Building the ARIMA Model for Forecasting the Production of Vietnam’s Coffee Export

Coffee is a significant industry, accounting for 3% of Vietnam’s GDP, with annual export turnover consistently exceeding USD 3 billion. Despite global economic challenges affecting purchasing power at various times, Vietnam’s coffee exports in December 2023 continued to surge, reaching the highest level in the past 9 months at 190,000 tons, a 59.3% increase compared to November 2023, but still a slight 3.5% decrease from the same period last year. The export turnover reached USD 538 million, a 51% increase from November 2023 and a 26.4% increase from the same period last year. Therefore, forecasting the coffee export volume holds significant importance for coffee producers nationwide. This research employs the Box-Jenkins method to construct an ARIMA model for forecasting Vietnam’s coffee export volume based on annual data published by the General Statistics Office. Results indicate that among the models considered, the ARIMA(1, 1, 2) model is the most suitable. The study also provides short-term forecasts for Vietnam’s coffee export volume. However, the current model is limited to forecasting and is not yet optimized, as the assumed linearity in the model is a simplification.

Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction

Artificial intelligence(AI)applications are observed in different industries including the services,where the impressive changing pace is boosted by the technological advances in AI.The use of AI in the daily work of individuals and in shaping the relationship between firms and customers has resulted in increased efficiency and a higher degree of fascination in interaction.There are,however,customers'fears related to the use of AI.Therefore,understanding how AI affects customer trust is worth investigating.This study focuses on the AI applications and their impact on communication quality and customer trust.The features of AI applications investigated in this study include anthropomorphism,empathy response and interaction.A model was first empirically validated through Confirmatory Factor Analysis and tested using Covariance-Based Structural Equation Modelling.Data were then collected from a survey of 507 customers of two banks and two big telecommunication firms in Vietnam.Empirical results indicate that anthropomorphism and interaction do not play critical roles in generating customer trust in AI unless they create communication quality with customers.The ability of empathy response makes customers trust in various service situations.AI application seems to be a good solution for service firms to enhance their relationship with customers in difficult circumstances,including a pandemic crisis.This study delivers implications for enhancing AI adoption in the service firms that intend to innovate their future operations.

Exploring the Staging Patterns of MNC Entry to New Markets:The Resource Leverage Approach

This study aims at providing explanations for how MNCs(multinational companies)proceed with their manipulation of resources and competencies in the entry process to a foreign market.To an increasing extent,such processes perform as a staging process of resource leverage.This study identifies three stages,using the multiple case study incorporating grounded theory:the initial stage,the adaptive intensification stage,and the advantage persistence stage.In the initial stage,an MNC’s entry decision tends to be based on the resource replication mode of resource leverage;the resource exploitation mode of resource leverage is used mainly in the adaptive intensification stage,and the resource exploration mode is used in the advantage persistence stage.This study suggests that these resource leverage modes incorporating their microfoundations can be viewed as a set of potential measurements for examining MNCs'dynamic capabilities in the entry process to a new host market.

Does green growth in E-7 countries depend on economic policy uncertainty,institutional quality,and renewable energy?Evidence from quantile-based regression

Environmental degradation is one of the most debatable topics at international forums and it is considered a prime concern for the entire world.Therefore,researchers and policymakers have turned their attention from conventional economic growth to green growth.Although the existing literature has discussed several determinants of green growth,the impact of economic policy uncertainty(EPU),renewable energy consumption(RENE),and institutional quality(IQ)on green growth(GGDP)is relatively unexplored.Hence,this study is the earliest attempt to investigate the impact of EPU,IQ,and RENE on GGDP for emerging seven(E-7)countries from 1996 to 2019.In doing so,we apply panel quantile regression(PQR).The empirical findings delineate that EPU has a negative impact on GGDP,whereas IQ and RENE enhance the GGDP in E-7 countries.Based on the outcomes,this study suggests policy implications for achieving targets of the SDG 07,SDG 08,SDG 13,and SDG 16.The governments of these countries can achieve higher GGDP by ensuring political stability and reliable macroeconomic policies and through making such flexible policies that can easily control or address unpredictable future economic issues.