Ming-Huei Hsieh

Product-, corporate-, and country-image dimensions and purchase behavior: A multicountry analysis

This research focuses on consumer perceptions that are developed on the basis of a firm’s advertising appeals as well as other factors. In conceptualizing brand-image perceptions, the authors extend the frequent use of productrelated images to include corporate and country images attached to brands. The authors report findings based on secondary economic and cultural data at the macro level and the results of a global brand-image survey conducted in the top 20 international automobile markets at the individual level. The findings suggest that while consumers’ attitudes toward corporate image and country image exert main effects on their brand purchase behavior, the effects of certain product-image appeals are moderated by sociodemographics and national cultural characteristics. The empirical results are broadly supportive of the proposed hypotheses and provide a consumer-based extension of Roth’s work on global brand image.

Understanding consumer heterogeneity: A business intelligence application of neural networks

This paper describes a business intelligence application of neural networks in analyzing consumer heterogeneity in the context of eating-out behavior in Taiwan. We apply a neural network rule extraction algorithm which automatically groups the consumers into identifiable segments according to their socio-demographic information. Within each of these segments, the consumers are distinguished between those who eat-out frequently from those who do not based on their psychological traits and eat-out considerations. The data set for this study has been collected through a survey of 800 Taiwanese consumers. Demographic information such as gender, age and income were recorded. In addition, information about their psychological traits and eating-out considerations that might influence the frequency of eating-out were obtained. The results of our data analysis show that the neural network rule extraction algorithm is able to find distinct consumer segments and predict the consumers within each segment with good accuracy.

Extracting salient dimensions for automatic SOM labeling

Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not need accompanying desired output information. Prior to its use in some real-world applications, however, a trained SOM often has to be labeled. This labeling phase is usually supervised in that labeled patterns need accompanying output information. Because such labeled patterns are not always available or may not even be possible to construct, the supervised nature of the labeling phase restricts the deployment of SOM from a wide range of potential domains of application. This work proposes a methodical and automatic SOM labeling procedure that does not require a set of prelabeled patterns. Instead, nodes in the trained map are clustered and subsets of training patterns associated to each of the clustered nodes are identified. Salient dimensions per node cluster that constitute the bases for labeling each node in the map are then identified. The effectiveness of the method is demonstrated on a SOM-based international market segmentation study.

Automatic knowledge extraction from survey data: learning M -of- N constructs using a hybrid approach

Data collected from a survey typically consist of attributes that are mostly if not completely binary-valued or binary-encoded. We present a method for handling such data where the underlying data analysis can be cast as a classification problem. We propose a hybrid method that combines neural network and decision tree methods. The network is trained to remove irrelevant data attributes and the decision tree is applied to extract comprehensible classification rules from the trained network. The conditions of the rules are in the form of a conjunction of M-of-N constructs. An M-of-N construct is a rule condition that is satisfied if (at least, exactly, at most) M of the N binary attributes in the construct are present. The effectiveness of the method is illustrated on data collected for a study of global car market segmentation. The results show that besides achieving high predictive accuracy, the method also allows meaningful interpretation of the relationships among the data variables.

Knowledge Sharing Through Intranet-Based Learning: A Case Study of an Online Learning Center

Recent academic and managerial interest in electronic commerce (e-commerce) activities has created enormous interest in the world of information technology and in many other industries. Therefore, managers are facing new challenges. One such daunting task is the ability to manage knowledge, as this can now be exchanged or transferred on the Internet or Intranet without physical contact or time constraints. To understand some of the key human resource issues related to organizing global knowledge in the e-commerce context, an exploratory case study was conducted. One of the key findings from this case study is the recognition that human resource management (HRM) will play a new dual role in organizing global knowledge sharing in the e-commerce era. One role is to continue dealing with traditional administrative transactions and the other is to nurture knowledge-related activities. This contradicts simplistic prescriptions about managing knowledge, which suggests that the implementation and utilization of a particular information system are all that are necessary to facilitate effective knowledge sharing. Instead, this exploratory study shows that successful knowledge sharing is dependent not only on the use of particular information technologies but also on the successful creation of a knowledge-sharing environment with a knowledge management-focused HRM as the coordinator of related activities.

Improved SOM Labeling Methodology for Data Mining Applications

Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of large volumes of data in various data mining applications. As a special form of neural networks, they have been attractive as a data mining tool because they are able to extract information from data even with very little user-intervention. However, although learning in self-organizing maps is considered unsupervised because training patterns do not need desired output information to be supplied by the user, a trained SOM often has to be labeled prior to use in many real-world applications. Unfortunately, this labeling phase is usually supervised as patterns need accompanying output information that have to be supplied by the user. Because labeled patterns are not always available or may not even be possible to construct, the supervised nature of the labeling phase restricts the deployment of SOM to a wider range of potential data mining applications. This work proposes a methodical and semi-automatic SOM labeling procedure that does not require a set of labeled patterns. Instead, nodes in the trained map are clustered and subsets of training patterns associated to each of the clustered nodes are identified. Salient dimensions per node cluster, that constitute the basis for labeling each node in the map, are then identified. The effectiveness of the method is demonstrated on a data mining application involving customer-profiling based on an international market segmentation study.

Predicting consumer preference for fast-food franchises: a data mining approach

The objectives of the study reported in this paper are: (1) to evaluate the adequacy of two data mining techniques, decision tree and neural network in analysing consumer preference for a fast-food franchise and (2) to examine the sufficiency of the criteria selected in understanding this preference. We build decision tree and neural network models to fit data samples collected from 800 respondents in Taiwan to understand the factors that determine their brand preference. Classification rules are generated from these models to differentiate between consumers who prefer the brand and those who do not. The generated rules show that while both decision tree and neural network models can achieve predictive accuracy of more than 80% on the training data samples and more that 70% on the cross-validation data samples, the neural network models compare very favourably to a decision tree model in rule complexity and the numbers of relevant input attributes.

Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception

A three-tier knowledge management approach is proposed in the context of a cross-national study of car brand and corporate image perceptions. The approach consists of knowledge acquisition, transfer and revision using neural networks. We investigate how knowledge acquired by a neural network from one car market can be exploited and applied in another market. This transferred knowledge is subsequently revised for application in the new market. Knowledge revision is achieved by re-training the neural network. Core knowledge common to both markets is retained while some localized knowledge components are introduced during network re-training. Since the knowledge acquired by a neural network can be expressed as an accurate set of simple rules, we are able to compare the knowledge extracted from one network with the knowledge extracted from another. Comparison of the originally acquired knowledge with the revised knowledge provides us with insights into the commonalities and differences in car brand and corporate perceptions across national markets.