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Marketers try to influence food shopping behaviour of children through various in-store food promotional strategies (FPS). These in-store FPS comprise of attractive packaging, accessibility, availability of foods in wide varieties and presence of helpful and friendly sales personnel. However, little is known about how children buy and the extent to which these marketing strategies are successful. Hence, the purpose of this paper is to investigate food shopping behaviour of children in retail stores and to study the effectiveness of in-store FPS and demographic factors (age and gender of child and monthly family income) on food shopping behaviour of children.

Data have been collected from 473 mothers of children in age category four to 11 years. Data have been analysed through descriptive statistics (means and standard deviations), bivariate correlations, exploratory factor analysis, confirmatory factor analysis and hierarchical regression analysis.

The study explores and validates four factor structure of food shopping behaviour of children in retail stores in Indian settings. These factors include children’s active assessment of foods, impact of availability and variety on children’s purchases, influence of TV food ads on children’s purchases and influence of packaging on children’s food purchases. Further, the findings also reveal that in-store FPS are truly effective in influencing food shopping behaviour of children.

Marketers may highlight packaging attributes in food advertisements as they can help escalate food purchase requests of children in retail stores. In addition to this, food advertising is strongly associated with assessment of foods in retail stores and looking for availability of advertised foods in retail stores. This suggests that food advertising as a medium of communication should not be ignored. However, sales personnel can also be used more effectively as they are seen to help children in identifying availability and variety of foods in retail stores.

As no such study has been conducted so far (to the best of researcher’s knowledge), this study potentially helps in bridging gaps in literature.

The current study aims to predict consumer complaint status (complainers or non-complainers) based on socio-demographic and psychographic factors and further to discern the differences in behavior disposition of consumer groups concerning determinants of consumer's tendency to exit (TE).

The research used survey-based data of 600 Indian consumers of three service sectors (hotel and hospitality, automobile service centers and organized retail stores). Chi-square automatic interaction detector (CHAID) decision tree analysis was used to profile consumers.

The results indicated that occupation; income; education; industry and attitude toward complaining were significant factors in profiling consumers as complainers or non-complainers. Further, determinants of TE (discouraging subjective norms, perceived likelihood of successful complaint, lower perceived switching cost, poor employee response, negative past experience and ease of complaint process) vary significantly across the groups of complainers and non-complainers.

The research questions in this study were tested with three service sectors consumers in India, so due care should be exercised in generalizing these findings to other sectors and countries. Study replication across other service sectors and countries is recommended to improve the generalizability of these findings with wider socio-demographic samples.

Firms striving for consumer retention and aim to extend their consumer life cycle can greatly benefit from the results of this study to understand the customer complaint behavior (CCB) specific to non-complaining (exit) behavior. The future researcher may benefit from replicating and extending the model in different industries for further contribution to the CCB literature.

To the best of the author's knowledge, there is no evidence of consumer segmentation based on their complaining behavior or socio-demographic and psychographic factors by employing CHAID decision tree analysis. In addition to illustrating the use of data mining techniques such as CHAID in the field of CCB, it also contributes to the extant literature by researching in a non-Western setting like India.