Process-based case study

Besides the overall circumstances, this research gives specific attention to time and to the temporal context, focusing on how the studied phenomenon evolves over time. Given that relationships and networks are not fixed but continuously re-created over time, paying specific attention to time is highly important in the study of collective international opportunity recognition. Moreover, in such a study, it is important to acknowledge and connect events and processes at the individual, firm, and network levels in order to understand how processes are enacted over time in a network setting even though it is challenging to incorporate multiple actors’ views of processes and analyse them at different levels in a single study. (Halinen et al. 2012)

A longitudinal case study is about examining a case over an extended period of time in order to identify and explain patterns of change in its context (Pettigrew 1990) by analysing flows of events over time and by linking features of context and process to certain outcomes (Pettigrew 1997). More specifically, this research is conducted as a process-based study, meaning that it combines process data with process theorising, which is strongly recommended in the study of internationalisation processes; surprisingly, only a minority of the extant studies on them take a truly process-based approach (Welch & Paavilainen-Mäntymäki 2014). In contrast to variance approaches, which seek relationships between variables and aim at answering what-questions, a process approach seeks to trace how and why events unfold through events – through what kind of mechanisms different time points are connected and how and why the observed patterns occur (Dawson 2003; Welch & Paavilainen-Mäntymäki 2014). Process theories ask what the antecedents and consequences of an issue are and how the issue emerges, develops, and grows, or terminates, over time (Van de Ven 2007). ‘The past contains the seeds for the future’, whereby embeddedness and temporal interconnectedness are key features of process research (Mari & Meglio 2013, 208). Conducting a process study that truly embraces these features requires that the process be maintained under focus in the aim, data, analysis, and contribution of the study (Paavilainen-Mäntymäki & Welch 2013).

Process-based research is, however, not simple to conduct because of the required time commitment as well as the dominance of variance-based assumptions (Welch & Paavilainen-Mäntymäki 2014). In addition, the data provide challenges for the researcher due to their volume, ambiguity, and complexity, particularly when process research is conducted with an inductive approach, and what is relevant is often decided only along the process (Langley 1999). Given the complexity of longitudinal investigation, the majority of process studies are conducted with single-level analysis in management and organisation journals, mostly at the organisational level, while the network level of analysis is largely

unexplored (Mari & Meglio 2013). This may relate to the fact that conducting process research in a business network setting poses further challenges: the researcher needs to define the items on which data are collected in a multi-layered network setup, specify the time periods from which data are collected at the chosen levels, choose the case(s) in a way that the subjects of the studied phenomenon remain comparable over time, and eventually, build comparisons in the analysis of complex network data. All this is not straightforward as, in a business network, the actors and relationships change, whereby the informants and data sources may change, and hence, the data to be analysed can differ considerably from one period to another. (Halinen & Mainela 2013)

However, as process research is about understanding a changing phenomenon through the flow of events and activities, the change in the research process must be embraced and dealt with via methods that enable the illustration and explanation of change (Halinen & Mainela 2013). Such methods employed in this research include analysis tactics for narrative research in business networks (Makkonen et al. 2012) and temporal bracketing, whereby the longitudinal data can be split into successive, comparable periods, illustrating change (Langley 1999). At the same time, flexibility and a progressive focusing approach allowed me to react to and adjust the research according to the changes in the research setting and the evolving research phenomenon (Alfoldi & Hassett 2013). In addition, concerning the multi-level network analysis, processual researchers are often storytellers that combine the multiple informants’ multiple stories as, over time, managers may adjust their views by reinforcing or downplaying some elements to the detriment of others when making sense of a past event, for instance. For this reason, it is important not to reconcile discrepancy but to embrace and look deeper into controversies within and between the views of individuals (Dawson 2013), which was followed in this thesis. All in all, the complex process data reflect the complexities of the industrial phenomenon we are trying to understand (Langley 1999), allowing the generation of strong process theories (Welch & Paavilainen-Mäntymäki 2014).

The timing and duration of a process study should be based on the phenomenon of interest – the process can be a short one, and what matters is not the timespan but the presence of a cause, an effect, and mechanisms in between, whereby the phenomenon can be explained (Paavilainen-Mäntymäki & Welch 2013). Hence, the length of the fieldwork depends on the length of the studied process. Most process studies in management and organisation research last up to three years (Mari & Meglio 2013), and also in this research the investigation period comprises three years, from the beginning of 2015 to the end of 2017, during which I was able to see the collective opportunity recognition and the ceasing of it as well as the paths that followed. Consequently, the attempt of this thesis is to produce

process theory through process data and thereby enhance the understanding on how collective international opportunity recognition unfolds over time.