gender
To test H1 and H2, I conducted several hierarchical moderated multiple regression or MMR (e.g., Aguinis, 2004) to predict value creation and value claiming in negotiations (continuous DVs) based on the use of positive or negative emotion words (continuous IVs) moderated by gender (categorical moderator). To avoid potentially problematic high multicollinearity with the interaction term, I centered the variables on positive, negative, anxious, angry and sad emotion languages (Aiken, West & Reno, 1991). I did not center the gender variable since it is already dummy coded (0 = Male, 1 = Female). I computed the interaction terms between gender and emotion words, and entered the two IV predictors (ex: Step 1: Gender and Positive emotion language) and then the interaction term (ex: Step 2: Gender x Positive emotion language) into the regression model.
After running the MMR to test for the influence of positive emotions on value claiming moderated by gender (H1a), the regression returned a non- significant equation (F(3,190) = .930, p = .427) with an R2 of .014. Participants predicted value claiming was equal to 3798.049 – 383.049 (Gender) + 66.443 (Positive emotion language) + 124.856 (Gender x Positive emotion language). Gender coding was 0 = Male, 1 = Female, and the measure for positive emotion language was the percentage of positive words within the total words used. None of the three variables was a significant predictor of value claiming in negotiations: gender (b = -383.049, SEb = 261.807, β = -.107, p = .145), positive emotion
SMU LKCSB PhD in Business (General Management) PhD Dissertation
language (b = 66.443, SEb = 135.885, β = -.043, p = .625) or their interaction term (b = 124.856, SEb = 241.949, β = .046, p = .6068). Hence, I cannot support H1a, as there is not enough evidence to support that positive emotion expressions predict value claiming for women more so than for men.
Figure 2. Interaction plot for positive emotion language and gender (Male = 0, Female = 1) on value claiming in negotiation.
When running the MMR to test for the impact of positive emotions on value creation moderated by gender (H1b), the regression returned a significant equation (F(3,190) = 3.068, p = .029) with an R2 of .046. Participants predicted value creation was equal to 7685.011 – 613.260 (Gender) + 106.162 (Positive emotion language) + 30.488 (Gender x Positive emotion language). Gender coding was 0 = Male, 1 = Female, and the measure for positive emotion language was the percentage of positive words within the total words used. Gender was found to be a significant (b =-613.260, SEb = 210.807, β = -.210, p =.004) predictor of value creation in negotiations, but positive emotion language (b = 106.162, SEb =
SMU LKCSB PhD in Business (General Management) PhD Dissertation
language) (b = 30.488, SEb = 194.817, β = .014, p =.876) were not significant. As such, I cannot support H1b either as there is not enough evidence to support that positive emotion expressions predict value creation for women more so than for men.
Figure 3. Interaction plot for positive emotion language and gender (Male = 0, Female = 1) on value creation in negotiation.
When running the MMR to test for the impact of negative emotions on value creation moderated by gender (H2b), the regression returned a significant equation (F(3,190) = 3.360, p = .020), with an R2 of .050. Participants predicted value creation was equal to 8263.851– 616.621 (Gender) – 194.821 (Negative emotion language) – 986.357 (Gender x Negative emotion language). Gender coding was 0 = Male, 1 = Female, and the measure for negative emotion language was the percentage of negative words within the total words used. Gender was found to be a significant (b = -616.621, SEb = 208.536, β = -.211, p =.004) predictor of value creation in negotiations such that the main effect between gender and value creation suggests that men create more value than women. However negative emotion
SMU LKCSB PhD in Business (General Management) PhD Dissertation
language (b = -194.821, SEb = 448.151, β = -.036, p = .664) and their interaction term (Gender x negative emotion language) (b = -986.357, SEb = 896.890, β = - .091, p =.273) were not significant. Hence, I cannot support H2b, as there is not enough evidence to support that negative emotion expressions predict value creation for women more so than for men.
Figure 4. Interaction plot for negative emotion language and gender (Male = 0, Female = 1) on value creation in negotiation.
To further understand this significant interaction and because our affective data observation method (LIWC2015) allowed us to break down negative emotions into three separate emotions (anger, anxiety, and sadness), I ran separate MMRs on the displays of specific negative emotions and value creation. I found that the interaction terms between gender and anxious emotion language (b = -3574.667, SEb = 2059.702, β = -.151, p =.084), gender and angry emotion language (b = - 2493.601, SEb = 2728.439, β = -.075, p =.362), and gender and sad emotion language (b = -924.204, SEb = 1461.065, β = -.055, p =-.633) were not significant
SMU LKCSB PhD in Business (General Management) PhD Dissertation
predictors of value claiming. I cannot support the argument that men create more value than women based on their use of anxious, angry or sad emotion languages.
I also ran the MMR to test for the impact of negative emotions on value claiming moderated by gender (H2a), the regression returned a non-significant equation (F(3,190) = 2.020, p = .086), with an R2 of .031. Participants predicted value claiming was equal to 3785.601 – 388.627 (Gender) + 539.245 (Negative emotion language) – 2318.936 (Gender x Negative emotion language). Gender coding was 0 = Male, 1 = Female, and the measure for negative emotion language was the percentage of negative words within the total words used. Gender (b = -
388.627, SEb = 257.380, β = -.109, p =.133) and negative emotion language (b =
539.245, SEb = 553.118, β = .081, p = .331) were not found to be significant predictors of value claiming in negotiations, but their interaction term (Gender x negative emotion language) (b = -2318.936, SEb = 1106.960, β = -.175, p =.038) was significant. Hence, I can support H2a, as there is enough evidence to support that negative emotion expressions negatively predict value claiming for women more so than for men.
SMU LKCSB PhD in Business (General Management) PhD Dissertation
Figure 5. Interaction plot for negative emotion language and gender (Male = 0, Female = 1) on value claiming in negotiation.
To further understand this significant interaction and because our affective data observation method (LIWC2015) allowed us to break down negative emotions into three separate emotions (anger, anxiety, and sadness), I ran separate MMRs on the displays of specific negative emotions and value claiming. I found that the interaction terms between gender and anxious emotion language (b = 112.486, SEb = 2545.789, β = .004, p =.965), and gender and angry emotion language (b = - 5499.512, SEb = 3312.480, β = -.136, p =.098) were not significant predictors of value claiming. Finally, the interaction term between gender and sad emotion language was found to be a significant negative predictor of value claiming (b = - 3720.283, SEb = 1797.940, β = -.182, p =.040). Results indicated that sad emotion expressions negatively predict value claiming for women more so than for men.
SMU LKCSB PhD in Business (General Management) PhD Dissertation
Figure 6. Interaction plots for sad emotion language and gender (Male = 0, Female = 1) on value claiming in negotiation.