Purpose -The purpose of this paper is to develop a marketing strategy for a modern food/grocery market based on consumer preferences and behaviour. Design/methodology/approach -A total of 101 households having sufficient purchasing power were personally surveyed with a structured questionnaire. These households are spread across the well-developed Gomtinagar area of Lucknow city. Simple statistical analysis such as descriptive statistical analysis, frequency distribution, cross tabulation, analysis of variance, and factor analysis to assess the consumers' preferences for food and grocery products and market attributes were carried out. Findings -The preferences of the consumers clearly indicate their priority for cleanliness/freshness of food products followed by price, quality, variety, packaging, and non-seasonal availability. The consumers' preference of marketplace largely depends on the convenience in purchasing at the marketplace along with the availability of additional services, attraction for children, basic amenities and affordability. Results suggest that most of the food and grocery items are purchased in loose form from the nearby outlets. Fruits and vegetables are mostly purchased daily or twice a week due to their perishable nature, whereas grocery items are less frequently purchased. Research limitations/implications -This paper analyses the buying behaviour of the consumers under survey with respect to food and grocery items. These consumers are in a relatively advantageous position in terms of purchasing power and awareness of health and nutrition. Practical implications -The results may help the food processors and outlet owners to understand a diversified set of preferences for products and market attributes, so that they can make better decisions in the emerging organized food and grocery retail environment. Originality/value -The topic is relatively less researched in emerging markets especially where organized retail is still in its early stages.

The universality of design perception and response is tested using data collected from 10 countries: Argentina, Australia, China, Germany, Great Britain, India, The Netherlands, Russia, Singapore, and the United States. A Bayesian, finite-mixture, structural equation model is developed that identifies latent logo clusters while accounting for heterogeneity in evaluations. The concomitant variable approach allows cluster probabilities to be country specific. Rather than a priori defined clusters, our procedure provides a posteriori cross-national logo clusters based on consumer response similarity. Our model reduces the 10 countries to three cross-national clusters that respond differently to logo design dimensions: the West, Asia, and Russia. The dimensions underlying design are found to be similar across countries, suggesting that elaborateness, naturalness, and harmony are universal design dimensions. Responses (affect, shared meaning, subjective familiarity, and true and false recognition) to logo design dimensions (elaborateness, naturalness, and harmony) and elements (repetition, proportion, and parallelism) are also relatively consistent, although we find minor differences across clusters. Our results suggest that managers can implement a global logo strategy, but they also can optimize logos for specific countries if desired.design, logos, international marketing, standardization, adaptation, structural equation models, Gibbs sampling, concomitant variable, Bayesian, mixture models

ways. Such a huge potential and disparity in access to data can create considerable tensions among different sections of the society.Let us look at the business perspective here. There is no doubt now that organizations, especially larger corporations have started accumulating large amounts of data. However, the data is unstructured, voluminous and of high speed. For example, the corporations have access to multiple sources such as call centre logs, client chats, SMS texts, Instagram pictures, Click Stream on the web, social media such as Facebook, Blogs, CCTV, RFID, Barcode Scanner, Geographic Information Systems (GIS), Genomics, Youtube, Internet of Things (IoT), and the list is further expanding. As Moldoveanu 5 claims, the real problem with Big Data is not storage, analysis, etc., but, how do the organizations effectively and efficiently transform relevant data reliably into useful information? Perhaps, this is the culmination of a number of complex problems that the manager/client is more likely to face in the wake of Big Data deployment.Recently much good science, whether physical, biological, or social, has been forced to confront-and has often benefited from-the Big Data phenomenon. Big Data refers to the explosion in the quantity (and sometimes, quality) of available and potentially relevant data, largely the result of recent and unprecedented advancements in data recording and storage technology. 7 7 Diebold, F.X. (2012). On the origin(s) and development of the term 'Big Data'. PIER Working Paper 12-037.