Introduction
With no doubt, the route to growth and economic recovery depends on creating more businesses and having a workforce with the skills to support business start-ups. Entrepreneurship and economic recovery are two sides of the same coin. Entrepreneurship is an important mechanism permeating the knowledge filter to facilitate the spillover of knowledge thus generating economic growth (Audretsch, 2007). As professional networks are key factors to help coping with economic uncertainty, entrepreneurs must network to survive.
Networking implies building personal and professional relationships to create a system of information, contact, and support crucial for personal and career success (Rasdi, Garavan & Ismail, 2011). Thus, networking is a proactive behaviour that can be defined as “individuals’ attempts to develop and maintain relationships with others who have the potential to assist them in their work or career.” (Forret & Dougherty, 2004, p.420).
In the academic literature, the value of a professional network has been widely acknowledged. Some benefits include access to other’s information, advice, influence, and resources not available via market transactions (Hoang & Antoncic, 2003; Ostgaard & Birley, 1994). It can also help the company to achieve economies of scale (Mancinelli & Mazzanti, 2008). Thus, managerial implications relate to the fact that business networking is a key asset for the competitive advantage of a company, business survival, growth and return on equity (Mitrega, Forkmann, Ramos & Henneberg, 2012; Watson, 2007). Despite these pieces of evidence, some findings indicate that the business advantages of social networking are still undervalued (Bennett, Owers, Pitt & Tucker 2010).
Authors such as Treadway et al. (2010) suggest that socioeconomic and managerial status could explain an important part of the levels of networking behaviour, but also come other factors. Muscanell and Guadagno (2012), and Wolff and Kim (2012) found that networking behaviour is influenced by personality dimensions (i.e., extraversion, agreeableness, openness to experience). Correa, Willard and Gil (2010) correlate individuals’ personality traits and emotional stability with the use of networks. In a parallel way, emotional intelligence (EI), as the ability to detect, use, understand and manage emotions, is increasingly becoming significant in social networking (Chopra & Kanju, 2010).
According to the results of Ahmetoglu, Leutner and Chamorro (2011), more emotionally intelligent individuals are more likely to engage in innovative entrepreneurial activities and usually have higher creative personalities. In a highly competitive economy, successful entrepreneurs will be the ones who effectively manage their networks and build strong relationships (Bhattacharyya, 2010).
An online survey was conducted to obtain evidence of the relation between entrepreneur proactive networking behaviour and EI. The Trait Emotional Intelligence Questionnaire-Short form (TEIQue-SF), developed by Cooper and Petrides (2010), was used to test hypotheses on the factors that define a proactive use of a professional network and their relationship with the individual level of trait emotional intelligence and its four components (well-being, self-control, emotionality and sociability).
The article is structured as follows: first, the literature on this field is reviewed to specify the research hypotheses; second, the research methodology, which addresses the main following questions, is presented:
(1) How does trait emotional intelligence relate to the proactive networking behaviour among entrepreneurs?
(2) How can trait emotional intelligence act as a predictor of proactive networking behaviour?
As networking, as defined by Torenvlied et al. (2012), is not a “one-dimensional phenomenon”, other variables have been included in the study. These added variables are related to the nature of the networking activity (strategic versus non-strategic) and to the way contacts are maintained (off-line versus on-line), which is a type of networking behaviour (Forret & Dougherty, 2001).
Finally, the theoretical and practical implications of the study findings and its limitations are discussed. Results show how proactive professional networking behaviour correlates with emotional intelligence and its components: self-control, emotionality, well-being and sociability. Thus, the present study provides a clear direction for further research by focusing on the analysis of emotional intelligence and how it affects social networking behaviour amongst entrepreneurs.
Proactive Networking and Emotional Intelligence
Proactive networking behaviour
In the field of entrepreneurship research, the value of networks as part of the explanation for the entrepreneurial success is widely acknowledged (Tipu & Arain, 2011; Bøllingtoft, 2012; Hite & Hesterly, 2001; Rothschild & Darr, 2005). Formal and informal networks are crucial in an entrepreneurial environment (Rothschild & Darr, 2005); though most entrepreneurs are not aware of the value of their networks for their business (Klerk & Saayman, 2012).
The level of proactivity that an entrepreneur adopts in networking can be positioned along a continuum from “reactive” to “proactive” (O’Donnell, 2004). As O’Donnell (2004) states, “an owner-manager will be “proactive” in networking with a particular network actor if he networks with the actor in a planned and deliberate way, has keen expectations of the benefits of networking and regularly creates opportunities to network with the actor.” (p. 212). Networking with a view to developing strong ties requires a proactive behaviour.
In a parallel way, professional networking based on the Internet is helping to increase the size of potential networks. Moreover, the structural characteristics of digital technology allow the establishment of numerous self-perpetuating connections (Kuss & Griffiths, 2011). At this respect, professional networking on the Net is increasing to support professional network development. Thus, both virtual and face-to-face networks must be emphasized as key factors for a successful development of any entrepreneurial activity. However, 85% of managers still do not use this activity with strategic purposes (Cheuk, 2007). Therefore, it is more important than ever to understand the factors that can change that percentage.
EI as predictor of proactive networking behavior
Several studies try to understand networking predictors and the establishment and usage of social networks among entrepreneurs (Slotte-Kock & Coviello, 2010). Forret and Dougherty (2001) show that gender, socioeconomic background, self-esteem, extraversion, favourable attitudes toward workplace politics, organizational level, and type of position were significant predictors of involvement in networking behaviours. Wolff and Kim (2012) offer an integrative framework on the personality-networking relationship. When online networking is taken into consideration, literature suggests that extraversion, emotional stability, and openness to experience relate to uses of social applications on the Internet (Correa, Willard Hensley, Gil de Zimiga, 2010).
Academic articles exploring the concept of EI began to appear in the early 1990s. According to Salovey and Mayer (1990), EI is “a kind of social intelligence that enables individuals to monitor the emotions of others and their own emotional status, to discriminate among these emotions and to use this information to guide thinking and actions” (p.187). For these authors, EI has four branches: the ability to accurately perceive and express emotion, assimilate emotion into thought, understand emotion, and regulate emotions in the self and others (Mayer & Salovey, 1997). In 2000, Mayer, Caruso and Salovey (2000) developed their most current measurement tool, the Mayer-Salovey-Caruso emotional intelligence test (MSCEIT), while Goleman (1995, 1998) developed its own EI competence model. In a similar way, Boyatzis and Goleman (2002) built the emotional competency inventory (ECI), which is designed to evaluate self-awareness, self-regulation, motivation, empathy, and social skills.
Petrides and Furnham (2001) claimed that there is a fundamental difference in the measurement of EI constructs. Consequently, the authors proposed a differentiation between ability EI and trait EI. Ability EI involves actual abilities and should be measured with “maximum-performance” tests, and it is directly applicable to cognitive ability (Petrides & Furnham, 2001, p. 426). Trait EI is comprised of “behavioural dispositions and self-perceived abilities” and should be measured through self-report questionnaires, and is related to the study of personality (Petrides & Furnham, 2001, p. 426). From the distinction between ability EI and trait EI, the theory of trait intelligence was established. The construct developed by these authors, measured through the TEIQue questionnaire, consists of four factors:
• well-being, related to optimism, self-esteem and trait happiness
• self-control, related to emotion regulation, impulsiveness and stress management
• emotionality, related to emotional expression, trait empathy, and quality of relationship
• sociability, related to emotion management, assertiveness and social awareness.
For our study, we will be using Petrides and Furnham’s (2001) trait EI definition and EI will be studied within a personality framework (Petrides, 2001, Petrides & Furnham, 2000, 2006).
EI is an important factor in the prediction of entrepreneurial outcomes (Chell, 2008; Ahmetoglu, Leutner & Chamorro-Premuzic (2011) and entrepreneurial behaviour (Bahadori, 2012). Given the social nature of entrepreneurial activities, EI can predict entrepreneurial success. Indeed, several authors suggest that higher levels of trait EI are necessary to exploit opportunities and innovations (Chell & Baines, 2000). Therefore, there is a relationship between entrepreneurs’ EI and their success (Karimi, Kloshani & Bakhshizadeh, 2012). Based on these arguments this study states the following hypotheses:
Hypothesis 1: Trait EI has positive effect on proactive networking behaviour.
Hypothesis 2.1: Self-control has positive effect on proactive networking behaviour.
Hypothesis 2.2: Well-being has positive effect on proactive networking behaviour.
Hypothesis 2.3: Emotionality has positive effect on proactive networking behaviour.
Hypothesis 2.4: Sociability has positive effect on proactive networking behavior.
Research Method
Sample
Authors employed a structured questionnaire to collect detailed information about trait EI and networking activities of local entrepreneurs residing in Tarragona (Note 1). In order to obtain a convenient sample, questionnaires were e-mailed to a stratified sample of 450 owner-managed firms. A total of 42 (9.33%) usable questionnaires was received and used in the analysis. Among these, 16.67% were women and 83.3% were men. In terms of age, the results revealed that the majority of respondents (66.6%) were below the age of 45. A 35.71% of the respondents worked in the manufacturing sector, a 54.76% in the service sector, and a 9.29% in the primary sector. Amongst respondents, 30.9% had never used networking proactively, 19% used it on a daily basis, 30.9% less than once a month, and 19% several times per month.
Instruments
The Trait Emotional Intelligence Questionnaire-Short form (TEIQue-SF). The instrument chosen to measure trait emotional intelligence in this study was the Trait Emotional Intelligence Questionnaire—Short Form (TEIQue-SF) (Petrides & Furnham, 2006). It provides scores on four factors: well-being, self-control, emotionality, and sociability (Petrides, 2001).
A high well-being score indicates an overall sense of well-being. In general, individuals with a high score on this factor are fulfilled and satisfied with life. The self-control factor refers to one’s degree of control over their urges and desires. Individuals with a high self-control score have the ability to manage and regulate external pressures. Individuals with a high emotionality score possess a wide array of emotion-related skills: recognizing internal emotions, perceiving emotions, and expressing emotions. The sociability factor focuses on one’s social relationships and social influence. This factor differs from the emotionality factor in that it evaluates one’s influence in a variety of social contexts, rather than just in personal relationships with family and friends. Individuals with a high sociability score are good listeners and effective communicators (Petrides, 2001).
For the present study, a reliability Alpha coefficient of 0.84 was obtained for the trait emotional intelligence scale and an alpha coefficient of 0.8 for the factor of Emotionality (8 items), 0.6 for Self-control (6 items), 0.82 for Well-being (6 items) and 0, 67 for Sociability (6 items).
Strategic versus non-strategic strategic. This research assumes that, as any other managerial action, networking can be categorized as either strategic or non-strategic. A strategic networking must be understood as the process that entrepreneurs use to align networking activities with business strategy. All the constructs were assessed with self-report measures. Responses to all items were made on 7-point Likert scales from strongly disagree (1) to strongly agree (7).
Online versus offline networking. As in today’s digital society, social networking technology can facilitate communication and collaboration between a new venture as its stakeholders (clients, suppliers, …); two variables measuring how entrepreneurs built their networks have being introduced in the analysis. Offline networking measures how entrepreneurs develop their professional network through the most appropriate offline channels (meetings, conferences,…) in terms of planned objectives, and online networking measures how entrepreneurs build their professional network through the most appropriate online channels (virtual communities) in terms of planned objectives. In both cases, there is an implicit proactive behaviour to search for opportunities for both formal and informal interaction and collaboration with clients/customers and other stakeholders.
Control variables. Following previous research (i.e., Bahadori, 2012), this study includes age (years), gender (1 female, 2 male), and working experience as control variables.
Data analysis
Correlation coefficients and reliabilities are shown in Table 1, which presents the result obtained from a bivariate analysis. The findings indicate that Proactive Networking Behaviour was positive and moderately associated with Strategic Networking (r = .332), and it is highly correlated with Trait EI and two of its components (Well-being and Sociability), with their respective r (r = .603, r = .626, r = .603). Finally Proactive Networking is negatively correlated with Sex (r =-.595). Previous research on proactivity has also revealed that proactivity is contingent on gender (Forret & Dougherty, 2004).
Total Trait EI correlated highly with its four components, as expected, and moderately with Offline networking, Age and Years in the Labour Market (r= .465, r = .675, r = .626). Trait EI Well-being correlated with Proactive Networking, Offline Networking and Sex (r = .626, r = .675; r = -.444). Trait EI Sociability correlated with Proactive Networking, Offline Networking, Sex, Age and Years in the Labour Market (r = .607, r = .338; r = -.331, r = .714, r = .619). Trait EI Self-control was positively correlated with Offline Networking, Sex, Age and Years in the Labour Market (r = .338, r = .353; r = .482, r = 545) and negatively correlated with Strategic Networking (r = -.333). Finally, Trait EI Emotionality correlated with Offline Networking, Age and Years in the Labour Market (r = .498, r = .541; r = .516).
In this study, we formulated different hypotheses regarding the relationship between proactive networking and trait EI and its four dimensions. In the first hypothesis, it was hypothesized that: (a) trait EI would be positively related to proactive networking behaviour. However, no support was found for this. In the second hypothesis, it was hypothesized that: (a) trait EI components would be positively related to proactive networking behaviour. Support for two of the subscales, well-being and self-control, was found. Thus, it can be pointed out that proactive networking behaviour is not related to trait EI global scale, emotionality and sociability, but correlated with well-being and self-control. This is surprising, as the ability to influence others should be expected to impact on proactive networking behaviour.
Table 1: Pearson’s correlations
N=42. Gender coded (male=2, female=1). ** p< 0.01; * p<0.05.
Due to correlation among variables and a detected problem of multicollinearity among Trait EI and its four components, an exploratory factor analysis was conducted to explore the data for patterns, to confirm research hypotheses and to better understand the factors that affect Proactive networking behaviour. To check about the viability of conducting a factor analysis, a Barlett’s test of sphericity and a KMO test were conducted. For the research data set, KMO was .442, which is not very large. Nevertheless, Barlett’s test was significant (p = .001). Thus, from the perspective of Bartlett’s test, factor analysis was feasible.
SPSS software was used to extract the number of components using principal component analysis. Tables 2 and 3 show the factor solution, once the set of variables with complex structure were removed from the model. These variables were Non-strategic networking, Totat Trait EI, Online networking and Age. The final factor solution was based on the extraction of 4 components, being communality value for each variable higher than 0.50. As shown in Table 2, the final solution explained 87.816% of the total variance.
Table 2: Total variance explained
Table 3: Rotated component matrix 
In the principal components output, the rotated component matrix (Table 3) presents the correlation of each non-excluded variable with each factor. The Varimax method was selected since it produced the most reliable results. Factor analysis provides a general understanding of the unique dimensions that exist in the analyzed data. Results help to confirm or reject preliminary hypotheses and they provide a first assessment of the dimensions in the data for further analyses. From the contribution of the variables, factors can be explained as follow:
• Factor 1. Well-being
• Factor 2. Sociability and self-control and years in the labor market
• Factor 3. Emotionality. Offline networking
• Factor 4. Strategic Networking.
To test the impact of the factors on proactive networking behaviour, a multiple linear regression analysis was conducted. Results are presented in Table 4. The overall regression model was successful in explaining approximately 84.0% of the adjusted variance showing a significant overall prediction of the Proactive Networking Behaviour among entrepreneurs. The findings reveal that three factors were found to be significant. This result indicated that Proactive Networking Behaviour is positively affected by Well-being, Sociability and Self-control, and Strategic Networking. However, emotionality has no influence.
Table 4: Multiple regression analysis on Proactive Networking Behaviour Coefficients a
Conclusions
Networking is a must-do activity for entrepreneurs as professional contacts link entrepreneurs with critical opportunities, support and resources. Networking helps an entrepreneur build more effective relationships with customers, suppliers, investors, public administration or financial institutions. As such, developing contacts through networking is important for starting a business and for its survival, growth and future development. In consequence, it is essential to investigate entrepreneurial behaviour in terms of networking practices and predictors. In this context, this paper adds to the literature by evaluating how trait emotional intelligence supports proactive networking behavior amongst entrepreneurs. It demonstrates the utility of using EI to evaluate high potential entrepreneurs. It explores whether EI can be a good predictor of proactive networking behaviour. Final findings partially confirm research hypothesis, with some components of EI (well-being and socio-selfcontrol factors) showing a significant positive correlation with proactive networking behaviour.
Managerial implications of our findings relate to the fact that two dimensions of trait emotional intelligence (well-being, self-control) have positive effect on proactive networking behaviour among entrepreneurs. This might indicate that entrepreneurs with higher scores in these components will proactively manage their networks. In other words, the entrepreneurs’ ability to regulate emotions will influence their networking behaviour. Therefore, emotional intelligence cannot be a missing factor in entrepreneurship studies.
End Notes
Note 1: Tarragona is a city located in the south of Catalonia on the northeast of Spain, by the Mediterranean Sea.
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