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Purpose -Rice consumption per capita in many Asian countries is decreasing constantly, but American and European citizens are eating more rice nowadays. A preference study among consumers was carried out with the aim of determining new rice product characteristics in order to support export of Thai rice. This paper aims to report the results Design/methodology/approach -The research was based on both secondary and primary data collection. The secondary data included exploratory surveys of rice and its products which were conducted in some of Thailand's potential rice export markets. Exploratory primary data were collected through qualitative focus group research. A quantitative questionnaire with 1,128 consumers of target nationalities was conducted to access consumer attitudes and preferences with respect to rice and rice products. Findings -Rice products were grouped with factor analysis and could be characterized by convenience (explained variance 33.9 per cent), grain variety (21.2 per cent), and tradition/naturalness (12.8 per cent). Rotated factor score plot of the preference for rice products among different nationalities showed a similarity in the preference for the tradition/natural products. Convenient products were preferred in higher income Asian countries and the non-rice eating countries. These three product categories were correlated with consumers' ideas concerning the health-supporting character of processed food. Originality/value -Consumers' rice preferences differed greatly among nationalities. Rice exporters have to understand these different preferences in order to offer the right products to their customers. Assuming consumer preferences to be comparable to one's own country's preference can cause new product failure. This paper confirms existing differences and presents details and backgrounds of these differences.

This research aimed at developing a high‐performing corrugated fiberboard box compression strength prediction model and to analyze the influences of ventilation and hand hole designs for these containers on the box compression test (BCT) by applying artificial neural network (ANN) modeling. The input variables considered in this study are composed of nine parameters including box dimension as well as shapes, sizes, positions, and locations of ventilations and hand holes of a regular slotted container (RSC, FEFCO 0201). Back propagation algorithms (BPNs) of ANN models were developed from 970 BCT testing data points (single wall boards, C flute, 205/112/205 g/m2). Tested data was randomly broken into three groups for the model development as 80:10:10 for the training set, testing set, and validating set. According to the analysis performed, a BPN 9‐13‐1 model reflected the highest prediction performance with R2 = 0.97. According to the analysis, BCT was significantly affected by the hand hole location followed by the geometrical dimensions of the box (height, length, and width) and the ventilation factors (shape, number, and location) in that order. Hand holes at the top flaps caused a lower BCT reduction compared with those at the vertical locations of the box. Slight changes to the eliminated board area for both hand holes and ventilation (±5%) contributed to less BCT reduction compared with its locations and shapes. Interestingly, increasing the box height significantly increased the BCT, and this was found to be limited only to shorter boxes fabricated from a high stiffness corrugated board.

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