Data collection

A striking feature of research to build theory from case studies is the frequent overlap of data analysis with data collection. For example, Glaser and Strauss (1967) argue for joint collection, coding, and analysis of data. While many researchers do not achieve this degree of overlap, most maintain some overlap, which is also shown in figure 4.

Overlapping data analysis with data collection not only gives the researcher a head start in analysis but, more important, allows researchers to take advantage of flexible data collection. Indeed, a key feature of theory-building case research is the freedom to make adjustments during the data collection process. These adjustments can be the addition of cases to probe particular themes that may emerge.

Additional adjustments can be made to data collection instruments, such as adding questions to an interview protocol or to a questionnaire (Harris, et al., 1986). These adjustments allow researchers to probe emergent themes or to take advantage of special opportunities that may be present in a given situation. In other situations adjustments can include the addition of data sources in selected cases.

These alterations raise an important question: Is it legitimate to alter and even add data collection methods during a study? For theory-building research, the answer is “yes”, because investigators are trying to understand each case individually, and in as much depth as is feasible. The goal is not to produce summary statistics about a set of observations.

Thus, if a new data collection opportunity arises or if a new line of thinking emerges during the research, it makes sense to take advantage by altering data collection, if such an alteration is likely to better ground the theory or to provide new theoretical insight. This flexibility is not a license to be unsystematic. Rather, this flexibility is controlled opportunism in which researchers take advantage of the uniqueness of a specific case to improve resultant theory (Eisenhardt, 1989).

This study is based on primary and secondary data. Internet searches were used to gather secondary data (i.e., press clippings, annual reports, and product information). In addition, press search on every case company was conducted within the databases of Factiva, Hoover’s and OneSource.

The primary data were gathered by ‘face to face’ interviews (with one exception via telephone), follow-up telephone-calls, and e-mails. Interviewees were searched and selected based on three criteria: they had to be high level to answer the strategic questions; they had to be involved in the alliance activities, and they had to be with the firm for more than two years to be knowledgeable about its development. In addition, the author tried to get more

than one interview where feasible. The final interview sample (a detailed list is enclosed in the appendix) looks as follows:

Position in Firm CEO CFO Business development / managing directors

Marketing and sales

Number of interviewees

5 2 2 2

Thereof founders 4 2 - -

Table 4 Interview sample

Close to 70% of the interviewees were on a board level and more than 50% were founders of the case study firms. The other interviewees were all second level, thus it was a high level interview panel. In two companies two people were interviewed, two check for consistency.

In the other firms only one employee was interviewed. Not being the ideal case, this procedure is justifiable for three reasons. First, the collected data is largely objective.

Performance data as revenues, employees etc, alliance data as formation and termination dates and data on the organizational change as the organizational structure or compensation and reward systems are non interpretative. Second, detailed and excellent secondary data sources helped to validate the data. And third, the interviews of the other two case studies were very consistent. In addition, this procedure was ‘as good as it gets’ because in many firms only the CEO was knowledgeable about the alliances, their integration into the corporate strategy, and the processes how the portfolio changed its structure.

The initial interviews usually lasted 2-3 hours and were structured in three sections. The first part covered the general development of the case study firm to understand how the organizational characteristics and the resource requirements changed over time. The second part covered structural aspects of the alliance portfolio enclosing the questions such as with whom, why, when, how intensive. The last part analyzed the alliance process to understand how alliances are formed and managed. Every section started with open question to understand the general settings. The core elements in each section were covered a second time with closed questions to assure that these aspects are enclosed in all case studies and thus comparable. In these closed section the interviewees had to clearly specify the development steps of their firm in terms of date, their characteristics, and resource requirements. The dimension to evaluate the organizational characteristics reached from the organizational structure, over the management focus, the communication style, and the flexibility of strategy, to the compensation and reward systems. These dimensions have been selected by referring to the current practice in organizational theory (i.e., Block, et al., 1985;

Alliance database structure

Case study firm Name

Industry segment Foundation date Country Address Domain

Partners Name Industry Company type

1 : n Partnership

Formation date Termination date Motivation Governance stru-cture

Intensity stage 1 : Intensity stage x 1 : 1

Greiner, 1972; Kazanjian, et al., 1989). The seven resource categories, for which requirements were collected, comprised technological know-how, reputation, access to markets and to supply, financial and human resources as well as organizational skills. The selected categories have also been used in other research studies focusing on company resources (i.e., Barney, 1991). To indicate the organizational characteristics and resource requirements four and five point scales were used. The detailed definitions of every category and the scales to measure these categories are provided on the basis of the first case study in section 3.2.

Collection alliance data was an iterative process based on archival data and interviews.

Archival data has been used to gather information on the partners and the dyadic relationships. Partners are characterized by their name, the industry they belong to, and their type (start-up, mid-cap, large firms). Foundation date, and contingently termination date, motivation, the governance structure, and the intensity over time describe partnerships.

Industry reports, company home pages, and business databases as Factiva were used as data sources. The relevant information was stored in a database, which structure is depicted in figure 5.

Figure 5 Alliance database structure

Database reports were used to discuss the alliance portfolios with the case studies’

management teams, who confirmed or completed the data during the interviews and made changes, where needed. After the interviews, the database was updated. For comprehensibility reasons, the documentation of the networks is explained on the basis of the first case study.

The interviews were taped and fully transcribed and are attached in the appendix. This procedure of full transcription is imperative for reasons of internal validity and reliability. In their authoritative work on the methods of data collection, Bortz and Döring (1995) state:

‘If an interview also contains open questions and narrative parts, an audio recording is unavoidable.’ (Bortz, et al., 1995, p. 230, 231)⁷

All transcripts are included as part of the case study database. Similar to the well-established Harvard Business School case research approach, all interviewees were granted anonymity, in that nothing they said was attributed to them personally until and unless they approved of the transcript (Leonard-Barton, 1990).⁸