pt. 1. Ramanuja, Madhva, Chaitanya, Vedanta Desika, Meykandar, Asvaghosoa, Utpaladeva, Kumarila Bhatta, Udayanacharya.

Making transformative services such as healthcare accessible to low-income consumers is an ethical challenge of vital importance to marketers. However, most low-income consumers across the world are excluded from the market for such transformative services because of financial constraints arising from poverty. In this paper, instead of focusing on the micro-interplay between firms and consumers, we examine the macro-interplay among firms, consumers, and public policy in addressing the ethical challenge of market inclusion at the base of the pyramid. Specifically, we (...) examine how the Vietnam government used a policy of free and universal health insurance for children under the age of six as a means of lowering affordability barriers and fostering market inclusion in the healthcare market. Overnight in 2005, all children under the age of six living anywhere in Vietnam became eligible for free health insurance. Using this policy intervention as a natural experiment, we compare market inclusion outcomes of children under the age of six with older children who were ineligible before and after the program was implemented. We show that lowering affordability barriers through public policy increases access to target services, increases consumers’ overall out-of-pocket spending, and increases access to complementary services. By adopting a macromarketing lens, this study makes a strong case for collaboration among firms, governments, and communities in addressing the ethical challenge of system-wide market inclusion in base-of-the-pyramid markets. (shrink)

Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation algorithm has been proposed with novel grouping and load balancing strategies for (...) an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup. (shrink)