Growth of milk production in German dairy farms: an empirical study based on event history analysis

This article investigates determinants of growth of milk production in German dairy farms with the use of event history analysis. This methodology enables the analysts to consider time as a proxy for not measurable effects on growth. The likelihood of a farm's moving from a nongrowth episode into a growth episode is estimated and the impact of various covariates on that likelihood is assessed. The analysis is based upon a balanced panel of annual farm accounts from 616 specialized dairy farms in Germany, covering the financial years 1995/1996–2008/2009. The results from event history analyses are presented for low and high growth rates. For both groups, it was found that the probability of entering a growth episode, defined as the event to be analyzed, increases over time, e.g., as a consequence of an increasing need to improve competitiveness. Moreover, several covariates, such as the share of subsidies in returns, farmer's age, and milk price, had a significant impact on growth in a farm's milk production. The analysis revealed, however, that the effect‐direction of some explanatory variables differed between the two groups.     

Indicators of opinion leadership in customer networks: self-reports and degree centrality

In this paper, we assess two alternative indicators of opinion leadership, self-reported opinion leadership and degree centrality, on the same dataset. We also investigate the interaction effect of these two indicators and the social network environment on opinion leadership. We use social network and survey data from the mobile telecom industry to analyze opinion leadership in smartphone adoption. We find that degree centrality indicates opinion leadership, but that self-reported opinion leadership indicates opinion leadership only under the right social circumstances. In case of weak to moderate network ties, the effect of self-reported opinion leadership is not significant. However, self-reported opinion leaders more effectively influence their strong ties. This study sheds light on indicators of opinion leadership and provides insights for managers to improve their social marketing campaigns.     

Staying Power of Churn Prediction Models

In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging procedure. Bagging consists of averaging the results of multiple models that have each been estimated on a bootstrap sample from the original sample. We test the models using customer data of two firms from different industries, namely the internet service provider and insurance markets. The results show that the classification tree in combination with a bagging procedure outperforms the other three methods. It is shown that the ability to identify high risk customers of this model is similar for the in-period and one-period-ahead forecasts. However, for all methods the staying power is rather low, as the predictive performance deteriorates considerably within a few periods after the estimation period. This is due to the fact that both the parameter estimates change over time and the fact that the variables that are significant differ between periods. Our findings indicate that churn models should be adapted regularly. We provide a framework for database analysts to reconsider their methods used for churn modeling and to assess for how long they can use an estimated model.     

Reward redemption effects in a loyalty program when customers choose how much and when to redeem

The redemption of loyalty program (LP) rewards has an important impact on LP members' behavior, particularly on purchase behavior before and after redeeming a reward. However, little is known about the interplay between members' purchase and redemption behavior when members are not pressured with point expiration and they choose for themselves when and how much to redeem. In this context, the effects of redemption are not straightforward, as little additional effort is required from an LP member to obtain the reward. Analyzing the behavior of 3094 members in such an LP, we find that the mere decision to redeem a reward significantly enhances purchase behavior before and after the redemption event, even when members redeem just a fraction of their accumulated points. Conceptually, we refer to this enhancement as the redemption momentum, which is an alternative and novel explanation of the existence of pre-reward effects that do not depend on points-pressure. In addition to the overall impact of redemption on purchases, prior purchase behavior also enhances redemption decisions. Finally, we find a number of moderating effects on purchase and redemption behavior that derive from the length of LP membership, age, income and direct mailings. Our study's most important managerial implication is that firms should avoid imposing point expiry and/or binding thresholds in order to enhance members' purchase behavior.     

Dynamic Effects of Sponsoring: How Sponsorship Awareness Develops Over Time

This study takes a longitudinal perspective to investigate sponsorship awareness in a multiple-year, international context pertaining to the Union of European Football Associations (UEFA) Champions League, a prestigious European soccer competition for club teams with worldwide exposure. A logit model aims to predict sponsorship awareness for one of the main sponsors of the league using data pertaining to more than 25,000 consumers from five countries, over the period 2005 to 2009. The findings provide an empirical argument for building long-term relationships. Sponsorship awareness levels increase over the measurement period, and the highest increase occurs in the second year of sponsorship.     

Multiattribute perceptual mapping with idiosyncratic brand and attribute sets

This article proposes an extremely flexible procedure for perceptual mapping based on multiattribute ratings, such that the respondent freely generates sets of both brands and attributes. Therefore, the brands and attributes are known and relevant to each participant. Collecting and analyzing such idiosyncratic datasets can be challenging. Therefore, this study proposes a modification of generalized canonical correlation analysis to support the analysis of the complex data structure. The model results in a common perceptual map with subject-specific and overall fit measures. An experimental study compares the proposed procedure with alternative approaches using predetermined sets of brands and/or attributes. In the proposed procedure, brands are better known, attributes appear more relevant, and the respondent??s burden is lower. The positions of brands in the new perceptual map differ from those obtained when using fixed brand sets. Moreover, the new procedure typically yields positioning information on more brands. An empirical study on positioning of shoe stores illustrates our procedure and resulting insights. Finally, the authors discuss limitations, potential application areas, and directions for research.     

Transaction Costs and standardisation in professional services to small business

In the market of business services the easier segment of large business is being saturated. Therefore, suppliers of business services must seek to cater to the growing but more difficult segment of small business, if their growth is to be maintained. With appropriate extensions, Transaction Cost Economics can facilitate understanding of the nature of the difficulty involved. Due to effects of scale in transaction costs it is relatively more expensive to provide services tailored to the individual smaller firm. Standardisation of services may be required to make access to this market viable. The question arises whether such standardised methods should be stimulated by the government. To see how this issue is perceived by suppliers of various business services, a survey was conducted on a sample of 1,000 firms, with a response of 30%. The issue of economies of scale in transaction costs did emerge, but was not universally perceived as a problem. Many respondents confirmed the need for standardised modules. Opinions were sharply divided on the issue whether the government should step in to promote standardisation across suppliers.Cluster analysis shows that respondents fall neatly into a number of classes that conceptually and statistically are highly distinct. Just over half of the respondents were clearly against government intervention, and about one third of these (radical market proponents) were of that opinion even though they granted that the scale issue constituted a problem of access to the small business market. Just under half of the respondents clearly favoured government intervention, and about half of those (radical interventionists) were of that opinion even though they saw no great problem of access due to problems of scale, or were hesitant about that problem. Accountancy firms tended to be in favour of intervention, and business consultants tended to be against. The government might explore the matter further with accountants, while leaving the business consultants alone.     

Meta-Analysis in Marketing when Studies Contain Multiple Measurements

Most meta-analyses in marketing contain studies which themselves contain multiple measurements of the focal effect. This paper compares alternative procedures to deal with multiple measurements through the analysis of synthetic data sets in a Monte Carlo study and a re-analysis of a published marketing data set. We show that the choice of procedure to deal with multiple measurements is by no means trivial and that it has implications for the results and for the validity of the generalizations derived from meta-analyses. Procedures that use the complete set of measurements outperform procedures that represent each study by a single value. The commonly used method of treating all measurements as independent performs reasonably well but is not preferable. We show that the optimal procedure to account for multiple measurements in meta-analysis explicitly deals with the nested error structure, i.e., at the measurement level and at the study level, which has not been practiced before in marketing meta-analyses.     

Children's Understanding of TV Advertising: Effects of Age, Gender, and Parental Influence

Parents, consumer organizations, and policy makers are generally concerned about effects of TV advertising directed towards children. These effects might be mediated by children's understanding of TV advertising, that is their ability to distinguish between TV programmes and commercials and their comprehension of advertising intent. In this paper, we investigate children's understanding of TV advertising, using verbal and non-verbal measurements. The sample consists of 153 Dutch children, ranging from 5 to 8 years old, and their parents. The results based on non-verbal measures suggest that most children are able to distinguish commercials from programmes and that they have some insight into advertising intent. The results based on verbal measures are not as conclusive; the percentage of children who show understanding of TV advertising is then substantially lower. Effects of age, gender, and parental influence are assessed using MURALS, a regression analysis technique for categorical and continuous variables, and CHAID, a technique for identifying homogeneous segments on the basis of the relationship between categorical dependent and explanatory variables. The age of a child turns out to have a positive effect. The effects of gender and parent- child interaction are rather small, both for verbal and for non-verbal measures of understanding of TV advertising. A high level of parental control of TV viewing may result in lower understanding of TV advertising. Implications for consumer policy and directions for future research are discussed.