceptance in Indonesia but also helps Indonesian organizations look up at those fac-tors when planning to develop chatbot for their businesses.

This paper investigates the effect of artificial intelligence (product information, recommendation, and social media exposure) and payment flexibility on the operational performance of ecommerce retailers. The study is based on Transactional Cost Analysis, Material Flow, and Technology Integration theories. It considered a sample size of 270 members out of the population of 769 employees from five ecommerce companies operating in the region (Namshi.com, Noon, Joly Chic, Extra, and Styli). The analysis involved constructing a structural equation model to examine the trickle-down effect of the variables included in the study. The study concluded that artificial intelligence and payment flexibility are the core reasons that the retailers in the region are registering operational success in the retail market.

The moderating role of influencer endorsement and perceived monetary benefits on the relationship between perceived risk and financial artificial intelligence services was explored in this study. Data were obtained through questionnaires distributed to 200 respondents who were selected using a purposive sampling method. The respondents were customers receiving financial artificial intelligence services in Jordan. Analysis was performed using a structural equation modeling approach run by Smart-partial least squares (PLS) 3.2.9 involving data from 138 returned questionnaires. The results show a negative impact of perceived risk on financial artificial intelligence services, and a moderation effect of influencer endorsement and perceived monetary benefits on the relationship between perceived risk and financial artificial intelligence services. The findings can assist companies in their strategies of decreasing perceived risks that individuals could be encouraged to utilize business intelligence applications, for instance, financial technology services.

Currently, artificial intelligence and marketing strategies have become significant factors for improving the business capabilities that lead to improved business performance. Thus, the current study investigates the impacts of artificial intelligence and marketing strategies on the organizational performance of the tourism industry in the UAE. The present study also examines the mediating role of organizational capabilities in linking artificial intelligence, marketing strategies, and organizational performance of the tourism industry in the UAE. The analysis of the moderating impact of organizational behavior on the links between artificial intelligence, marketing strategies, and organizational performance are also part of the current study’s goals. This study used questionnaires to collect primary data from the respondents and analyzed it using Smart-PLS. The results indicated that artificial intelligence and marketing strategies have a positive association with the organizational performance of the tourism industry in the UAE. The outcomes also revealed that organizational capabilities positively mediate the links between artificial intelligence, marketing strategies, and organizational performance. The findings also demonstrated that organizational behavior significantly moderates the links between artificial intelligence, marketing strategies, and organizational performance of the tourism industry in the UAE. This study is relevant for regulators in supporting policy development related to artificial intelligence and marketing strategies for high organizational performance.

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

The arrival of the era of robots and autonomous machines is undisputable. It is anticipated that the future business environment will be characterized by a variety of intelligent systems and autonomous robots. In 2017, the International Federation of Robotics reported that momentum gained by robotic technologies is strong and that the sales volumes of both service and industrial robots is expected to grow. Building on this projection, the present study proposes a set of prerequisites or key determinants for the adoption of autonomous robots in the supply chain and logistics industry: technological context (i.e., relative advantage, complexity, and cost), organizational context (i.e., management support, financial support and employee competence) and environmental context (i.e., competitive pressure, customer pressure and vendor support). The study adapts a quantitative research design and uses an online survey to collect the needed data to test the conceptual framework and hypotheses proposed. Part of the study results confirms the association between the cost of digital technologies and the adoption of autonomous robots. However, the study found no evidence that the perceived relative advantage positively impacts supply chain and logistics firms’ adoption of autonomous robots. The study offers some managerial advices to supply chain mangers and marketers of the digital technologies and tools that can be applied in the supply chain setting.

Artificial Intelligence (AI) techs help businesses turn internal and external data into real gold by analyzing customer data, extracting valuable insights, and offering individual solutions. The current study aimed to identify the mediating effect of artificial intelligence and digital experience on the relationship between neuromarketing (emotional appeal and memory encoding) and consumer behavior. The current study was applied to consumers within the MENA region by using an online questionnaire self-administered by (837) individuals. The study hypotheses were all accepted, and results confirmed that there is a mediating effect of AI and digital experience (DX) on the relationship between neuromarketing (emotional appeal and memory encoding) and consumer behavior. The degree to which digital experiences can modulate the association between emotional appeal, memory encoding, and consumer behavior, is not absolute; other factors and external cues that may be integral parts of a seller's marketing campaign remain relevant as well. The marketing strategy, quality of the product, prices, and effect of the enclosing environment influence consumer behavior from the digital dominion. Consequently, the comprehensive study of the digital situation is the premise of comprehending as well as using the mediating influence of digital experience on a consumer's mind. The study supports the idea that it is crucial for digital experience to bring up good feelings and provide opportunities for better information memory. Building visualizations that are appealing, interactive, and immersive to the users is intended to keep them interested and promote remembering. Through this approach, information on the brand can be associated with, recalled, and can become a significant decisive factor in future purchase decision-making.

Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.