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This study examines the impact of perceived value, materialism and tourist intention on willingness to pay a premium for ecotourism (WTPP).

Data were collected and conducted in Vietnamese National Parks. The dataset consists of 927 valid responses by domestic travelers. Structural equation modeling (SEM) was used to test the proposed relationships among perceived value, materialism, tourist intention and tourists' WTPP.

This study found a significant positive effect of perceived value on both tourist intention and their WTPP. The results also indicated that materialism is revealed to have a totally negative influence on WTPP for ecotourism; its indirect effect is transmitted through tourist intention. These findings highlight that tourists are mainly attracted when they received unique information about eco-destination. Therefore, they tend to participate in ecotourism and decide to pay for ecotourism.

First, the study findings contribute strategies for tourism providers in their marketing activities by segmenting the target market which can be based on tourists with materialistic and nonmaterialistic. Second, ecotourism marketers need to focus on increasing the unique image of ecological sites. These efforts will create a favorable action for tourists. Third, ecotourism providers might increase the prospective customer base among consumers who have materialistic behaviors. Furthermore, they can provide ecotourism promotion materials for targeting customers. Finally, government needs to consider strategies designed to enhance the eco-destination places and environmental intention.

First, the study has not compared the efficacy of two different measures which are affective and cognitive items in perceived value construct. Second, this research does not discuss about the interrelationships among other factors influencing tourists' WTPP. Third, the customers' demographic characteristics in this study did not explore because the difference on gender may be perceived differently in analyzing advertising. Fourth, this study was conducted in Vietnam, not in different countries and other different cultures.

This study investigates the impact of perceived value and materialism on ecotourism intention and tourists' WTPP for ecotourism. This study reveals the understanding of how individuals' concern about the value of ecological sites and influences their intention to actual behavior in engaging ecotourism. Significant practical implications are also provided for ecotourism providers, marketers and government. In general, government needs to consider strategies designed to enhance the eco-destination places and environmental intention. Ecotourism marketers need to focus on increasing the unique image of ecological sites. These efforts will create a favorable action for tourists. Otherwise, ecotourism providers might increase the prospective customer base among consumers who have materialistic behaviors. Furthermore, they can provide ecotourism promotion materials for targeting customers.

The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.

The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.

Using the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).

The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

The purpose of this research is to advance the conceptual and practitioner understanding concerning the maximisation of Industry 4.0 technologies industries in an emerging economy. The study first examines the internal resources that companies possess to implement Industry 4.0 effectively and, second, identifies the critical gaps that necessitate external resources, both at the industry and government levels.

The study embraces qualitative and inductive approaches with semi-structured interviews conducted with 112 company leaders representing nine industries and operating in various cities/regions of Vietnam.

The data analysis helped unveil 16 critical dimensions. Notably, the organisation-developed competences dimension illuminates the understanding regarding firms’ available internal resources, while the financial management and technological leap ability dimensions help explain firms’ required resources. More broadly, the business community unity and business community learning dimensions ascertain the significance of industry-level support, while the technological sponsorship and legal framework guide dimensions underline government support.

First, the study unpacks various key aspects of their daily, mid- and long-term operations associated with their current internal resources, gaps identified and the support they require to progress within the Industry 4.0 environment. Second, the study proposes a framework that advances the extant conceptual understanding of operational, strategic, managerial and production aspects among firms operating in an emerging economy. Third, it focuses on companies operating in nine industries in an emerging economy. Fourth, the study contributes to addressing various extant research gaps.

The purpose of this study is to examine the implementation of Industry 4.0 (I4.0) through the lens of the dynamic capabilities framework. Contrary to most existing research, this study chooses a cross-national viewpoint, exploring companies operating in two emerging economies.

Semi-structured interviews were conducted with 80 company managers operating in eight industries in Vietnam and Bolivia. The chosen inductive analysis was supported by qualitative content analysis and data structure.

The analysis reveals 13 conceptual dimensions. For instance, sensing opportunities underlines tangible and intangible “direct prospects”, such as enhanced accuracy, speed and cost effectiveness, whereas “operational management pressures” (sensing threats) identify the dilemma of changing individuals’ mindset, recruitment and addressing financial needs. While there is an overall agreement in key dimensions, differences between managers from both countries also arise, including staff’s adaptation and constant upskilling.

Empirically, this study responds to calls for cross-national studies investigating I4.0 initiatives. In doing so, the data gathered from company managers engaged in business in emerging economies afford new perspectives, with practitioner value. Theoretically, the numerous dimensions emerging from the data analysis provide useful conceptual insights to understand managerial aspects in considering and adapting to I4.0 expectations and requirements. These insights are reinforced by the development of a conceptual model that illuminates the initiatives, efforts and challenges of embracing this phenomenon.