Contributions published at Marketing (Martin Natter)

The aim of this paper is to develop a model predicting the collected amount of waste paper at the regional level of municipalities. Learning about the factors that influence the amount of collected paper is a prerequisite for the evaluation and reorganization of collection systems. We hypothesize that the amount of collected paper depends on both the waste potential and the factors which influence the convenience such as the density of collection sites. For this study, we used a sample of 649 municipalities. Between the municipalities, the data show a high variance in terms of the collected waste paper per person and year. We developed a multivariate regression model providing valuable insights about the relationship between demographic parameters and the amount of collected waste paper. Furthermore, we found a significant positive impact of the convenience of the collection system.

Conjoint analysis is one of the most important tools to support product development, pricing and positioning decisions in management practice. For this purpose, various models have been developed. It is widely accepted that models that take consumer heterogeneity into account, outperform aggregate models in terms of hold-out tasks. The aim of our study is to investigate empirically whether predictions of choice-based conjoint models which incorporate heterogeneity can successfully be generalized to a whole market. To date no studies exist that examine the real world performance of choice-based conjoint models by use of aggregate scanner panel data. Our analysis is based on four commercial choice-based conjoint pricing studies including a total of 43 stock keeping units (SKU) and the corresponding weekly scanning data for approximately two years. An aggregate model serves as a benchmark for the performance of two models that take heterogeneity into account, hierarchical Bayes (HB) and latent class (LC). Our empirical analysis demonstrates that, in contrast to the performance using hold-out tasks, the real world performance of HB and LC is similar to the performance of the aggregate model. Our results indicate that heterogeneity cannot be generalized to a whole market and suggest that aggregate models are sufficient to predict market shares (MSs).

In this paper, the authors develop a model to predict collected amounts of glass at the regional level of municipalities. Learning about the factors that influence the amount of collected glass is a prerequisite for the evaluation and reorganisation of collection systems. Furthermore, such a model provides essential input for decisions like restructuring activities, implementation of legal directives or investments into new recycling plants. The authors hypothesise that the amount of collected glass depends on both, the waste glass potential and factors which influence the convenience such as the density of collection sites. The authors develop a multivariate regression model providing valuable insights about the relationship between demographic parameters and the amount of collected waste glass, as well as between logistic parameters and collected waste glass. The aim of such a model is the identification of parameters which significantly influence the amount of collected waste in order to provide decision makers with a tool for accurate planning. A significant positive impact of the logarithmic number of collection sites on the amount of waste glass collected was found. A significant impact of the percentage of city area, overnight stays per person, percentage of employees in the service sector, indices of purchasing power and percentage of alpine area were identified. The model explains the amount of collected waste glass and can help to estimate the waste glass potential.

The literature on recommendation systems indicates that the choice of the methodology significantly influences the quality of recommendations. The impact of the amount of available data on the performance of recommendation systems has not been systematically investigated. The authors study different approaches to recommendation systems using the publicly available EachMovie data set containing ratings for movies and videos. In contrast to previous work on this data set, here a significantly larger subset is used. The effects caused by the available number of customers and movies as well as their interaction with different methods are investigated. Two commonly used collaborative filtering approaches are compared with several regression models using an experimental full factorial design. According to the findings, the number of customers significantly influences the performance of all approaches under study. For a large number of customers and movies, it is shown that simple linear regression with model selection can provide significantly better recommendations than collaborative filtering. From a managerial perspective, this gives suggestions about the selection of the model to be used depending on the amount of data available. Furthermore, the impact of an enlargement of the customer database on the quality of recommendations is shown.

This paper analyzes acquisitions resulting in a product line expansion of a firm. When the firm faces a non-stationary and stochastic demand in both the current and the new product line, switching between the production facilities may give diversification advantages. Switching between production facilities is similar to holding an inventory for both products. A case in the beverages industry illustrates that even when switching costs are relatively high as compared to inventory costs, switching has significant advantages over holding inventories.

This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting.

This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting.

The valuation of Flexible Manufacturing Systems is one of the most frequently undertaken productivity improvement activities. In practice, the introduction of an FMS into industry must be done on the basis of cost justification. Recently developed techniques for the evaluation of the value of flexibility typically include the computation of stochastic dynamic programs. However, the computational effort of stochastic dynamic programs grows combinatorially and limits application to real world problems. In this contribution, we derive fast approximations to the stochastic dynamic program and compare their results to the exact solution. The proposed methods show an excellent worst case behavior (1%) for a wide range of volatility of the underlying stochastic profit margins and costs for switching the production mode. The computational effort is reduced by a factor of more than 200.

Two neural network approaches, Kohonen's Self-Organizing (Feature) Map (SOM) and the Topology Representing Network (TRN) of Martinetz and Schulten are employed in the context of competitive market structuring and segmentation analysis. In an empirical study using brands preferences derived from household panel data, we compare the SOM and TRN approach to MULTICLUS, a parametric approach which also simultaneously solves the market structuring and segmentation problem. Our empirical analysis shows several benefits and shortcomings of the three methodologies under investigation, MULTICLUS, SOM, and TRN. As compared to MULTICLUS, we find that the non-parametric neural network approaches show a higher robustness against any kind of data preprocessing and a higher stability of partitioning results. As compared to SOM, we find advantages of TRN which uses a more flexible concept of adjacency structure. In TRN, no rigid grid of units must be specified. A further advantage of TRN lies in the possibility to exploit the information of the neighborhood graph which supports ex-post decisions about the segment configuration at both the micro and the macro level. However, SOM and TRN also have some drawbacks as compared to MULTICLUS. The network approaches are, for instance, not accessible to inferential statistics. Our empirical study indicates that especially TRN may represent a useful expansion of the marketing analysts tool box.

An artificial neural network (ANN) algorithm is proposed that incorporates both market segmentation and discriminant (regression) analysis of the segments. The method simultaneously estimates the models relating consumer characteristics to market segments, i.e., subjects are assigned to (unique) segments so that subjects within a class show similar purchase behavior and share the same characteristics (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm. The performance of the ANN methodology is assessed in a Monte-Carlo study. In contrast to the usual stepwise approach adopted in segmentation studies, our study found that simultaneous segmentation and discrimination are preferable for finding an overall optimum in that this way clusters are formed not only to create homogeneous submarkets but also to show a good dicriminatory behavior.

We compare the performance of a specifically designed feedforward artificial neural network with one layer of hidden units to the K-means clustering technique in solving the problem of cluster-based market segmentation. The data set analyzed consists of usages of brands (product category: household cleaners) in different usage situations. The proposed feedforward neural network model results in a two segment solution that is confirmed by appropriate tests. On the other hand, the K-means algorithm fails in discovering any somewhat stronger cluster structure. Classification of respondents on the basis of external criteria is better for the neural network solution. We also demonstrate the managerial interpretability of the network results.

The main contribution of this paper is a method that allows one to study the effects of different degrees of competition. We find that optimal prices and profits are more sensitive to cooperative than to aggressive behavior on the part of competitors. With more aggressive policies, the average pricing level decreases and the average difference between high and low prices increases. An empirical model of the detergent market illustrates the methodology.

We propose to use neural networks to value options when analytical solutions do not exist. The basic idea of this approach is to approximate the value function of a dynamic program by a neural net, where the selection of the network weights is done via simulated annealing. The main benefits of this method as compared to traditional approximation techniques are that there are no restrictions on the type of the underlying stochastic process and no limitations on the set of possible actions. This makes our approach especially attractive for valuing Real Options in flexible investments. We, therefore, demonstrate the method proposed by valuing flexibility for costly switch production between several products under various conditions.

Adaptive methods are used to forecast three main Austrian economic indicators. We use a weighted recursive model as well as a neural network approach both with and without adaptive characteristics and compare our results to the forecasts of two Austrian research institutes. It appears that even models which use very limited information can outperform the two Institutes’ forcasts of the unemployment rate. For the case of most series adaptivity represents a possibility of improving the forecasts.