Medidas de percepción del Entorno del Centro

The next two subsections look at the methods employed in existing research, as well as the general approaches to researching organizational agility in the existing Infor-mation Systems literature.

Research approaches to organizational agility reflect the development of the area of Information Systems strategy research outlined above. There is a long debate in the social sciences on how to label and differentiate the two commonly used traditions of research. As discussed before, research on Information Systems strategy can be broadly separated into research taking a rationalist view of the world (usually applying a positivist epistemology) and research in the tradition the social sciences (usually applying an interpretivist epistemology). This distinction is often reflected in the kind of data collected: While positivist research generally uses quantitative data (e.g. gained through a large-scale survey and analysed using statistical methods), interpretivist research mostly uses qualitative data (e.g. gained through a case study and analysed using interpretive methods). Since authors are not always

explicit about their choice of epistemology, this thesis instead distinguishes the empirical papers based on their choice of data collection. There have been long and heated debates around which approach is more useful (e.g. Goertz & Mahoney 2012). Some consensus seems to be emerging that a less dogmatic view is called for.

King et al. (1994) even argue that the logic of inference is the same for both styles of research:

the differences between the quantitative and qualitative traditions are only stylistic and are methodologically and substantively unimportant. All good research can be understood – indeed, is best understood – to derive from the same underlying logic of inference. Both quantitative and qualitative re-search can be systematic and scientific. (p. 4 f.)

Whatever the case may be, it seems appropriate to appreciate the strengths of both styles of research and apply both, depending on which one is more useful for a specific research project. However, there are significant methodological questions connected to the choice of research style. These will be discussed in more detail in Chapter 4. For now, research on organizational agility following these two traditions is discussed and compared.

Much of existing Information Systems research on organizational agility is based on statistical analysis of quantitative data (see Table 2 for an overview). It applies the corresponding methods, e.g. surveys (Roberts & Grover 2012; Kharabe & Lyytinen 2012; Chen et al. 2013), regression analysis (Chakravarty et al. 2013) or quantitative field studies (Lu & Ramamurthy 2011; Fink & Neumann 2007). This implies a positivist stance and a search for a fixed truth. This is in line with the majority of Information Systems research in general: as Mingers (2004b) points out, statistical analysis in the positivist tradition remains “the dominant research method within IS”

(p. 97).

finite mixture theory quantitative regression analysis Chen et al. 2014 resource based view quantitative survey

Choi et al. 2010 system dynamics quantitative model, simulation Ciborra 1996 structuration (Giddens),

sensemaking (Weick)

qualitative case study

Paper Theories used Type of Huang et al. 2014 information processing qualitative case study Kharabe &

IT capability quantitative field survey Lyytinen & Rose

Ngai et al. 2011 RBV qualitative multi-case study

Richardson et al.

2014

digital options qualitative case study Roberts & Grover

organizational IT impact quantitative field survey Tallon &

Pinsonneault 2011

strategic IT alignment quantitative survey

Tallon 2007 RBV quantitative survey

van Oosterhout et al. 2006

previous research on agility mixed survey + qualitative data from interviews

Zheng et al. 2011 organizational performance, sensemaking, paradox

qualitative case study

Table 2 Research designs

Statistical analysis has enabled authors to propose a number of relationships around agility, including:

 “while more IT spending does not lead to greater agility, spending it in such a way as to enhance and foster IT capabilities does.” (Lu & Ramamurthy 2011, p.932)

 “alignment between customer-sensing capability and customer-responding capability will impact the firm’s competitive activity” (Roberts & Grover 2012, p.231)

 “positive and significant link between alignment and agility and between agility and firm performance. We also show that the effect of alignment on performance is fully mediated by agility, that environmental volatility posi-tively moderates the link between agility and firm performance, and that agility has a greater impact on firm performance in more volatile markets.”

(Tallon & Pinsonneault 2011, p.463)

Such research has produced useful findings. Singh et al. (2013) argue that “agility is best viewed as an organizational capacity to produce change along two dimensions that are posited to be typically in tension: (1) magnitude, and (2) rate of variety” (p.

3). This means it can be measured, which they do based on the dimensions given above, e.g. by looking at the release cycles and the amount of innovation (e.g. new features) in smartphones. The correlation, however, is not always this straight-forward: van Oosterhout et al. (2006) see IT as both a potential enabler and disabler of organizational agility, as legacy systems can get in the way of agility initiatives.

Kharabe & Lyytinen (2012) investigate whether ERP systems promote or hinder organizational agility, finding evidence in the literature for both. They find that ERP assimilation (i.e. the extent to which it gets taken up and diffused across the organization) positively influences organizational agility, and find that systems agility also positively influences organizational agility, as well as strengthening the impact of ERP assimilation on organizational agility. Chakravarty et al. (2013) contribute to a better understanding of how information technology competencies shape organizational agility and firm performance, arguing that they play both an enabling and a facilitating role. Similarly, Chen et al. (2013) point out that IT capability does not directly lead to better firm performance and stress the role of business processes and environmental factors.

Some researchers have taken issue with such rational approaches. Ciborra (2004) criticises them for assuming a “geometrical” universe based on the ideas of rational planning and building of (static) information systems to align with business strategy.

Such criticism does have a point: As an example, Roberts & Grover (2012) hypo-thesize a model, based on existing literature, to propose a number of relationships leading to increased customer agility and competitive activity. They then conduct a survey of marketing executives of US high-tech firms to test these hypotheses.

Respondents were asked how much they agreed with statements like “We sense our customers’ needs even before they are aware of them” (p. 263). Results from the survey are statistically analysed, with five out of six hypotheses supported by the findings (p. 252). Thus, their hypothesized relationships are empirically supported and can serve as a basis for future research. On the other hand, a question like “We sense our customers’ needs even before they are aware of them” will lead to answers that are affected by respondents’ personal views of the matter, and not as neutral as the approach makes them out to be. Thus, such statistical approaches may be less rational than they claim to be. Yet Roberts & Grover themselves also reflect on the limits of such rationality: They conceptualize agility as a dynamic capability, which, as discussed, implies the fact that managers continuously reconfigure the capabilities in an organization as they “satisfice rather than optimize in searching for and selecting solutions to problems” (p. 237).