Comparing Aggregate and Individual Measures of Habit A Study of Grocery Buying Behaviour

Comparing Aggregate and Individual Measures of Habit A Study of Grocery Buying Behaviour Thang Pham, Richard Mizerski, The University of Western Australia, James Wiley, Journal of Business Research Katherine Mizerski, Edith Cowan University Abstract The Binomial Negative Distribution (abbreviated NBD) model, initially applied in marketing by Ehrenberg (1959) has been used extensively ever since to predict purchases of products at category and single brand levels. This study employed a new measure of habit called Self-Report Habit Index (SRHI) to predict purchases at the individual level. The hope is to find the appropriate mechanism at micro level that could generate equivalent results with those of NDB. The empirical base to examine the supposition is grocery purchasing behaviour of young consumers in New Zealand. Literature Review One well-known theory in social science research- the Theory of Planned Behaviour (hereafter TPB) is often accurate in predicting planned performance in many behaviour domains (Armitage & Conner, 2001). However, behaviours examined under these TPB-based studies are largely driven by volitional factors. The theory has overlooked the role of non-volitional factors such as habit and past behaviour in driving human actions. The role of past behaviour within the TPB context received much support among academics in the last decade (Ajzen, 2002). It has been suggested that behaviour when repeated consistently within a stable context can become habit (Ouellette & Wood, 1998). Habit is then the prime factor directing the performance of actual behaviour. Previous studies have deemed the effect of past behaviour linear in that the more the behaviour is repeated, the stronger the habitual force in behaviour. However, in many of these studies, the habit construct was operationalised based on frequency of past behaviour. Consequently, it has been argued that claiming habit is an independent construct from past behaviour is erroneous when the construct itself is measured based on past frequency (Ajzen, 2002). The solution, as suggested by Ajzen (2002), is creating the new measure of habit which operationalises the construct independently from the frequency of past behaviour. Habit, like past behaviour, is a form of learning from past experience. The habit construct, however, has been recognised as an automatic response that requires the history of repetition of the behaviour in a stable supporting context (Ouellette & Wood, 1998; Verplanken & Orbell, 2003). Automaticity can be characterised as a behaviour that performs without awareness, is difficult to control, and it mentally efficient (Verplanken & Orbell, 2003, p.1317). Habits expose limited controllability. The absence of mental awareness in developing and executing the habitual process indicates that cognitive processes have very little impact in triggering habitual behaviour (Aarts, Paulussen, & Schaalma, 1997). So long as appropriate stimuli exist, habitual behaviours are carried out without much deliberation. The efficiency of habit means that habit frees mental capacity to do other things at the same time. A recent development from social psychology research, the Self-report Habit Index (SRHI), promises to resolve the debate on past behaviour versus habit. This newly developed scale attempts to measure habit by capturing its key characteristics without relying on frequency of behaviour. This study will empirically 1

test the robustness of the SRHI scale in the context of grocery shopping by benchmarking it against the well-known Negative Binomial Distribution model. The NBD model provides a simple yet powerful representation of aggregate consumer consumption patterns. It was introduced to the marketing discipline in 1959 by Ehrenberg and has been proved to work well in a wide variety of product categories, across many countries (Morrison & Schmittlein, 1988). This model is mainly used to predict future purchasing patterns. This model is based on three assumptions. Firstly, the NBD assumes that each customer held a λ (Poisson distributed) that determines a number of purchases made towards a particular brand/category within a fixed period of time. These purchases are spread irregularly over times (as-if randomly) and are assumed to be independent of each purchase occasion (zero-order process) (A. S. C. Ehrenberg, Uncles, & Goodhardt, 2004). Secondly, s λ have been assumed to be gamma distributed across population of customers. The gamma distribution in turn has two parameters- scale and shape parameters. The scale parameter is directly proportional to the volume of purchase while the shape parameter shows the balance of light and heavy buyers within the population. Thirdly, NBD is a stationary model which means that each customer remains as Poisson purchasers with his/her λ unchanging over time. In the study described below, the NBD model generates theoretical benchmarks to compare against actual buying patterns of four grocery products at aggregate level. The study then investigates the relationship between the patterns of consumption with the habitual patterns of buying at individual level generated by SRHI. RESEARCH PROCEDURE Data used in this study is derived from a previous research by the same authors at a large New Zealand university that examined the grocery purchasing behaviour of young consumers. A pilot study was conducted to select suitable grocery products that can be used in the study. There were four versions of the questionnaire, each focused on one particular product. The actual data collection took place in two undergraduate business units. Survey forms were mingled to ensure their randomness when distributed to students. The total number of responses attracted was n= 284 for four versions of the questionnaire. After a preliminary data cleaning, 278 surveys were retained for further analyses. The key question estimating purchase frequency took the form of categorical responses where respondents were asked to select one category that best describe their purchase frequency of a particular grocery product. Response categories include never buy, once, two times, three times, four times and more than four times within a period of one month. These responses were then transformed into metric data for the NBD test and comparison of NBD and reported purchases. Because the original questionnaire did not specify purchase frequency bigger than four times per month, the current study assumes that those who selected the more than four times category would buy the product on average of five time a month. This deliberation arises from the assumption that consumers would normally shop for grocery produces on a particular day of the week. This works out about four times per month. Thus, the additional fifth occasion may arise from unexpected needs of the product. Other reasons (eg. multiple shopping trips per week) why a significant number of consumers in this study would purchase a product more than five times a month seem unlikely taking into considerations the typical consumption behaviour of this young consumer segment. This study employed the software by Wright (1999) to test the predictive ability of NBD. Two inputs are required to run this software. They are the product penetration (b) which is the proportion of respondents who actually purchased the product last month and the corresponding average (w). The NBD outputs provide expected frequencies of purchasing behaviour. 2

At the individual level, was measured by the Self-reported Habit Index (SRHI) scale which includes 12 seven-point items asking respondents to report different aspects regarding the habitual nature of their purchase behaviour. Each item was anchored by strongly disagree (1) and strongly agree (7). The scale was then reduced to minimise unnecessary complication in data analysis. Three items in the original scales indicating past behaviour frequency were eliminated to ensure that the new scale legitimately captures the essences of habit other than past behaviour. The factor analysis conducted for the remaining nine items showed multidimensionality in which items loaded onto two components. The reduction process began with computing the item-to-total correlations, deleting item with lowest fitness, fitting the confirmatory factor analysis model and conducting the Chi-Square (χ2) difference test (Voss, Spangenberg, & Grohmann, 2003). The process was reiterated until the χ2 difference was no longer significant. Fit indexes employed in this process include NFI Delta 1 (Normed Fit Index Delta 1), RMSEA (root mean-squared error of approximation) and Pclose (probability of close fit). The newly reduced SRHI scale comprises of four items capturing the automatic nature of habitual behaviour (see Table 1). In the light of validity of the new scale, factor analysis demonstrates strong unidimensionality. All four items are highly correlated with one another and are loaded onto one factor which explains about 73% of variance in the data. The new scale fits well with the existing data (χ2(20)=4.158; NFIDelta1=.999; RMSEA=0; Pclose=1). In addition, face validity is ensured since all four items capture appropriately the automatic essence of habit construct. Furthermore, nomological validity was tested by assessing the relationship between measured by the new SRHI scale with the construct of past behaviour measured by three frequency items. The result shows average correlation (r=0.45) (χ2(70)=156.305; NFIDelta1=.979 RMSEA=0.024; Pclose=1). Therefore, although these two constructs are related in which habit formation requires the existence of consistent past behaviour, they are indeed different constructs. This analysis demonstrates the nomological validity of the new scale. Last but not least important, item-to-item reliability of the new habit scale is also satisfactory (Cronbach alpha= 0.88). In general, this newly reduced scale shows strong reliability and validity. The construct of toward a product is now the composite value of all four items showed below. Purchasing X is something that make me feel weird if i do not do it Purchasing X is something that would require effort not to do it Purchasing X is something I start doing it before I realise I am doing it Purchasing X is something I would find hard not to do Table 1. The SRHI reduced scale measuring the construct of Major Findings The table below demonstrates both observed and theoretical () values of purchase frequency distributions of the consumers in this study across four grocery products. 3

MILK ( r=0.89) EGGS (r=0.88) 5 4 4 3 3 Purchate rate Purchase rate SALAD DRESSING (r=0.81) BUTTER (r=0.78) 7 6 5 4 4 4 3 Purchase rate Purchase rate Figure1. Comparison between observed (O) frequency distributions and (T) frequency distributions of the population making purchases across four products Figure 1 shows that NBD can report closely the actual purchase frequencies of the sampled population (r range from 0.78 to 0.89). In addition, the analysis illustrates that about 60% of the grocery purchases are made by only 30% buyers in the sample, who make four or more purchases in a month. In other words, a minority of population comprising of heaviest buyers makes up most of the purchases in the market. By and large, the grocery purchasing patterns observed in this study conform to the NBD benchmarks. The market penetrations are transformed into percentages of sales accounted by buyers at each in order to test the correlation between SRHI and observations. The transformation is necessary to test the effects of consumption power accounted by each groups of buyer. The figures below demonstrate how well these two measures fit one another across four products examined in this study. 4

MILK (r = 0.66) EGGS (r = 0.63) 3.5 3 2.5 2 1.5 1 0.5 0 6 5 4 3 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 SALAD DRESSING (r = 0.54) BUTTER (r = 0.15) 5 4 4 3 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 4 3 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Figure2. Correlations between NBD-generated percentages of sales and the Self-reported Habit Index measures of Except for butter (r=0.15), the other cases show good correlations between consumption patterns measured at aggregate level by NBD model and those measured at individual level by the SRHI. Pearson correlation values are 0.54, 063 and 0.66 for salad dressing, eggs and milk respectively. The outcomes suggest that there are generally significant and high levels of correlations in consumption patterns between aggregate population and individual levels measured by the NBD and the SRHI scale. Limitations This study has several limitations that may affect the outcomes found. The division of the target sample into four groups has reduced the number of respondents for each product. With regarding to the SRHI, there were a few respondents reporting very high purchase frequencies and may not be accurate. Likewise, the transformations of interval to metric data may have been an effect in obtaining the lower correlations. Finally, this study employed a student sample which may not be ideal for behaviour such as grocery shopping. Conclusion Several initial studies have supported the reliability and the nomological validity of the SRHI in measuring the effects of habit construct in predicting individual behaviour within the context of the Theory of Planned Behaviour (Verplanken & Wood, 2006). This study took a combining approach comparing the predictive ability of the SRHI scale with the well-known NBD model with the hope to find similarities in consumption patterns. The aggregate sales predicted by the NBD model demonstrate 5

good correlations with the patterns of individual consumption derived from the SRHI measures. This seems to support the assumption that repeated grocery purchases in mature categories are consistent at both aggregate and individual levels and follow NBD-like patterns. In general, the most important implication derived from this research is that it may be possible to identify individuals in a population that are purchasing largely on habit, not cognitive reasoning. Specific targeted advertising and promotion may then be possible (A. S. C. Ehrenberg et al., 2004). This information may also benefit public policy by being able to empirically identify groups that may be vulnerable to targeting because they are not making reasoned decisions. References Aarts, H., Paulussen, T., & Schaalma, H. (1997). Physical exercise habit: on the conceptualization and formation of habitual health behaviours (Vol. 12, pp. 363-374). Ajzen, I. (2002). Residual Effects of Past on Later Behavior: Habituation and Reasoned Action Perspectives. Personality & Social Psychology Review, 6(2), 107-122. Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: a meta-analytic review. The British Journal of Social Psychology [NLM - MEDLINE], 40, 471. Ehrenberg, A. S. C. (1959). The Pattern of Consumer Purchases. Applied Statistics, 8(1), 26-41. Ehrenberg, A. S. C., Uncles, M. D., & Goodhardt, G. J. (2004). Understanding brand performance measures: using Dirichlet benchmarks. Journal of Business Research, 57(12), 1307-1325. Morrison, D. G., & Schmittlein, D. C. (1988). Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort? Journal of Business and Economic Statistics, 6(2), 145 159. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 214(1), 54. Verplanken, B., & Orbell, S. (2003). Reflections on Past Behavior: A Self-Report Index of Habit Strength. Journal of Applied Social Psychology, 33, 1313-1330. Verplanken, B., & Wood, W. (2006). Interventions to Break and Create Consumer Habits. Journal of Public Policy & Marketing, 25(1), 90-103. Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the Hedonic and Utilitarian Dimensions of Consumer Attitude. Journal of Marketing Research, 40(3), 310-320. Wright, M. (1999). NBDNORMS Version 0.1 (Beta): Software for Calculating NBD and Mixed Poisson Norms of Buyer Behaviour: New Zealand. 6