Search results

Given the growing emphasis on environmental consciousness and sustainability as core principles within most companies, product-service systems are recognized as strategic approaches to achieving sustainability objectives. Consequently, understanding consumer acceptance of these systems is of paramount importance. This study seeks to explore users' perspectives on the barriers that impede the adoption of product-service systems, intending to prioritize these obstacles.

This study utilizes a social media-based approach, specifically analyzing tweets related to Zipcar, an American car rental company that exemplifies a usage-oriented product-service system. The analysis identifies the factors influencing the acceptance of this system. The study utilizes topic modeling and sentiment analysis techniques to analyze the tweets. The opportunity value of each topic is determined, aiding in the identification of topics that require improvement. Furthermore, the interrelation between topics is explored, followed by correlation analysis to assess their significance.

Eight topics strongly related to the keywords are identified. Among them, “responsiveness”, “responsibility”, and “trust” hold the highest opportunity values. The findings emphasize the importance of service providers proactively addressing the obstacles that impede consumers' willingness to adopt product-service systems. Prioritization should be given to topics with higher opportunity values.

This research uncovers the primary obstacles to adopting the product-service system by directly considering consumer opinions and providing a prioritized list of these obstacles.

This research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield.

A three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data.

Rice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCI_min and VCI_min are found to be the most proper indices to predict rice yield.

The results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances.

This study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.