MATERIALES Y MÉTODOS 1. Pruebas desarrolladas en México

The implementation of information technology and its impact towards organisational change has been an important phenomenon for discussion in IS literature over the last 30 years (Markus and Robey, 1988). The two most commonly used definitions of change encapsulate its modes of explanation (Van de Ven and Poole, 2005). With the definition of change as “an observed difference over time in an organisational entity on selected dimensions” (Poole et al., 2000) this correlates with the variance theory. Where as, “a narrative describing a sequence of events on how development and change unfold” (Poole et al, 2000) highlights the notion of process theory. The development of both variance and process theories was mainly for explaining the emergence of this complex phenomenon, especially in organisational change studies (Van de Ven and Poole, 2005; Poole, 2000). It is to establish the logical argument (Markus and Robey, 1988) through distinct modes of explanation (Mohr, 1982).

In variance studies, “precursor”, “antecedent” or “independent” variables are identified and causally linked with measures of outcomes (dependent variables) (Sabherwal and Robey, 1995; Mohr, 1982). In this type of research, change is represented as dependent variables (Van de Ven and Poole, 2005). In process studies, rather than consider the effect of variables, they focus on events that occur over time and attempt to explain how and why these events occur and how they affect the outcomes (Mohr, 1982; Sabherwal and Robey, 1995). In this type of study, change events occur based on a story or historical narrative (Pentland, 1999; Van de Ven and Poole, 2005). Variance and process theory have always been debated. Their function as an explanatory theory of human behaviour is always being challenged. According to Mohr (1982), although there is a prominent use of variance theory within the organisational studies, especially for its power of prediction and control, it does not dominate theory in practice. I would suggest that their differing methods of viewing and analysing the data contribute to fuelling the debate. With variance theory, the inclination is to view static relationships between

variables, whereas process theory suggests a more diachronic nature of events (Mohr, 1982).

Variance Theory Process Theory Definition The cause is necessary and

sufficient for the outcome

Causation consists of necessary conditions in sequence; chance and random events play a role Assumption Outcome(s) will invariably

occur when necessary and sufficient condition are present

Outcomes may not occur (even when conditions are present) Basis of explanation The basis of explanation is

efficient causality.

The basis of explanation is final, formal and path dependent.

Elements A variance theory deals with variables.

A process theory deals with discrete states and events. (discrete outcomes)

Role of time Snapshots, cross sectional and static

Longitudinal and dynamic Generalisation Depends on uniformity across

contexts. Statistical.

Depends on versatility across cases

Time-ordering (sequence)

Immaterial to outcome Critical to outcome. Path dependency.

Table 7: Characteristics of variance theory and process theory (Mohr, 1982; Markus and Robey, 1988; Poole et al., 2000)

There are several main differences between variance and process theory. The main difference is the association between inputs and outputs or in other words, the precursor and outcomes respectively. In variance theory it is agreed that the precursor is a necessary and sufficient condition for the outcomes (Mohr, 1982) where in a process theory the precursor is a necessary condition for the outcomes. While both of the associations engage with understanding how outcomes are achieved, variance theory incorporates variables while process theory accommodates necessary conditions (Mohr, 1982). It is where outcomes can be understood from the information on the process or the sequence of events that occur rather than prediction of variance (Markus and Robey, 1988).

The following differences relate to efficient cause and rearrangement of elements (probabilistic processes). Efficient cause is the heart of the variance theory identifying that “the force that makes it what it is or change it from what it was” (Mohr, 1982).

This notion of causality creates an understanding of the association between the necessary and sufficiency of precursors to produce outcomes of an explanatory theory (Mohr, 1982). Within process theory, the rearrangement of elements (necessary conditions or objects) to achieve outcomes is empowered as its explanatory power. Rearrangement refers to the joining or separation of two or more specified elements (Mohr, 1982). The joining or separation of the elements constitutes a probabilistic process. These combinations are to some degree affected by the external forces or the context. The notion of probabilistic processes refers to the path of events which are subject to the probability of the outcomes (Shaw and Jarvenpaa, 1997).

The final difference between variance and process theory is the issue on time-ordering. The nature of variance theory is to focus only on snapshots of event or a specific state of event rejects time ordering (Mohr, 1982). The idea of prediction and testing at times requires certain variables to remain constant which rejects time ordering (Mohr, 1982). Process theory supports the ordering of time where events or activities (joining or separation of elements) that occurs happen after one another.

Why should we engage in process study? Past research indicates that most studies conducted follows variance theory (sometimes referred to as a factor study approach) that shows the relationship between variables and looks at the degree of interaction between critical factors with outcomes. For example, variance studies look at the impact of ERP systems on the outcome of system implementation through surveys. This type of research is not able to address the nature and complexities of the change process. In contrast, process studies provide an in-depth analysis of events within a specific context. Markus and Robey (1988) further identify the benefits of process theory. Process theories make identification of new patterns within empirical data possible. The identification of the events, their paths and their sequences permits pattern generation. Also, the prediction of these patterns over time is also one of the goals of process theories (Markus and Robey, 1988). The relevance of process theories towards real life or actual events makes prediction of patterns applicable.

Mohr (1982) identifies attempts to combine both models in explaining organisational behaviour. Mohr (1982) also suggests co-existence rather than combination, which is supported by Newman and Robey (1992). They agreed that variance and process theory are mutually informative but not suitable for integration (Mohr, 1982; Newman and Robey, 1992). According to Newman and Robey (1992), a factor study and a process study are complementary where findings from each study can be further elaborated through other research. This complementary feature of the variance and process study was captured and further elaborated on by Sabherwal and Robey (1995) in their attempts to reconcile both types of studies. In their paper, they discuss the feasibility for reconciliation, method of reconciliation and the benefits of such reconciliation (Sabherwal and Robey, 1995).