Análisis de Porter para la industria en la que se desarrolla la empresa o

Dependent variable 1 (DV1) is the perception of securitization. Initially, a four-item scale was constructed to operationalize the variable. Principal component analysis (see Table 1) defined three of the four items loaded as highly correlated components of one factor:

DV1a: The story talks about a very important problem (Factor 1 loading of 0.843).

DV1b: The situation represents a real threat to the security in the area (Factor 1 loading of 0.618). DV1d: The story described a problem so serious that it needs a very special measures to solve it (Factor 1 loading of 0.834).

These three items were added to calculate the value of DV1.

One of the initially considered items, DV1c, was not incorporated into the final measures for DV1 because in comparison to other three items its component loading was low (Factor 1 loading of 0.052). Thus, this item was less correlated with the other items of the scale, which meant that it was not as appropriate as the other three included for the determination of the final variable.

The computation of the final DV1 was initiated by following the formula applied to each different exposure: DV1a + DV1b + DV1d. Then, the final values of two specific types of exposure, such as PP were added (eg., DV1.PPexposure1 + DV1.PPexposure2). The items were measured on a 7-point Likert scale. Thus, the final measure of DV 1 ranged on a scale from 6 to 42, with 6 representing lowest perception of securitization and 42 representing highest perception of securitization.

Dependent variable 2 (DV2) is the perception of complexity of the problem. A three-item– scale was conceived in order to address this variable. Principal component analysis (see Table 1) showed that these three items loaded as correlated components of the second detected factor: DV2a: The problem described in the story is very complicated (Factor 2 loading of 0.333). DV2b: I think that the news story doesn’t provide a full picture of the problem it describes (Factor 2 loading of 0.824).

DV2c: I need more information to form an opinion about this issue (Factor 2 loading of 0.832). While the item DV2a did not show such a high correlation value in comparison to DV2b and DV2c, it still showed a correlation that might not be wholly ignored. Thus, all the three items were included in the calculation of the final DV2. As all the three items were added, (DV2a + DV2b + DV2c) and each specific type of exposure was added (eg., DV2.PPexposure1 + DV2.PPexposure2). The original items were measured on a 7-point Likert scale. Therefore, the final scale for DV2 ranged from 6 to 42, with 6 corresponding to the lowest possible perception of complexity of the problem and 42 corresponding to highest possible perception of complexity of the problem.

Dependent Variables 3

Dependent variable 3 (DV3) is composed of three specific sub-variables corresponding to the three selected from the spectrum of the fundamental emotions, which are hope, anger, and fear. In previous studies, these variables were operationalized with just one item each. Therefore, for replication purposes, this study also employs just one item to correspond to each of the variables. A 7-point Likert scale was applied to determine a self-report of the subjects’ emotional state

For each of the DV3 sub-variables, the final value was calculated by adding the reported values for each of the two exposures of the same type. For instance, to reach the value of DV3(fear)

for PP, the following formula was applied: DV3(fear)PP.exposure1 + DV3(fear)PP.exposure2. Therefore, for each of the final values of the fundamental emotions of DV3 sub-variables, the scales ranged from 2 to 14, with 2 corresponding to lowest possible level of that specific emotion, and 14 corresponding to the highest possible level of that specific emotion.

Dependent Variable 4

Dependent variable 4 (DV4) is the perceived value of the user-generated comments. The variable is centered on negative evaluation. The first item, DV4a, represents positive evaluation of comments, and hence, its scale mathematic values are considered negative. The other two items, DV4b and DV4c, represent negative evaluation of comments, and hence, their scale mathematic values are considered positive. Indeed, principal component analysis (see Table 1) revealed that the items were loaded as correlated on the third factor, with specific directions of the correlations as originally envisioned:

DV4a: Some readers’ comments include important information on the story (Factor 3 loading of - 0.585).

DV4b: Readers should not comment on stories of this type (Factor 3 loading of 0.704).

DV4c: I would rather read opinions of experts than opinions of the public on a story like this (Factor 3 loading of 0.664).

The items loaded at correlations that comprise a justifiable factor, and thus, all three items are considered in the construction of DV4.

The required addition towards the DV4 was attentive to the direction of the items. Hence, the flowing formula was employed: DV4 = -DV4a + DV4b + DV4c. Similarly, as with the variables DV1 and DV2, the final version of DV4 was constructed by simple addition of the exposures of a specific type (eg., DV4.PWexposure1 + DV4.PWexposure2). Each individual

item’s numeric value was determined through a 7-point Likert scale. Considering the employed formula, the possible final values for DV4 might range from -10 to 26. The scale point -10 represents the lowest possible level of negative evaluation of the user-generated comments. The scale point 26 represent the highest possible negative evaluation of the comments. In other words, the higher the scale value, the more negative are the user-generated comments evaluated.

IV. Hypothesis Testing