Conclusions about the research problem

In this paragraph the conclusions about the initial research problem are drawn. Paragraph 5.2 already drew some conclusions on the hypotheses based on the data analysis of Chapter 4. These conclusions are taken together here, and are used to answer the overall research question and to draw conclusions on this research. The hypotheses are supported by the data analysis of Chapter 4, but there are some interesting results that have influence on the supposed hypotheses and research framework. The overall research question where the hypotheses are extracted from is the following:

How to support customization and personalization for pure digital products in the Internet economy to dramatically decrease complexity and search costs for consumers, so variety can be maximized?

This overall research question followed from the research problem which was identified in Chapter 1. The goal of this research is to make an addition to the work of Brynjolfsson et al. (2003), who argue that increased online availability of previously hard-to-find products represents a positive impact on consumer surplus. This addition can be found in the use of customization of digital products on the Internet.

This research is both explanatory and exploratory. It is explanatory because it explains where the hypotheses can be supported and where they can not. It is exploratory because the hypotheses are based on literature on the customization of physical products, where this research addresses customization of digital products. Through the research presented in this thesis, I came to a better understanding on how to support customization according to the overall research question. The data analysis in Chapter 4 has lead to insights that were discovered during the interviews, and that were not assumed and explained from literature. The hypotheses still hold after the data analysis, but some are revised.

The most important conclusion from the data analysis is that the lowering of search costs for consumers is not the most important goal of the customization of digital products in the Internet economy. The average interaction length of time increases the number of alternatives searched, but that does not mean an increase in search costs for consumers. More important is that discovering new digital products should be made easier for consumers. This and other insights have direct implications for the research framework presented in Chapter 1 and elaborated on in Chapter 2. Before the research framework is revised, I reflect on the conclusions of the hypotheses in paragraph 5.2

The conclusion on H1 was that the larger the product variety, the higher the level of customization. However, the increase in variety has the most influence on the type of modularity employed. Customization can be seen as a strategy for the supplier and as interaction possibilities for the consumer. This way of treating customization is in line with the definition that was proposed in this thesis (section 2.2.1), which in short states that customization is a strategy that creates value by some form of consumer–supplier interaction. This conclusion results in the revision of the hypothesis from H1 to H1’:

H1’: The larger the product variety, the higher the level of customization in terms of modularity at the assembly and use stages.

The conclusion on H2 was that the higher the level of customization, the larger the variety, but not on all variety factors and also not all customization factors increase variety. The type of modularity employed is the most important customization factor that increases variety, because modularity as one of the characteristics of the mass customization classification increases variety on the multiple usages of modules. The most important specific customization factors that increase variety are automatic and direct recommendations and presenting large choice sets by attribute instead of by alternative. They are the most important because they are supportive to the type of modularity employed. Consumer expertise and trust building do not increase variety, but they can not be seen as consumer-supplier interaction. This conclusion results in the revision of the hypothesis from H2 to H2’:

H2’: Mass customizers that can be classified as involvers, increase the variety or choice.

The cases can neither prove nor reject the proposed hypothesis H3.1. The cases show deviations with theory, because the increase in variety illustrated by the cases does not increase complexity for the consumers. It seems that it is desired to increase the average interaction length of time when customizing digital products. The increased average interaction length of time has influence on the second part of the third hypothesis. The cases support hypothesis H3.2 to some extent. It is true that the average interaction length of time is related to the number of alternatives searched. According to theory, the average interaction length of time should be minimized to reduce complexity (Blecker et al., 2006), and the number of alternatives searched should be minimized to reduce search costs (Blecker et al., 2006; Kurniawan et al., 2006). The cases show however that it is not the purpose to decrease the interaction length of time, but to increase interaction with the consumer. The above results in the revision of the hypotheses H3.1 and H3.2 to H3.1’ and H3.2’:

H3.1’: The larger the product variety, the higher the average interaction length of time.
H3.2’: The higher the average interaction length of time, the better it is for consumers to discover new digital products.

The cases support hypothesis H4, albeit not completely. Except for two customization factors, the data analysis of Chapter 4 shows that customization decreases search costs on two of the three search costs factors. Both case studies show results that are similar for most customization factors. Because it does not seems to be the goal to lower search costs for consumers, but to increase the discovery of new digital products, customization by means of consumer-supplier interaction should increase the average interaction length of time. This increase in turn should result in the discovery of more new digital products for consumers. The above results in the revision of the hypothesis H4 to H4’:

H4’: The higher the level of customization, the better it is for consumers to discover new digital products.

It can be concluded that customization and personalization should be supported by employing modularity at the assembly and use stages, and consumer involvement at the design and fabrication stages. Employing modularity at the assembly and use stages increases variety, and consumer involvement at the design and fabrication stages supports customization that lowers search costs. Taken together, mass customizers who can be classified as involvers in terms of the mass customization classification of Duray et al. (2000), make it easier for consumers to find previously hard-to-find digital products on the Internet, while variety induced complexity is controlled and variety is high. According to theory, lower the average interaction length of time is a factor that decreases variety induced complexity for the consumer, which in turn should decrease search costs. The cases demonstrate however that it is not the purpose to lower, but to increase the average interaction length of time. This increase in interaction should make it easier for consumers to discover new digital products. The conclusions on the hypotheses in paragraph 5.2 and the revision of them in this paragraph leads to the revision of the research framework, which is visualized in Figure 5.1.

fig 5.1

In summary, the most important findings and conclusions are:

  • customization of digital products can be split in generic and specific customization;
  • an increased variety of digital products does not lead to more complexity;
  • the average interaction length of time should not be decreased but increased;
  • discovering new digital products or modules is more important than minimizing search costs.

Generic customization can be seen as a strategy to follow for suppliers. The optimal customization strategy in terms of the mass customization classification is offering consumer-supplier interaction at the design and fabrication stages, and to employ modularity at the assembly and use stages. Customizers that follow this strategy can be classified as involvers (Duray et al., 2000). An increase in external variety of digital products does not increase complexity. It does increase the average interaction length of time, but that is desired when customizing digital products. The cases show that an increase of variety is something that is something to strive for because more consumers can be reached, and these consumers can customize more by interacting more, and can discover more digital products.

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