Delimitaciones del concepto

This section discusses (1) the selection of ‘mobile location service (MLS)’, ‘mobile content services’, and ‘mobile marketing’ as the most promising segments for studying alliances. In

addition, it is argued that (2) the selection of nine cases⎯two cases in the narrow field of MLS and up to four cases in the broad field of mobile information services⎯is the most reasonable research strategy.

Segment selection

The selection of industry segments is based on two criteria: (1) the alliance activity and (2) the segment attractiveness. The first criterion is motivated by Eisenhardt’s argument (1989) that we learn more from extreme settings. As such, alliance portfolios can be better observed in a segment that has a high alliance intensity. When many alliances are developed, the alliance portfolio of every company must be constantly restructured. And given the fact that companies⎯especially small ones⎯are only capable of interacting with a limited number of partners at any one time, it necessary follows that many alliances will have to be terminated.

The second criterion⎯segment attractiveness⎯will affect the generalizability and importance of the study’s results. Two different arguments support this criterion. (1) Promising segments will have higher future sales and, therefore, a higher proportion of industry sales in the future. Their characteristics will have a higher impact on the characteristics of the overall industry. (2) Promising segments often are pushed by very promising companies, which are recognized as ‘stars’ in the industry. Other companies tend to copy the structures and processes of these ‘stars’ and thereby adapt their own business model to the most popular business model in the promising segment. In other words, other segments will tend to follow the most promising segments. Both arguments lead to the point, that studying the most promising segments has the highest generalizability and importance.

Figure 16 shows the industry segments mapped by their alliance activity and their mid-range prospects. The alliance activity is measured in importance of the weighted drivers mentioned in chapter 3.1 (results are shown in appendix 2); the mid-range prospects are measured in revenue growth rates (2002-2005) by adjusting for growth rates of segments, which were nearly irrelevant in 2002. The latter segments were taken out of the sample because no sufficient alliance history could be tracked. The growth results are based on the data provided in figure 11 ‘European mobile service and content revenue forecast’. A detailed table is provided in appendix 3.

Research attractiveness of different industry segments

Low Low High

Source: Author, based on Booz Allen Hamilton (2001a, 2001b) EITO (2002), Gartner (2002), Frost and Sullivan (2002) 1,00

Music and Entertainment m-Learning

m-Office

m-Health and wellness s ervices m-Games

Figure 16 Mobile Internet segment portfolio

Based on this mapping, ‘location-based-service (LBS)’, ‘mobile information services’, and

‘mobile marketing’ are picked as most promising segments. A detailed description of each of the analyzed segments is provided as an introduction in each of the particular sections (sections 3.2 - 3.4).

Case selection

As previously described in the research methodology in sub-section 2.2.3, the selection of the number of cases is based on two criteria: (1) size of the segment and (2) diversity of the segment. In addition, a minimum of two cases is required from each segment.

The argument for the size criterion is similar to the argument for growth in the segment selection and is motivated by the goals of generalization and significance. The size of a segment is measured in forecasted industry sales in 2005. The diversity criterion attempts to control for differences in business models within a segment. Heterogeneous business models are likely to cause differences in performance. Thus, more cases are selected in heterogeneous segments to understand the performance differences created from efficient alliance portfolio management and to distinguish them from performance differences caused by differences in business models⎯especially due to different value chains. Segment diversity is measured by the variance of business models.

The minimum requirement of two cases per segment was set to facilitate within-segment comparisons. In each segment, a company that is well known and rewarded, and a company

that is not known for being successful have been selected. Table 6 shows the assessment of the target segments.

Segment Segment size and growth

rate

Evaluation of segment diversity

Reasoning¹⁹

MLS Large segment,

medium high growth

Medium low variety

Only partly established segment, due to technical requirements (location data). No industry association installed yet, but different industry forums as OGIS Similar value prepositions of companies, limited differences in business models.

MCS Large segment

medium high growth

Large variety Established but diverse segment (many different services as traffic, sports, financial data, etc.), No industry association established. Different value prepositions: integrated content creation (such as Clever.Tanken or Airweb) to pure content enabling for stock quotes. Differences in the business model easily observable

Medium variety Fast established segment, Industry associations exist Similar value prepositions between YOC, 12snap, mindmatics …etc.. Differences to ApollisInteractive due to the technology employed

Table 6 Segment evaluation, author

Based on this segment evaluation, nine cases were selected with the following distribution:

- two cases in the LBS segment: Gate5 and YellowMap

- four cases in the mobile information service segment: Airweb, Clever.Tanken, e-hotel, and Multichart

- three cases in the mobile marketing segment: 12snap, mindmatics, and ApollisInteractive

Figure 16 captures the segment and case selection criteria and provides the company names of the selected case studies. The following sections will give an overview of these segments and an introduction to the selected cases. It should be noted that the selection of cases was not made according to the proposed ideal way (Eisenhardt, 1989), that is, by stopping after the incremental learning of the last case study was below a certain threshold. Instead the selection of the number of case studies was based on Eisenhardt’s rule of thumb:

‘Finally, while there is no ideal number of cases, a number between 4 and 10 cases usually works well.’ (Eisenhardt, 1989, p. 545)

Although it is not the ideal procedure, it is a practical one, and is used by many researchers.

Eisenhardt (1989) supports this procedure as well.

19 A more thorough view of the business model complexity is the introduction of the chapter 3.2-3.4

‘In practice, theoretical saturation often combines with pragmatic considerations such as time and money to dictate when case collection ends. In fact, it is not uncommon for researchers to plan the number of cases in advance.’ (Eisenhardt, 1989, p. 545)