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This paper aims to identify distinct consumption patterns among Asian consumers and examine how these relate to cultural antecedents and key human values.

This study uses a large, representative sample of almost 7,000 Asian consumers in 10 culturally varying markets, using latent profile analysis (LPA) to identify the consumption profiles.

The findings empirically demonstrate that the two profiles are “inner-directed nationalistic frugals” (IDNF) and “outer-directed self-eekers” (ODSS). IDNF consumers spend more time and money on education and prefer ethnocentric consumption. ODSS consumers emphasize individuality, self-expression, seeking novelty and impressing others. Consumers with more collective values in Schwartz's typology tend to demonstrate the IDNF pattern; those with more individualistic values demonstrate the ODSS pattern. The distribution of IDNF and ODSS profiles is influenced by demographics, religion and geographical region: IDNF is greater than ODSS in Southeast Asia; ODSS is greater than IDNF in East Asia; IDNF is roughly equal to ODSS in Northeast Asia. IDNF tends to be found among older and more religious consumers, while ODSS is the opposite. Importantly, in the more religious Southeast Asian countries, even younger consumers are more IDNF than ODSS.

This research uses an exploratory and discovery-oriented approach; future research can use more confirmatory approaches to systematically examine the relationship between cultural dimensions (e.g. individualism-collectivism) and consumption patterns.

For their brands to grow in Asian markets, marketing practitioners are advised to use multiple brands to segment Asian consumers based on their values, demographics, geographical location and what religious/faith traditions they follow.

This is the first paper to identify consumption profiles in Asian markets using LPA without prior conceptual biases and relate them to cultural values and demographic variables.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.