An accurate demand forecast by artificial intelligence (AI) reshapes information asymmetry and strategic interactions in a decentralized supply chain, where the observability of real-time market fluctuation originates from the AI-driven demand forecast. This paper constructs an analytical model of the supply chain to examine the retailer's demand prediction strategy and the manufacturer's information sharing strategy. Research shows that the retailer prefers AI-driven demand prediction when the unit computing power cost is low, while achieving information sharing can lead to a win-win situation. Although the accurate demand forecast improves the manufacturer's payoff, higher cost allocation of AI adoption may also deter information sharing as the unit computing power cost increases. In contrast, if the retailer adopts traditional demand prediction, they can only reach an information-sharing agreement through a subsidy contract. Once voluntary sharing is reached, such an agreement can constantly be reached under AI-driven demand prediction but fails under traditional demand prediction due to the absence of the subsidy cannibalization effect. Furthermore, it is through the subsidy contract that the demand distortion can be prevented from harming the manufacturer's payoff when the retailer inflates forecasting demand. This study provides enterprise managers with a better understanding of tradeoffs between the costs and benefits of AI adoption and makes prescriptions regarding how information sharing can be achieved for supply chain coordination.

The research aimed to know the influence of long-term relationships, information sharing, Cooperation, and integration process in partial on the supply chain effectiveness of the EPC Steam Power Plant project in the Province of North Sulawesi. It was also to know whether enterprise risk management moderates the influence, long-term relationship, information sharing, Cooperation, and integration process toward supply chain effectiveness. The employees who became the sample in the supply chain activities of the steam power plant project in North Sulawesi were 250 people, 149 of whom were proportionally from the project owner. The research uses the data analysis technique using Structural Equation Modelling-Partial Least Square. The result of the study indicated that long-term relationships, information sharing, Cooperation, and integration process partially have a positive and significant influence on supply chain effectiveness. In addition, enterprise risk management proved to moderate the impact of information sharing. Still, it needed to moderate the effect of a long-term relationship, Cooperation, and integration process on the supply chain effectiveness of the EPC Steam Power Plant Sulut-3 project.

The study explores the combined influence of AI technology, supply chain collaboration, and information sharing on supply chain resilience. An integrated study model was developed and tested via smartPLS using a purposive sample of 542 respondents across different industries, to understand how information sharing mediates the collective impact of AI technology and collaborative practices on supply chain resilience. The experimental results demonstrate that AI technology paves the way for timely information and insights generation, which develops collaborative relationships among supply chain partners, facilitates trust and transparency, and thereby, information sharing to exchange pertinent data and insights across the supply chain network. The data were collected by surveying 542 managers from various industries and analyzed using SmartPLS. Relationships among technology adoption, supply chain collaboration, information sharing, and supply chain resilience were investigated. Structural equation modeling (SEM) was employed to observe the direct and mediating effects of information sharing between technology adoption, supply chain collaboration, and supply chain resilience. This study implies that practical information-sharing activities are essential for achieving supply chain resilience amid unpredictability and disturbances in solid market environments. It presents how technology adoption and supply chain collaboration are crucial to supply chain resilience. Information sharing, however, is shown to be an essential mediator, as clear communication and knowledge exchange amongst supply chain partners are fundamentally important in achieving supply chain resilience. Organizations need to invest in AI-driven technologies and develop collaborative ties with their supply chains to be resilient. This paper constitutes a valuable study of the primary drivers of supply chain resilience. It offers implications that organizations can use to persist and grow in the current ambiguous business setting.

Each company will make every effort to improve productivity, efficiency, fast, easy service, and continue to create various new innovations to stay ahead and survive in the market. In addition to productivity and efficiency that need to be improved, companies must also understand and know what consumers need. This study aims to determine and explain the effect of information sharing and innovation in Supply Chain Management partially on company performance. Data was collected by distributing questionnaires. The unit of research analysis is the tourism sector SMEs in Bogor, West Java, Indonesia. Respondents who were used as samples were 210 people. The analytical method used in testing the hypothesis is Structural Equation Modeling (SEM) using Partial Least Square (PLS). This research has succeeded in proving the existence of mediation of information sharing and significant innovation in the relationship between supply chain management and performance.

This study examined the relationship among blockchain technology utilization, information sharing, supply chain flexibility, and competitive advantage in UAE financial institutions. For this purpose, a theoretical framework was examined regarding the relationship among blockchain technology utilization, information sharing, supply chain flexibility, and competitive advantage. Additionally, mediating effect of supply chain flexibility was also examined. This research is cross-sectional by design. The respondents collected the data in the form of questionnaires. The response rate of this study was 63.03%. The data collected was examined by Smart PLS. The study's findings confirm that supply chain flexibility is affected by blockchain technology and information technology, which affects the financial institutes' competitive advantage. The implications of these findings cannot be neglected in the financial industry.

Information Technology (IT) plays an essential role on development of effective supply chain planning and it can improve the supply chain performance, either directly or indirectly. As a national industry, the National Iranian Oil Products Distribution Company involves a large number of organizations within its supply chain. Therefore, this descriptive-survey uses information sharing indicators, fuzzy TOPSIS technique based on managers and expert opinions to evaluate and to rank some oil products distribution companies. Data are analyzed and the results show that Oil Products Distribution Company of Chaharmahal and Bakhtiari received the highest rank and Farsan maintained the lowest rank compared with other regional companies.

This study aims to confirm the information sharing behavior using social media scale and to vali-date every item and make it reliable as an inventory by using Exploratory Factor Analysis (EFA). The researcher adapted the measuring instruments for every latent construct from the literature and customized the items to suit this particular study. The study sent the revised questionnaire to 262 respondents in order to gather the pilot study data and able to get 163 filled ones as final data. The set of questionnaires consists of 66 items that assess the 6 constructs. Data is analyzed using SPSS AMOS Version 21.0. The results show that every construct achieved its Bartletts’ Test of Spherici-ty 6.0 with the re-sult of Information Sharing Behavior .000 and .871; Intention .000 and .782; Belief Expectancy .000 and .911; Attitude Influence .000 and .925; Readiness For Change .000 and .959; and Self-Efficacy .000 and .902. The entire item of the construct has exceeded the minimum limit of 0.7 reli-ability of Alpha Cronbach value to achieve the Internal Reliability. The new integrate model has been proposed due to this finding.

This study develops an empirical investigation of trust antecedents and consequences in creating a collaborative business relationship between distribution companies and retailers in the cosmetics market. A conceptual framework based on trust antecedents as inputs and trust consequences as outputs is designed for both parties. In order to evaluate the performance and effectiveness of each considered trust factor for each party, a fuzzy data envelopment analysis (FDEA) based approach is proposed. In order to demonstrate the applicability of the proposed model, a real-life case study is considered. The required data are collected using interview and questionnaires, and the reliability of the collected data is examined using the Cronbach’s alpha. The obtained results indicate that there is no significant difference between both parties’ tendency towards building a collaborative business relationship based on trust. The results also indicate that information sharing is not an effective trust antecedent for both parties. The “product quality” and “product price” are the most effective trust antecedents for retailers, while the “retailer’s financial conflicts records” along with “length of partnership” are the most effective trust antecedents for distribution companies. Finally, the most effective trust consequences for distribution companies and retailers are “information sharing” and “brand advertising”, respectively.

This study investigates the factors that influence the sharing of information on social media platforms and examines the effects of perceived security, perceived privacy, and user awareness on users' trust in social media platforms, as well as the moderating effects of age, gender, educational attainment, and internet proficiency on information sharing. The study collected data from 837 social media users in Jordan and analyzed them using structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods. The findings of the study indicate that perceived security, perceived privacy, and user awareness all have a significant impact on users' trust in social media platforms. Trust, in turn, has a significant impact on the amount of information shared on these platforms. Also, the findings of this study provide valuable insights into the dynamics of information sharing on social networks. This knowledge will be of interest to managers, policymakers, and developers of social media platforms. In addition, the findings of the study also have implications for the privacy and security of social media users. For example, social media users can be more careful about the information they share on social media platforms, and they can take steps to protect their privacy.

The purpose of this study was to investigate how public sector workers in Indonesia use e-learning systems and how they can benefit from them. The researchers analyzed five variables that contribute to the effectiveness of e-learning: system reliability, information sharing, service quality, user satisfaction, and net benefit. Structural Equation Model analysis was used to analyze the data collected from 203 respondents who were public sector employees in Indonesia. The findings of this study revealed that information sharing, and service quality significantly impact user satisfaction, which in turn has a significant effect on net benefits. Additionally, system reliability was found to significantly impact user satisfaction. This theoretical implication suggests that there is a direct relationship between the level of information sharing and service quality provided by a public sector organization and the level of user satisfaction experienced by its usage of e-learning. The practical implication of the finding is that public sector organizations must prioritize the reliability of their e-learning systems. This includes investing in regular maintenance and updates, ensuring proper testing and quality control procedures, and addressing any issues or downtime quickly and effectively.