CONCEPTO DE INTERPRETACION JURÌDICA

Van de Ven and Poole (2005) distinguish between two epistemological approaches that are typically used in organisational studies to tackle change and innovation: variance and process theories (Van de Ven & Poole, 1995, 2005), which have been briefly introduced in the theoretical background.

In the first, the so called variance theory methodology, change is defined as a dependent variable, and can be explained statistically in relation to a series of independent variables (Poole, Van de Ven, Dooley, & Holmes, 2000). The focus of variance methods is typically on the variables that represent the most important aspects or attributes of the subject under investigation. Explanations are presented in form of causal statements or models, and incorporate such variables. In fact, an implicit goal of variance research is to establish the necessary conditions to bring about an outcome (Van de Ven & Poole, 2005). In summary, variance approaches offer good explanations of continuous change. This continuous change is driven by deterministic causation, i.e., by an identified cause-effect relation. Yet, this does not allow to fully conceptualise change as it overlooks many critical aspects of change processes, such as contextual influence and multiple time scales in the sub-processes (Langley et al., 2013).

Process theories, on the other hand, are narratives that describe a sequence of events on how development and change unfold. From the process perspective, change occurs as a story or historical narrative develops. Therefore, the unfolding of change is tackled by narrating the temporal sequence of events that unfold in an organisational environment (Langley, 1999;

Pettigrew, 1997; Poole et al., 2000). The flow of time is considered irreversible, and temporal succession actually treated as a developmental process (Van de Ven & Poole, 1990, 2005). Process methods, therefore, should allow capturing a higher degree of complexity as compared to variance approaches. In fact, process methods incorporate various types of effect into their explanations, which include: (1) critical events and turning point; (2) contextual influence; and (3) formative patterns that give the overall direction to change. It also involves causal factors, which influence the sequencing of events (Langley et al., 2013; Van de Ven & Poole, 2005). Langley (1999), for instance, stresses how, to produce reliable research, process studies require methods that can (1) identify and test temporal linkages between events and overall temporal patterns; and (2) cope with the multiple time scales that often occur in processes. To achieve such results, process methods are very labour-intensive and typically involve the collection of large amount of multi-faceted data (Langley, 1999; Pettigrew, 1997). By combining the two ontologies presented above with these two epistemological approaches, Van de Ven and Poole (2005) propose a typology of research approaches for the investigation of change and innovation within organisational studies (Table 9).

Table 9: Typology of research approaches for organisational change and innovation (Adapted from Van de Ven and Poole, 2005).

Ontology A noun, a social actor, a real entity

(“thing”)

A verb, a process of organising, emergent flux

Epistemology

Variance method

Approach I

Variance studies of change in

organisational entities by causal analysis of independent variables that explain change in entity (dependent variable).

Approach IV

Variance studies of organising by dynamics modelling of agent-based models or chaotic complex adaptive systems.

Process narratives

Approach II

Process studies of change in

organisational entities narrating sequence of events, stages or cycles of change in the development of an entity.

Approach III

Process studies of organising by narrating emergent actions and activities by which collective endeavours unfold.

As mentioned above this dissertation is grounded in the first ontology, and defines organisations as identifiable entities. In addition, it adopts both the variance method and the process narratives in different papers. I applied Approach I in paper 3, and combined the first ontology with a variance methodology. In papers 1, 2, 4 and 5 I followed Approach II, and adopted a process approach. On one hand, Approach I emphasises the study of change in organisational entities with a variance methodology. It thus offers a good picture of the cause-effect mechanisms that are behind a process. In paper 3, Approach I resulted as the most appropriate to outline the co- dependency between innovation in services and ICT, as the study was centred on the investigation of the cause-effect relationship between the two constructs. On the other hand, Approach II

conceptualises change as a succession of events, stages, cycles or states in the development or growth of an organisation. Within Approach II, scholars study how change unfolds in organisational entities. This is what I do in papers 1, 2, 4 and 5. More specifically, I use Approach II to identify coherent periods of activities through which processes unfold (Van de Ven & Poole, 2005). In this perspective, time is divisible and differentiated. This means that time is dependent on its observer(s), and that critical events are determined by what the observers themselves notice as significant (i.e., transactional view of time). I therefore measured time by identifying events that are critical or significant to the subjects, who were involved in the processes under investigation (Van de Ven & Poole, 2005, p. 1390).

The critical incident technique

To incorporate such transactional view of time in my empirical work, I applied the critical incident technique (Flanagan, 1954), as visualised in Figure 7 on page 67. The critical incident technique consists of a flexible set of principles that was developed in the mid-20th century by psychologists, for the main purpose of job analysis. Originally, the critical incident technique was meant as a tool to create a functional description of an activity, by identifying the aim or objective of such activity before any other aspect of it (Butterfield, Borgen, Amundson, & Maglio, 2005; Flanagan, 1954). Following its application to various disciplines, the critical incident technique was classified as a qualitative research approach that is characterised by:

1. The focus on critical events, incidents or factors that characterise a specific situation or event in the eye of the observers;

2. The data collection primarily from interviews;

3. The data analysis conducted by determining the frame of reference, forming categories that emerge from the data, and determining the specificity or generality of the categories; 4. The narrative form of categories with operational definition and self-descriptive titles

(Butterfield et al., 2005).

The critical incident technique has recently been used in a variety of service contexts to explore service research issues, and scholars have proved its reliability as method to be applied in research within the service context (Gremler, 2004). Researchers have used the critical incident technique primarily in business-to-consumer contexts. Nevertheless, the characteristics of such method make it appropriate, and have been proven successful, for use in a broader range of issues, including the cross-organisational business-to-business context investigated here (Butterfield et al., 2005; Gremler, 2004).

At the same time, the critical incident technique embeds some inherent weaknesses that are worth mentioning. First of all, respondents have limited and varied ability to recall historical events. This implies that the collected data might be heterogeneous and that day-to-day activities

might be overlooked. In this dissertation, informants typically focused on critical incidents that had occurred during the last six months before the interview (Ahola, 2009; Flanagan, 1954; Yin, 2009). In addition, the interview guides included questions that touched upon, and explicitly asked examples of, critical examples of day-to-day activities. When the interview touched upon events that took place more than six months prior to the interview, archive data mining complemented interview data. This was carried out, for instance, in the longitudinal in-depth study for paper 2. In this study, the data collection covered a time period of over 8 years, which made it necessary to integrate interviews with archive data. Secondly, individuals might be reluctant to discuss events that happened in the past. The risk increases if the respondents were themselves responsible of negative outcomes or if they personally or professionally experienced the incident as negative (Ahola, 2009; Flanagan, 1954; Yin, 2009). Thirdly, the importance of critical incidents is relative and thus hard to evaluate objectively (Ahola, 2009). Archive data mining and the combination of interviews with more individuals on the same events were implemented here to strengthen the quality of data collected for this dissertation. Finally, the inherent flexibility of the technique might cause lack of methodological rigor and inconsistent findings. To ensure reliability of the application of the critical incident technique to my empirical work, I rigorously followed the recommendations by Butterfield et al. (2005) while collecting and analysing data, and built on previous studies that were based on the critical incident technique (such as, e.g., Ahola, 2009; Butterfield, Borgen, Amundson, & Erlebach, 2010; Gremler & Gwinner, 2008; Specht, Fichtel, & Meyer, 2007).

I explicitly asked respondents to elaborate on the issues that arose during the interviews, with emphasis on those events that made a significant, either positive or negative, contribution to the activities or phenomena that we were discussing (Butterfield et al., 2010; Gremler, 2004; Specht et al., 2007). More specifically, once a respondent mentioned an event that he or she identified as critical, four elements were discussed and documented:

1. Time of the critical incident, i.e., when the incident occurred; 2. Description of the critical incident, i.e., what happened;

3. Cause for the critical incident, i.e., what were the reasons behind the occurrence of the incident;

4. Results of the critical incident, i.e., what was the outcome of the incident (Ahola, 2009, p. 88).

I then coded and interpreted the critical events that resulted from the data collection throughout the analysis by classifying them into concepts, categories and links thereof. The purpose of such analysis was to understand how concepts and categories, i.e., the abstraction of the examples that were raised by respondents, were related to each other and how the progression of events

visualises the process, based on the critical incident technique, through which I extracted findings from the data. Please notice that the process in the figure is linear only to ease representation and support a clearer understanding on the application of the critical incident technique. As discussed below (in the Research design sub-section), data collection and analysis were in fact overlapping and iterative.

Figure 7: The research process and the application of the critical incident technique.

For instance, the phases and steps of the business model innovation processes in paper 1 were identified on the basis of the critical incidents that interviewees pointed out in relation to the business model development under investigation. Similarly, I used the critical incident technique in paper 2. Here, the purpose was to analyse the longitudinal, in-depth case study—on which the study is built upon—as an embedded case study. This was achieved by extracting from the data those critical incidents that could be associated to innovation processes as defined by the theory.