Hengyun Li, Lingyan Zhang, Rui (Ami) Guo, Haipeng Ji and Bruce X.B. Yu
This study aims to investigate the promoting effects of the quantity and quality of online review user-generated photos (UGPs) on perceived review usefulness. The research further…
Abstract
Purpose
This study aims to investigate the promoting effects of the quantity and quality of online review user-generated photos (UGPs) on perceived review usefulness. The research further tests the hindering effect of human facial presence in review photos on review usefulness.
Design/methodology/approach
Based on review samples of restaurants in a tourist destination Las Vegas, this study used an integrated method combining a machine learning algorithm and econometric modeling.
Findings
Results indicate that the number of UGPs depicting a restaurant’s food, drink, menu and physical environment has positive impacts on perceived review usefulness. The quality of online review UGPs can also enhance perceived review usefulness, whereas facial presence in these UGPs hinders perceived review usefulness.
Practical implications
Findings suggest that practitioners can implement certain tactics to potentially improve consumers’ willingness to share more UGPs and UGPs with higher quality. Review websites could develop image-processing algorithms for identifying and presenting UGPs containing core attributes in prominent positions on the site.
Originality/value
To the best of the authors’ knowledge, this study is the first to present a comprehensive analytical framework investigating the enhancing or hindering roles of review photo quantity, photo quality and facial presence in online review UGPs on review usefulness. Using the heuristic-systematic model as a theoretical foundation, this study verifies the additivity effect and attenuation effect of UGPs’ visual elements on judgements of online review usefulness. Furthermore, it extends scalable image data analysis by adopting a deep transfer learning algorithm in hospitality and tourism.
Details
Keywords
Fujin Yi and Bruce McCarl
The purpose of this paper is to examine the grain production implications of alternative designs for China’s grain subsidy policy. In particular, the authors examine three subsidy…
Abstract
Purpose
The purpose of this paper is to examine the grain production implications of alternative designs for China’s grain subsidy policy. In particular, the authors examine three subsidy designs including area-based subsidy, quantity-based subsidy and production-cost-based subsidy.
Design/methodology/approach
To carry out the analysis, the authors develop a Chinese agricultural sector model (CASM) and an econometric, policy action–farmer response summary model. The CASM is used under a wide variety of subsidy level and basis experiments to generate pseudo data on farmer reactions to subsidies. Then a summary function model was estimated over those pseudo data that quantitatively summarized modeled farmer responses to different grain subsidy schemes. In turn, the summary functions were used to optimize the subsidy level such that it maximized grain production both within and across the area-based, quantity-based and cost-based subsidies. Regional implications were also developed.
Findings
The authors found that the production-quantity-based subsidy is the most cost-effective in stimulating grain production among the subsidy schemes. The authors also argue that scheme complies with WTO regulations regarding product-specific support. The authors found that the areas where grain production was most affected were the traditional grain-producing regions.
Originality/value
To the authors’ knowledge the authors have not seen a study of the Chinese grain subsidy program context that examined the effects of alternative subsidy schemes, nor one that developed estimates of the optimal subsidy level. In addition, the methodology is unique employing bottom-up, regionally disaggregated, sector modeling coupled with an aggregate pseudo data based summary function approach providing a new, original approach for analyzing agricultural policy design.