Does Emotional Intelligence Effect Leaders Strategic Decision Making?


Emotional intelligence is important for a leader nowadays as it could improves leader in strategic decision making, problem solving and cooperate with others. The objective of this study is to examine the effects of emotional intelligence on strategic decision making among leaders in Government Link Companies (GLCs). Data were collected using questionnaire. Stratified random sampling were used to select the sample for this study. Collected data were analysed using Partial Least Square Structural Equation Model (PLS-SEM) approach. The result revealed that there is a significant effects between the emotional intelligence and strategic decision making. The implications of this study could be used as a guide for leaders in managing and controlling their emotions when making a strategic decision for their organization. Theoretically, this theory use which is leader member exchange theory, is significant since the outcomes will contribute to the body of knowledge in the field of leadership, and GLCs in Malaysia.

Keywords: Emotional intelligencestrategic decision makingPLS-SEMgovernment link companies


In many organization, the management affects the quality of decisions in the long term. According to Papadakis and Barwise (2002), the strategic decision making is influenced by the top management. At the strategic level, decision making is highly depending on the way executives think and the organization’s performance (Khosrowshahi & Howes, 2005). In an organization, a good emotional intelligence is needed by a leader in order to make a strategic decision for the organization (Ahmed, 2013). Thus, it shows the importance of emotional intelligent as it plays a significant role in various aspects of leaders. Sumathi, Felix and Madhavi (2015) agree that effective decisions are important to be made in working life in an organization. Moreover, strong emotional intelligence could enhance leaders in making effective decisions. Leader’s ability in tackling issues and opportunities faced by the organizations and also the ability in solving the problems can be enhanced through the emotional intelligence (George, 2000; Gardner & Stough, 2002).

Problem Statement

Most of the Malaysia’s organizations face the challenges in managing the environment which changing speedily nowadays (Hudani, Marof, Iqbal, & Anuar, 2015). As stated by Modassir and Singh (2008), in order to have effective leadership roles, emotional intelligence abilities should be equipped by the leaders. In achieving organizational success, emotions play a significant role especially in the process of employee’s decision making in order to ensure creativity and more innovative changes, teamwork, strategic renewal, customer loyalty, transparency and open communication (Groves & Vance, 2009). There is an increasing debate on the significant of emotions as one of the factors that may influence the process of decision making (Ealias & George, 2012). However in the context of Malaysia, findings of emotional intelligence might not be generalizable to business environment as most of the studies were done in the education field (Hazana, Alina & Noorlaila, 2015). With this in mind, this study is been done to find whether emotional intelligence plays a key role in strategic decision making.

Research Questions

The research question for this study: What is the effect of emotional intelligence on strategic decision making among leaders in Government Link Companies (GLCs)?

Purpose of the Study

According to Diggins (2004), the best managers need to possess emotional intelligence to make decisions based on a combination of self-management, relationship skills and awareness of their behaviour’s effects on others in the organization. He argued that emotional intelligence plays a greater role than "traditional intelligence" in determining leaders and organizations' success. Thus, the purpose of the paper is to identify the effect of emotional intelligence on strategic decision making among leaders in Government Link Companies (GLCs).

Research Methods

This study used a cross sectional study, with a quantitative approach. A stratified random sampling techniques was employed in selecting the sample for this study. Since this study is focusing on leadership, data were gathered from middle to top level management from the selected government link companies (GLCs). Total number of questionnaire distributed is 120, however only 55 questionnaire were returned, which represents 45.8% of respond rate. Data were collected using a structured questionnaire. The dimensions for each variable used and source of reference are as in Table 01 below. Data were analyses using IBM SPSS version 22 and Partial least squares (PLS) structural equation modeling (SEM) approach.

Leader member exchange (LMX) theory is use in this study. This theory focuses on the level of the relationship between the leader and an individual follower (Graen & Uhl-Bien, 1995). LMX theory is use in this study as it is also centred on emotional intelligence, since it focuses on the interaction between the leader and the follower. In order for the leader to successfully guide other members to accomplish their goal they must be able to manage and understand emotions accurately. They also must be able to accurately perceive and interpret emotions so they can accurately respond in a way that makes the other members comfortable and content (Hohlbein, 2015). Besides, LMX theory also supports the idea that it is the leaders who are responsible for leadership and action to be taken as well as making a strategic decision for the organization (Bornay-Barrachina, & Guerrero-Villegas, 2014).

Hypothesis proposes for this study are as follows:

H1: There is a significant effects of emotional intelligence on strategic decision making.

Table 1 -
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Respondent profile

The study analyse the data with a 55 sample respondent out of which 34 respondent are female (61.8%) and male with 21 respondent (38.2%). For nationality, majority of the respondent are Malaysian with 50 respondent (90.9%). As for age, most of the respondent are age between 31-40 years old (49.1%), and the least are age between 51- 60 years old with 2 respondent (3.6%). 36 respondent (65.5%) are Malay respondent which holds the highest number of respondent. Majority of the respondent are married with 37 respondent (63.6%). The greater portion of the respondents were bachelor degree with 38 respondent (69.1%). Highest number of respondent are from the executive level with 34 respondent (61.8%). For years working in the organization, most of the respondent are with less than 5 years with 40 respondent (72.7%), Lastly, for number of years working in the industry, highest number are for less than 5 years with 28 respondent (50.9%).

Measurement model

To assess the measurement model it involves evaluation of reliability and validity. Reliability, for this study will be assessed through internal consistency which is measures using composite reliability (CR). CR is considered more suitable for Partial least squares (PLS) structural equation modelling (SEM) (PLS-SEM) because it incorporates information about the item loadings into its calculation (Hair et al., 2011; Kock, 2011). Result is presented as in Table 02 below, CR value are range from 0.831 to 0.965, all of the CR are value are as recommended by (Compeau, Higgins, & Huff, 1999), where all the items must be more than 0.70.

Data validity for this study were analyses through two analysis which is (1) convergent validity and (2) discriminant validity. In assessing convergent validity, the factor loadings, and average variance (AVE) should be assessed (Hair, 2010). From Figure 1 below, it can be seen that we have conceptualized emotional intelligence and strategic decision making as second-order constructs. Thus, we followed the method suggested in the literature in PLS which is the repeated indicator approach to model the second-order factors in the PLS analysis (Hair, Matthew, Matthew, Sarstedt, 2017) As presented Table 02 below, for factor loading all of the coefficient are greater than 0.5, which is as recommended by Hair, Ringle, & Sarstedt (2010) and Barclay, Higgins, & Thompson (1995). Lastly, to show acceptable validity, AVE value of the constructs are assess, each construct should be higher than 0.50, in order to explain more than half of the variance of its indicators on average (Henseler, Ringle, & Sinkovics, 2009). As presented in Table 02 below, all scales meet this criterion.

Next, discriminant validity were analyses using Fornell and Larcker method. To compute the discriminant validity, the square root of the AVE for each construct was used. Which means, the square roots of AVE coefficients are used in replacing the correlation matrix along the diagonal line (Fornell, & Larcker, 1981). As shown in Table 3 , we have used the second method which is to compare the square root of the AVE with the correlations. Result as in Table 03 below shows that, all the AVE square roots are higher compared to the corresponding off-diagonals which shows, the construct has a discriminant validity.

Table 2 -
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Table 3 -
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From the analysis above, it could be concluded that the measurement fit, as it meet all the criterion for convergent validity, discriminant validity, as well as reliability. Thus, once the measurement model is satisfactory, assessment for structural model results is proceed as noted by Hock, Ringle, & Sarstedt (2010).

Structural model analysis

Path coefficients are the relationships between the latent variables in a structural model. The path coefficients have standardized values between -1 and +1 with coefficients closer to zero considered weakest. Figure 01 below shows the path analysis for the direct relationship between emotional intelligence and strategic decision making, it shows that the relationship are important with a path coeeficient value of 0.898 having a strong bearing on the strategic decision making construct.

Figure 1: Path Coefficient
Path Coefficient
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Bootstrapping is specifically, used to obtain the significance levels of the path coefficients through t-statistics (Ringle, Wende & Will, 2005). The result in Table 04 below, accept the hypothesis with (t-value: 2.831**, p-value <0.01). The result is suppoorted with findigns by Al-Azzam (2015) and Fallon, Panganiban, Wohleber, Matthews, Kustubayeva, & Roberts (2014) who have emphasized the role of emotional intelligence in decision making. Westaby, Probst, & Lee, (2010) in his studies have also revealed that emotions have strong effect on decision-making. The predictive capabilities of the structural model is determined based on the R2 (Hair et al., 2013). As shown in Table 04 below, the R2 value are 0.806, indicating that emotional intelligence explain 80% of variance in strategic decision making. The overall validity of the structural model is also verify by the Q2 value, Table 04 present the Q2 values of 0.404, which is above 0 as recommended by Chin (1998). Thus, the result provide the support that the variables use in this study are adequately predicting the independent variable.

Table 4 -
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Emotional intelligence managers make better decisions as they could use the emotions to improve judgments and see things clearly. Besides, leaders with a high emotional intelligence tend to achieve their personal and organizational goal more effectively than leaders without that quality (Ahmed, 2013). For this study, the result revealed that emotional intelligence has a significant effect on strategic decision making, which shows how emotional intelligence plays a big role in decision making abilities. This study could contribute towards improving leaders in GLCs as leaders with a high emotional intelligence are able to correctly scan the organizational environment, weigh the possible consequences of their decision (Alkahtani, 2015). The present study has contributes theoretically, in a way that, with the presence of LMX, leader is not alone to make decision as employee’s feedback could also help in enhancing leader strategic decision. Other than that, this study not only highlights the importance of emotional intelligence towards leader strategic decision making, but it also highlight that the presence of LMX theory could enlightens the positive impacts towards employee participation in strategic decision making. Future study should be conducted in a different organizational setting, as different setting would require a different level of emotion.


Authors would like to acknowledge UNITEN BOLD research grant for allocation of fund to publish this paper.


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