Defining the model

To underpin the final model and hypotheses the elements in the model and the consequences

of the moderating or mediating effects should be analysed. A moderator is a qualitative or a

quantitative variable that affects the direction and or strength of the relation between an

independent and a dependent variable (Baron & Kenny, 1986). Further, a given variable may

be said to function as a mediator to the extent that it accounts for the relation between the

independent and the dependent variable (Baron & Kenny, 1986). While moderators certify

when certain effects will hold, mediators explain why and how such effects occur.

Below detailed criteria of moderators and mediators are presented.

3.6.1. Mediators or moderators?

The regression equation with the three moderating effects

The general regression equation with one independent variable and t control variables is:

yi = αi x1 + ϒi c1 + ϒ2 c2 +...+ ϒt ct

where yi is the dependent variable,

x1 i s the independent variable,

c1 .. ct are control variables

αi is the regression coefficient and

ϒi is the regression coefficient of the control variables

i is defined for control, service and strategy task (respectively)

With the three moderating effects the moderators are included in the regression equation:

yi = α1i x1 + α2i m1 + + α2i m2 + α2i m3 + β1i x1m1 + β2i x1m2 + β3i x1m3 +ϒi c1 + ϒ2 c2 +...+ ϒ8 c8

where i = control, advisory, strategic m1 is the exploratory learning moderator

m2 is the transformative learning moderator

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and α, β and ϒ are the regression coefficients

which defines the moderators having a direct and an indirect impact on the dependent variables.

By a transformation we further find that

yi = (α1i + β1i m1 + β2i m2 + β3i m3)x1 + α2i m1 + + α2i m2 + α2i m3 + ϒi c1 + ϒ2 c2 +...+ ϒ8 c8, (α1i + β1i m1 + β2i m2 + β3i m3) defines the total regression coefficient of x1 on yi,

where α is the direct regression coefficient and βji mj is the moderating regression coefficient for j = 1..3

By calculating the expressions zj =mj x1 for j = 1..3, the equation can be written as:

yi = α1i x1 + α2i m1 + + α2i m2 + α2i m3 + β1i z1+ β2i z2+ β3i z3+ϒi c1 + ϒ2 c2 +...+ ϒ8 c8

This equation is similar to the general regression equation, and the values are defined in the

same way. The coefficients can thus be calculated by a normal multiple regression analysis.

SEM analysis can be used as well, but the results will be the same as if the analysis is

conducted by multiple regressions in SPSS.

When a mediating effect is analysed, we have not got any similar way of writing expressions.

The consequence is that mediation cannot be measured exactly, but the tendency of mediation

can be tested.

A mediation model is a causal model. In other words, it means that the mediator variable has

been assumed to cause the effect in the dependent variable and not vice versa. As reported

above, the mediation caused by the mediator variable cannot be defined statistically. On the

contrary, statistics can be utilised to assess an assumed meditational model developed by the

mediator variable (Baron & Kenny, 1986). The mediator variable explains the relationship

between the dependent variable and the independent variable. When complete mediation

appears the independent variable no longer affects the dependent variable. The process of

partial mediation by the mediator variable is defined as a reduced, but still existing effect of

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3.6.2 The model

The causal model was defined in section 2.8. The research question for the quantitative study

is related to the impact of absorptive capacity on board task performance, and mediation will

be tested. Several other models were considered before this model was derived. Originally, I

found several options of suitable models for testing a possible connection between these

variables:

 Testing the direct impact of absorptive capacity on board task performance

 Testing the impact of absorptive capacity on board task performance with presence of knowledge and skills as a moderator or a mediator

 Testing the impact of presence of knowledge and skills on board task performance with absorptive capacity as a moderator or a mediator

While a moderation analysis is an exercise of external validity in that the question is how

universal is the causal effect between the antecedent and the consequences, a mediation test

analyses how (and by what means) an effect occurs. What accounts for the impact of the

antecedents (A) on the consequences (C)? It is hypothesized that A "causes" B and that B then

"causes" C. (Holmstrom, 2006).

Four conditions must be met for B to be a mediator:

1) A is significantly associated with C

2) A is significantly associated with B

3) B is significantly associated with C (after controlling for A)

4) The impact of A on C is significantly less after controlling for B (Baron & Kenny, 1986;

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Deciding which variables are the moderators or the mediators, will depend on the focus of the

research. In this context analysing absorptive capacity as mediator on the connection between

presence of knowledge and skills and board task performance will examine how absorptive

capacity strengthens or weakens the impact of the knowledge variable on board task

performance. The opposite opportunity will be analysing presence of knowledge and skills as

mediator on the impact of absorptive capacity on board task performance, explaining whether,

how, and to which degree, the presence of knowledge and skills has an impact on the

relationship between absorptive capacity and board task performance.

I will base the model on the former defined relationship between presence of knowledge and

skills and board task performance (Forbes & Milliken, 1999; Machold et al, 2011; Huse,

2007; Minichilli et al, 2009). Then the new contribution will be the analysis of whether, and if

so, how and to which degree, absorptive capacity has an impact on board task performance as

a mediator. I will thus test the mediated effect of presence of knowledge and skills on board

task performance with absorptive capacity (exploratory learning, transformative learning and

exploitative learning) as the mediator.

The model will be tested by the methods and analyses presented in this chapter and by

structural equation modelling which are reviewed in the next section.