Digital infrastructures as a lens

6.6.1 Context

In the next iteration of the analysis, there was a focus on how people contribute to the digital infrastructures in Telco. An insight was developed from analysing the

infrastructural nature of some of the information systems presented in the case study:

Digital infrastructures should be understood as a lens to look at information systems in organizations. Thus, in this phase of the research, the view of digital infrastruc-tures shifted from seeing them as a tool (as implied by the initial research question,

“How can digital infrastructures support performances of agility in organizations?”) to seeing them as a lens through which to look at existing IT in organizations. The latter view is reflected in the mechanism of infrastructuralization here, defined as interpreting the information systems in organizations as digital infrastructures. This was inspired by the tradition of interpretivist Information Systems research (e.g.

Ciborra 1996; Walsham 2006), and especially Weick's (1995) concept of sense-making. Thus, people contribute to agility in these digital infrastructures both by engaging with the grown IT in Telco and shaping it and by interpreting it as digital infrastructures.

6.6.2 Retroduction

This view takes up the notion of sensemaking (Weick 1995) that was identified as a factor of collective agility by Zheng et al. (2011). As Weick puts it, “sensemaking is about the ways people generate what they interpret” (p. 13). The concepts of “infor-mation systems” and “digital infrastructures” are here seen as different ways of looking at the same phenomena. The traditional view of systems inside organizations sees them as more static and constrained, as represented by the traditional term

‘information systems’, which defines systems by their performative functions. The infrastructuralization view advocated here takes a more modular, service based, open perspective on a similar phenomenon, reflecting the change in systems today where-by they are more infrastructural. This thesis argues that organizations commonly focus on the former view, but they would benefit from adopting the latter.

The case study has presented several instances where employees in Telco engaged in such sensemaking activities. For example, there were several incidents when inter-viewees spoke about the way their IT has historically grown (e.g. “it’s just bits added on as it goes”, i17). Moreover, it is argued here that the changes brought about in the cases of OfferMaker and SalesTool were only possible because employees inter-preted their information systems as digital infrastructures as this enabled them to see them as grown, evolving systems open to such modifications. This enabled them to

engage in the creation of these tools, which were relatively minor additions to the grown infrastructures that nevertheless significantly supported agility. This act of sensemaking enabled employees of Telco to solve the issues around the lack of agility presented in these two cases. At the same time, the changes made to the systems were relatively small and did not endanger the day-to-day functioning of these systems.

Developing further the focus on the interpretations performed by the users of an information system, a mechanism called agilization was proposed. This relates to the act of making an organization more agile by cultivating digital infrastructures and minding flows of information to attain an appropriate level of agility for the given situation. Thus, it takes up elements of the mechanisms of informatization and infra-structuralization as well as the notion of bounded agility. The concept of agilization stresses the performative nature of organizational agility and highlights the aspect of sensemaking, the choice by people in the organization to make it more agile. It also takes up the idea of agility as a performance by the users of an information system, as formulated by Zheng et al. (2011). Agilization can include many activities, but in this case, the focus was on the interactions with digital infrastructures, which consti-tute an important part of these activities. As digital infrastructures are here concep-tualized as both enabling and constraining change, it becomes clear that successful agilization involves engaging with, and harnessing, the digital infrastructures the right way. This is illustrated next and summarized in Table 16 (p. 159).

The term ‘cultivation’ goes back to Ciborra (1997) and has recently been used by Grisot et al. (2014) to describe the development of an information infrastructure:

A cultivation approach acknowledges the existence of the installed base, and it seeks to address change in an incremental and gradual manner. (…) Overall, three main aspects can be said to characterize a cultivation strategy:

process-orientation, user mobilization, and learning. (p. 200)

In this case study, aspects of cultivating digital infrastructures were evident in all units of analysis: for OfferMaker and SalesTool, this relates to the modifying of the historically grown infrastructures. In the case of Analytics, existing systems were interpreted as resources for the new analytics infrastructure. Also a Hadoop database was created to enable real-time analysis of business information. A central goal of

these initiatives in all examples was to improve flows of information. This was illus-trated using the examples of faster, easier offer creation (OfferMaker), a new, simple interface enabling faster order processing (SalesTool) and the analysis of transac-tional data to present information on it in real time (Analytics).

The aspect of attaining an appropriate level of agility for the given situation was also present in each unit of analysis: for OfferMaker, the tool was perceived as increasing agility in the order creation process. Nevertheless, there were limitations due to the grown infrastructure (for example, it does not work for all products yet). Also, some employees were concerned that with the new tool, there may be too many offers now (i24). In the case of SalesTool, there is some evidence that it has accelerated order processing. Partly, this was achieved by presenting limited options to sales agents (thus making it harder to break anything or make mistakes). This tool was also con-strained by the historically grown infrastructure, as for example it still needs to co-exist with the old OneView tool. In the case of Analytics, the initiatives were seen as increasing agility as they enabled more informed business decisions based on infor-mation that would not have been available earlier. This perceived agility was again limited by constraints like concerns around privacy and data security as well as regulatory issues (i22) or organizational concerns (i37).

OfferMaker SalesTool Analytics

Thus, the mechanism of agilization illustrates how, by cultivating digital infrastruc-tures and minding flows of information, companies can attain an appropriate level of agility for a given situation. Referring back to the terms of the conceptual frame-work, agilization contributes to the creation of change with perceived economy, quality and/ or simplicity.

6.6.3 Comparison

The mechanism of infrastructuralization is useful as it illustrates how existing infor-mation systems can be interpreted as digital infrastructures, which can then lead to developing them in a way that strengthens infrastructural qualities like modularity and generativity. Either way, this mechanism has higher explanatory power than the ones discussed above, as it is broader and relates to more general situations. Like-wise, the mechanism of agilization is useful as it combines the notions of digital infrastructures and organizational agility, thus illustrating the relevance of digital

infrastructures for agility. Together with informatization (discussed above), these mechanisms were selected as the key elements of the explanatory framework.

6.6.4 Summary

The proposed mechanisms that were presented and compared regarding their expla-natory power are summarized in Table 17. Details of the ‘comparison’ stage of the analysis (summarized here as Benefits, Limitations, and Explanatory power) were discussed in the “Comparison” subsections in this chapter (e.g. 6.3.3).

Mechanism Definition Benefits Limitations Explanatory power

Mechanism Definition Benefits Limitations Explanatory

Out of these candidate mechanisms, agilization, infrastructuralization and informati-zation were found to have the highest explanatory power. They make up the explana-tory framework proposed here. This is summarized in Figure 9 above (p. 136) and will be elaborated next.