The usage of Artificial Intelligence (AI) technique under retail industry will bring glorious outcome and flourishing benefits for both the retailers and the distinguished customers. The multiple platforms of AI usage in retail arena are discussed under two different cluster classified as online and offline, based on the terms of execution of retail activity. The present research was conducted with the objectives of evaluating the contribution of quality, customer relationship management and big data in designing futuristic retail model and analyzing the intention of retailers and shoppers in experiencing the emergence of AI. Disproportionate multistage judgement sampling method was em-ployed. A sample of 610 shoppers from four different capital regions of southern part of states in India was considered for the statistical analysis. Data was collected during the first quarter period of 2018. Descriptive research design was used to describe the intention of shoppers towards the emer-gence of AI in the Indian retail sector. The usages of AI technologies in online and offline retail are grouped separately and its effect on building the quality, customer relationship management and big data was evolved. Finally, its impact on the retailers intention and customers delight was studied through Structural Equation Modeling with testing of appropriate hypothesis.

Information System (IS) is a strife to exploit Business Intelligence (BI) in an organization. In the Malaysian Halal Food Manufacturing, a league of Information Technology (IT) professional and decision makers is the architect of the perspective in IT. There are numerous research studies on utilizing and investigating strategic effects of environmental factors augmentation on the organizations, but compact information is acknowledged prevailing how the subjective conception for the strategic source of deci-sions is transformed into the objective principle. Hence, general interpretation of the IT professional and the decision makers is crucial for a comprehensive and collaborative decision-making process. Therefore, prosper stimulate assimilation of the environmental factors that persuade the knowledge integration between IT professionals and decision makers is compulsory. BI and Big Data (BD) help organizations derive enhance decision-making process and knowledge creation. The objective of this research study is to emerge knowledge from organizing BD and to utilize BI together with perceiving MIT90s framework and environmental factors for the analysis of decision-making process of halal food manufacturers in Malaysia. The study applied regression analysis to predicted 103 responses to determine decision making of business performance. The results indicate that halal market demand played important role in predicting business performance of halal manufactures. This study provides some insights into decision making perspectives of business performance management among halal food manufacturers in Malaysia.

This article examines the nexus between Big data governance, dynamic capability and decision-making. Survey data was garnered from firms in Jordan and fuzzy sets (fsQCA) approach was applied. Fuzzy set configural modeling revealed two causal models for high decision-making effectiveness conditions. First, Big data governance, number of employees and firm age are necessary for higher levels of effective decision-making. Second, dynamic capability, number of employees and firm age are necessary for higher levels of effective decision-making. Insights for practitioners in an emerging economy and possible areas of future research are highlighted.

The main goal of this research is to study and investigate the impact of data management and analysis tools on improving organizational behavior within three IT service firms in Jordan. This research focuses on choosing three data management and analysis tools: Database analysis tool, data processing tool, and big data processing tool and how these tools could positively influence enhancing employee performance and organizational behavior. It accomplishes this within the distinct framework of three Jordanian IT service firms. The research supposed that combining big data analysis, integrated data processing, and database analysis is necessary to enhance organizational behavior and employee performance, as indicated by the findings. Furthermore, the research highlights the potential for IT service firms in Jordan to benefit from emerging database analysis technologies, promote the use of integrated data processing techniques, and leverage big data analysis for their own benefit. This can enhance corporate behavior and employee performance. Data was distributed and collected from three Jordanian IT service firms, and all collected data was analyzed using AMOS. The study's findings offer valuable recommendations for enhancing the operational efficiency of IT service firms in similar business environments, emphasizing the crucial importance of comprehending and purposefully employing database-related technologies for sustained prosperity.

The current study aims to determine the role of artificial intelligence (AI) in developing the accounting system (AS) in Jordanian Islamic banks. Currently, Islamic banks in Jordan are included in the research population. Using a quantitative research approach, 128 workers of Islamic banks in Jordan were chosen as a sample for this study. The study used a survey questionnaire instrument that was created based on past relevant literature and studies to collect the required data. The results indicated that there is an influence of AI (big data, intelligent agents, expert systems and automation processes) on the development of the AS in Jordanian Islamic banks. Accordingly, the study recommends that to improve AS tasks and reduce associated costs, accountants and accounting companies should always increase their understanding of artificial intelligence.

This study seeks to analyze the impact of digital marketing, social media, and digital transformation on the development of digital leadership abilities and the resulting enhancement of employee performance. The research examines the complex connections between digital elements and their influence on organizational dynamics, using a case study done at the Amman Stock Exchange as a basis for analysis. The inquiry explores the impact of digital marketing strategies on leadership skills, the use of social media platforms to promote digital leadership, and the transforming influence of digital initiatives on employee performance. In addition, the study evaluates the interaction of these characteristics and their combined impact on organizational success. Evidence suggests that digital methods have a complex and wide-ranging effect on leadership and performance, including factors such as adaptation, communication, and strategic decision-making. The study highlights the capacity of companies, specifically within the framework of the Amman Stock Exchange, to utilize digital tools for the purpose of developing leadership skills and improving performance. The findings obtained from this study provide important insights for enhancing digital ecosystems in comparable organizational contexts, highlighting the importance of a thorough comprehension and strategic integration of digital components in order to attain long-lasting success.

The aim of this study was to evaluate the impact of social media, big data, and data mining on the development of organizational behavior within the telecommunications industry in Jordan. The main objective of this study was to investigate the effects of technological components on the alteration of organizational behavior in the communications sector of Jordan. To accomplish this objective, a thorough empirical investigation was undertaken, encompassing the collecting of data from key stakeholders within the telecommunications sector in Jordan. A sample size of 412 participants, encompassing people from diverse roles within the communications sector, was chosen for the purpose of this study. The participants' replies and perspectives were gathered via the administration of surveys and conducting interviews, resulting in a comprehensive data set suitable for analysis. This study investigated the intricate relationship between the utilization of social media, the application of big data analytics, and the implementation of data mining techniques in influencing the dynamics of organizational behavior. The study's results underscored the substantial impact that social media platforms have on communication patterns and collaboration within telecommunication firms. Furthermore, the utilization of big data analysis has emerged as a significant catalyst for the enhancement of informed decision-making processes, exerting influence on diverse facets of organizational behavior, including strategic planning, employee engagement, and customer interactions. Data mining techniques have been identified as having a crucial function in extracting significant patterns and trends from extensive datasets, hence helping to the improvement of organizational learning and adaptation. The research findings indicated that the incorporation of social media, big data, and data mining technologies had a beneficial effect on the development of organizational behavior within the telecommunications industry in Jordan. The findings underscore the importance for enterprises to proactively utilize these technologies to cultivate a work environment that is characterized by increased agility, responsiveness, and collaboration. This study provides significant contributions to the subject of organizational behavior by examining the impact of social media, big data, and data mining within the specific context of the telecommunication sector in Jordan. The research sheds light on the transformative consequences of these technological advancements. The consequences of these findings have broad relevance for organizational leaders, politicians, and researchers, serving as a basis for further investigations in the dynamic realm of technology-driven organizational behavior.

This research aims to analyze the direct and indirect influence of the use of Big Data and Cloud Computing Platforms on the smooth processing of financial accounting system data and its implications for the success of village development. This research used quantitative methods, using Saturation sampling techniques, and obtained a sample of 131 respondents who were village financial information system operators, consisting of 131 villages in Pringsewu Regency, Lampung Province, Indonesia. The data collected from the surveys was then analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The research and data analysis demonstrate that Cloud Computing Platforms significantly enhance the efficiency of processing financial accounting system data. Additionally, Cloud Computing Platforms have a direct and positive influence on the success of village development. Moreover, the efficient handling of financial accounting system data directly and significantly impacts the progress of village development. Furthermore, the application of Big Data has a direct and substantial impact on the effective processing of data in financial accounting systems, as well as on the achievement of success in village development. Ultimately, the efficient data processing of the Financial Accounting System serves as a partial intermediary between the utilization of Big Data and Cloud Computing Platforms, and the achievement of village development in Pringsewu Regency, Lampung Province, Indonesia. Because the independent variable is able to significantly influence both directly and indirectly the dependent variable.

The space-time model combines spatial and temporal elements. One example is the Generalized Space-Time Autoregressive (GSTAR) Model, which improves the Space-Time Autoregressive (STAR) model. The GSTAR model assumes that each location has heterogeneity characteristics, and that the data is stationary. In this research, the moving average component is calculated by involving the relationship between variable values at a certain time and residual values at a previous time, and it is assumed that the data is not stationary, so the model used is the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Model. The model order for GSTARIMA is determined through the Space-Time Autocorrelation Function (STACF) and Space-Time Partial Autocorrelation Function (STPACF) to ensure accurate forecasting. Previous research only discussed the GSTARIMA(1,1,1) model, so in this research, the GSTARIMA(3,1,1) model will be addressed as a form of development of the GSTARIMA(1,1,1) model and applied to climate data. The climate data used in this research is sourced from NASA POWER and consists of rainfall variables with large data sizes, requiring the use of the data analytics lifecycle method to analyse Big Data. The lifecycle includes six phases: discovery, data preparation, model planning, model building, communicating results, and operationalization. Based on the data processing results with Python software, the GSTARIMA(3,1,1) model has a MAPE value of 9% for out-sample data and 11% for in-sample data. In contrast, the GSTARIMA(1,1,1) model has a MAPE value of 11% for out-sample data and 12% for in-sample data. So the GSTARIMA(3,1,1) model provides more accurate forecasting results. Therefore, selecting the correct model order is crucial for accurate forecasting.

AI is tanking different functions of businesses, and marketing is no exception. Digital marketing is gaining pace with the advancement in technology and the internet. The research aims to find the answer to the research question that marketers can benefit from AI in content creation for the digital market. The study also finds the relevance and use of AI in content creation and develops an AI infrastructure adoption model for content creators in digital marketing. The findings of this study were compiled using a systematic literature review that adhered to the Preferred Reporting Items for Systematic Reviews (PRISMA) statement and the criteria established by Meta-Analyses. The findings revealed that using AI in content creation provides personalized data, which helps the creators make relevant, targeted, and specific content. The research also finds that AI alone is not mature enough to carry out the whole content creation procedure as there is some limitation attached, especially regarding ethical implications. That’s why human surveillance of AI systems involved in content creation for the digital market is needed.