Weak Ties and Self-regulation in Job Search

Abstract

The purpose of this study is to empirically investigate the relationship between the self-regulatory variable of goal orientation and the extent to which job seekers reach out to and use weak ties in their job search. Weak ties, as defined by Granovettor, are connections to densely knit networks outside the individual’s direct contacts who could provide nonredundant information. The study builds on the previous conceptual work that discussed how learning and performance goal orientation, as a part of a larger system of self-regulatory variables, may affect the extent to which individuals seek feedback and network during job search. Using a sample of Canadian job seekers, this study examines whether learning goal-oriented individuals contact weak ties more often than performance goal-oriented individuals. The results indicate that both performance and learning goal orientation are significant predictors of weak tie counts.

Keywords

job search self-regulation social networking goal orientation

Since the beginning of the global economic slowdown in 2008, job search has been an important concern and often a necessity for many who have become unemployed. The process of finding a job may be long and arduous and requires effort and commitment from the job seeker. Many people give up and withdraw from the workforce. Many job seekers become discouraged, perhaps because they do not know how to find job leads. This group of people, called discouraged workers, is a part of a larger group known as workers marginally attached to the workforce. The marginally attached workers are those who are not in the labor force but want and are available for work, while discouraged workers are those who believe that no work is available for them. Of the long-term unemployed (4.7 million, or 36% of all unemployed), 2.6 million persons are marginally attached to the workforce and 885,000 are discouraged workers.

Kanfer, Wanberg, and Kantrowitz (2001) suggest that looking for a job is equivalent to an unstructured, ambiguous, and autonomous work task. It requires significant self-regulation on the part of the job seeker. The individual must set appropriate goals, structure the approach to the job search task, make decisions about the intensity of effort to exert, and periodically adjust efforts and strategies (G. Blau, 1994). Therefore, Kanfer et al. (2001) defined job search as the outcome of a dynamic, recursive, and self-regulated process. Part of this process involves connecting with others to obtain job leads and feedback on one’s progress toward the goal of obtaining employment. Such interactions inevitably necessitate processing self-evaluative information and adjusting one’s effort based on this information. This process is essentially a self-regulatory process. In previous research, it was argued that self-regulatory mechanisms, specifically goal orientation, may be a key determinant of an individual’s propensity to engage in networking behaviors (Yamkovenko & Hatala, 2013). In this study, we empirically examine these propositions and test the hypotheses that performance and learning goal orientations may reduce or increase, respectively, the number of contacts a job seeker reaches out to for job-related information.

Self-regulation

Self-regulation refers to the psychological capacity to bring one’s thoughts, feelings, and behavior in line with their own standards, values, and goals (Forster & Jostmann, 2012). Goals are the key element of self-regulation (Burnette, O’Boyle, VanEpps, Pollack, & Finkel, 2012). Self-regulation is rooted in the concept of the feedback loop. Burnette, O’Boyle, VanEpps, Pollack, and Finkel (2012) conceptualized the feedback loop in terms of goal setting, which begins the loop. Goal setting leads to goal operating, which serves as an input function to the goal monitoring process, which in turn leads back to goal operating (goal operating includes actions and strategies to reach the goal and continues in some modified form after goal monitoring provides feedback on how well an individual is doing). This loop continues until the goal is achieved or is deemed unachievable, at which point an individual disengages from the goal.

The self-regulated nature of the job search suggests that it is likely to change over time, which means that the intensity of the job search, and therefore frequency of various job search behaviors, can decrease, increase, or remain stable (Wanberg, Glomb, Song, & Sorenson, 2005). Wanberg, Glomb, Song, and Sorenson (2005) suggest that getting discouraged, adjusting the goals, and uncertainty about what to do next may all contribute to the change in job search intensity. This complexity of the job search behaviors is a function of the interaction of personal tendencies, personal and social conditions, and desire to obtain employment (Kanfer, Wanberg, & Kantrowitz, 2001).

One of the variables that gained a lot of attention in the self-regulation literature is goal orientation. Goal orientations contribute to the self-regulatory process by affecting the types of goals people set (Kanfer, 1990). These types are learning goals, which focus on learning and developing a particular skill or ability, and performance goals, which focus on demonstrating competence in a given task (VandeWalle, 2001). Learning- and performance goal orientation—two broad dimensions of one construct—have been shown to influence different types of performance outcomes and to evoke various types of behaviors in performance settings (e.g., Arenas, Tabernero, & Briones, 2006; Button, Mathieu, & Zajac, 1996; Sujan, Weitz, & Kumar, 1994; VandeWalle, 2001). Because of the nature of this construct, it is specifically related to dispositional and situational goal preferences in achievement situations (Payne, Youngcourt, & Beaubien, 2007). It has also been linked to goal operating strategies and differences in goal monitoring behaviors (Burnette et al., 2012).

Goal Orientation and Job Search

Examining goal orientations in the job search context, Van Hooft and Nordzij (2009) suggested that job search consists of two phases—deliberative and behavioral. A deliberative phase, during which individuals process information available to them, ends in the formation of the goal orientation, which in turn guides job seekers. Goal orientation is a relatively stable construct, but it has been shown that contextual characteristics and manipulations can help induce goal orientations (e.g., Kozlowski & Bell, 2006; Payne et al., 2007; Van Hooft & Noordzij, 2009). Goal orientation determines responses to achievement situations, and the malleability of the goal orientation is what makes it so useful in these situations.

Learning goal-oriented individuals engage in feedback seeking and advice seeking, make persistent attempts at solving problems, and use various strategies. At the same time, they are less likely to avoid challenges and withdraw after negative feedback (Elliot & Dweck, 1988). It has also been shown that learning goal- and performance goal-oriented individuals select and set goals of varied difficulty (Elliot & Dweck, 1988). This means that in a job search situation, performance goal-oriented individuals may set goals of lower difficulty and lower standards for themselves. This could translate into sending out fewer resumes, making fewer contacts, or not applying to positions similar to the ones from which an individual gets rejected. These behaviors suggest lower intensity of job search and lower intensity of job search behaviors lead to the lower likelihood of finding a job (Van Hooft & Noordzij, 2009). Therefore, performance goal-oriented individuals may be at a disadvantage in their job search efforts.

Lower intensity of job search may also stem from the perception of ability as fixed and viewing effort as an indicator of low ability (Dweck & Leggett, 1988). Performance-oriented individuals perceive that their failures are the results of their low ability, and an increase in effort will not lead to improved performance. Therefore, any failure in the job search process, like a rejection letter, lack of interviews, or absence of a callback, is predictive of future failures to these individuals. Because performance-oriented individuals attempt to avoid failures and only engage in tasks that display their competence to others, they may limit their job search activities, thus reducing their chances of getting an interview and eventually finding a new place of employment. Van Hooft and Noordzij (2009) posit that the difficulty and independent nature of the job search process calls for an individual who sets challenging goals and persists at achieving these goals. Therefore, learning goal orientation and learning goal orientation training may increase the job search intensity, thereby increasing the likelihood of finding a job. Learning goal-oriented individuals attribute failure to lack of effort rather than ability. The effort is then increased by analyzing and changing job search strategies (Van Hooft & Noordzij, 2009).

Performance-oriented individuals tend to assess their performance through normative comparisons rather than by comparing their progress to some internal standard (VandeWalle, 1997). The reliance on normative comparison is one of the key distinctions between learning and performance goal orientations (Elliot, 1999). Individuals who make normative comparisons and want to avoid tasks that may expose their perceived low ability to others may refrain from important job search behaviors like feedback seeking. Ashford (1989) suggested that people evaluate the costs of feedback-seeking behaviors and among these costs are ego and self-presentation costs. Negative feedback may increase perceived ego costs. Self-presentation costs are those of exposing one’s uncertainty and need for help. In addition, Ashford and Cummings (1983) noted that feedback seeking often entails considerable effort. In view of these suggestions, it is possible that performance goal-oriented individuals may refrain from seeking feedback because they may perceive the ego costs and self-presentation costs to be very high. In job search, feedback seeking is often an important part of the process. Job seekers may benefit significantly from asking for feedback on their resumes and cover letters, soliciting feedback from others on their interviewing abilities, and even asking for feedback from job interviewers to obtain information about the skills and experience they may be missing.

Looking for a new place of employment often involves leveraging resources within one’s network, making efforts to expand one’s network, making social comparisons, and processing various types of feedback from others. Research suggests that many jobs are often found through some sort of networking, which includes contacting friends, acquaintances, and family (Granovetter, 1995; Hatala, 2007; Schwab, Rynes, & Aldag, 1987). In addition, there is extensive research that investigated the value of social networks in industries, where positions are not typically advertised in the media, and in employment among graduating college students. In both cases, the findings suggest that networking is an effective informal method of job search (Meyer & Shadle, 1994; Stevens, Tirnauer, & Turban, 1997). To fully understand the implications of social networks for the job search process, it is instructive to review key research in this area.

Social Networks and Job Search

Research has suggested that an individual’s network is critical to social mobility (Dominguez & Watkins, 2003; Erickson, 1996; Lin, 2001; Stanton-Salazar & Dombusch, 1995). More specifically, individuals with higher levels of social capital have access to social resources that can be utilized to achieve a desired objective, such as searching for a job (Burt, 2001; Flap, 1999; Lin, 2001). From a job search perspective, actors within the network can improve behaviors or gain access to job-related information based on the connections they possess.

A social network theory approach to conducting a job search (Auslander & Litwin, 1988, 1991; Smith, 1989; Specht, 1986) suggests that social networks establish norms for behavior within a group, including accelerated job-search activity. Social networks may provide information and opportunity that are relevant to becoming reemployed by supplying additional contacts.

One approach to conceptualizing social capital is weak tie theory (Granovetter, 1973). The strength of weak ties theory showed that job opportunities for mid-level managers were most likely to come from an individual’s weak ties versus the strong connections in their network. Strong ties consisted of close relationships (family, coworkers, and close friends), which provided information that was widely shared and became quickly redundant within the clique. Granovetter (1973) viewed weak ties as a connection to densely knit networks outside the individual’s direct contacts which could provide nonredundant information. It was more likely that weak ties rather than strong ties would provide a greater opportunity for new information about job leads. In essence, the weak tie theory focuses on the characteristics of the tie between actors.

On the basis of this conceptualization, examining the job seekers’ social network will help to reveal the ties an individual possesses and how they affect their job search. Those individuals who seek support from their weak tie connections are not only more likely to receive nonredundant job-related information but also gain access to job opportunities not found in traditional sources (Yakubovich, 2005). If barriers to connecting with weak ties exist, the transition back into the labor market may be delayed. This delay can have severe implications for self-efficacy and other self-regulatory mechanisms, which, in turn, may restrict one’s ability to affectively conduct the job search (Fort, Jacquet, & Leroy, 2011).

Goal Orientations and Networking in Job Search

Goal orientation can be one of the barriers to connecting with weak ties. For example, performance goal-oriented individuals tend to seek feedback with ego-boosting motives (Ashford, Blatt, & Vandewalle, 2003). Learning goal-oriented individuals seek feedback with instrumental motives—motives to improve their skills. The differences in these motives also affect the means by which people seek feedback—using direct inquiry or monitoring (Ashford et al., 2003). Monitoring would be equivalent to assessing one’s progress toward the goal using normative comparisons. This implies that job seekers with trait performance goals may monitor their progress without directly reaching out to their social resources. It is possible that in a job search situation, a person would ask one’s close contacts, or strong ties, for help, regardless of the goal orientation. But, based on weak tie theory, such contacts may not be able to provide one with the resources needed to learn new job skills or find job leads. Therefore, it is important to broaden one’s network.

Exposing one’s status of unemployment may be perceived as equivalent to exposing one’s shortcomings, like lack of skills and ability. Exposing these shortcomings to those outside one’s immediate network may be perceived as a serious threat to one’s ego. So a performance goal-oriented job seeker would rather limit the networking and feedback-seeking behaviors to his or her closest contacts. These limits may restrict one’s access not only to job leads but also to an objective assessment of one’s marketable skills and therefore learning needs.

Therefore, goal orientation may be an important factor in networking and reaching out to others for help during job search. Although research has examined the individual influences of goal orientation and networking behaviors on job search success (Van Hooft & Nordzij, 2009; Wanberg, Kanfer, & Banas, 2000), there are no studies that examined the interaction of these variables. Therefore, our first hypothesis is:
Hypothesis 1: Higher scores on learning goal orientation are associated with higher weak tie counts, whereas higher scores on performance goal orientation are associated with lower weak tie counts.

Two other constructs should be considered in explaining the number of weak ties. Wanberg, Kanfer, and Banas (2000) proposed that the extent to which individuals feel comfortable reaching out to one’s network predicts networking behaviors. The authors developed a networking comfort scale, which encompasses evaluative beliefs that portray individual’s attitudes toward using networking as a job search method. However, the measure of networking intensity did not differentiate between weak and strong ties.

In addition, job search self-efficacy is an important predictor of job search behaviors. Job search self-efficacy predicted preparatory and active job search behaviors and job search intensity (Saks & Ashford, 1999). Part of the job search self-efficacy construct involves one’s confidence in the ability to reach out to others. Burnette et al. (2012) argued that self-efficacy played a role during the goal-setting stages, where an individual assesses his or her ability to succeed in the task before engaging in it but not in the goal operating or goal monitoring stages. We believe that networking is a strategy required during the latter two stages of an achievement task and that self-efficacy will not predict networking behaviors over and above learning goal orientation.

Based on the above discussion, we suggest that networking behaviors and decisions to seek feedback during job search happen in the operating and goal-monitoring stages and thus goal orientation will provide incremental predictive validity, beyond that of networking comfort and job search self-efficacy. More formally:
Hypothesis 2: Learning goal orientation will incrementally predict the weak tie counts over and above self-efficacy and networking comfort.

Lastly, according to Wanberg et al. (2005) the self-regulated nature of the job search suggests that it is likely to change over time. Although we only collected data at one point in time, we also considered the amount of time individuals were unemployed. Behaviors associated with goal orientations, like persistence, goal adjustment, and withdrawal from task require time in the achievement situation to manifest themselves. Therefore, it is interesting to examine whether the hypothesized relationship for learning goal orientation and weak ties differs for those individuals who have been unemployed longer. It is logical to expect that over time learning goal-oriented (LGO) individuals will connect with more weak ties than they would at early stages of unemployment and in the later stages, learning goal orientation will become more and more important for increasing one’s network.
Hypothesis 3: Learning goal orientation will interact with length of time unemployed such that learning goal orientation will predict weak tie counts more strongly as time of unemployment increases.

Method

Participants

Participants were unemployed job seekers who were contacted via reemployment agencies in Ontario, Canada (78% were Caucasian, 7% were Black, 8% were Asian, and 7% did not identify themselves; 71.5% were female; 36.4% had high school education, 23.7% had college diploma, 21.3% had bachelor’s degree, 9% had graduate degrees, and 4.5% had graduate equivalency diploma). The criterion for inclusion into the study was that the job seekers remained unemployed at least 1 month before responding to the survey. This time frame would allow for possible setbacks in the job search process to manifest and for job seekers to consider alternative approaches to search for job opportunities. The reemployment agencies were approached to gain access to the job seekers. The career counselors in these agencies were explained the purpose of the study and were asked to administer the surveys to the job seekers prior to the training sessions routinely conducted by the agencies. The participation in the surveys was voluntary and anonymous. The data had to be collected at one time because many participants of the job search training sessions provided by the agencies may not return to the agency and it would be very difficult to track them. Overall 400 participants were provided with a link to the survey, via Survey Monkey, and 189 participants responded for a response rate of 47%. The usable data were obtained from 142 surveys; the remainder did not respond to the dependent measures or skipped the questions on the goal orientation scale.

Measures

Goal orientation

Goal orientation was measured with a 16-item Goal Orientation Scale (Button, Mathieu, & Zajac, 1996). The scale is a two-dimensional measure, in which 8 items assess performance goal and 8 items measure learning goal orientation.

A sample item for LGO is “The opportunity to do challenging work is important to me.” A sample item for performance goal oriented is “The opinions others have about how well I do certain things are important to me.” The authors of the instrument reported reliabilities of .84 and .82 for learning and performance goal orientation scales, respectively (Button et al., 1996). The Cronbach’s α internal consistency measures for the two scales in the current study were .84 and .80 for learning and performance goal orientation scales, respectively.

Job search self-efficacy

Job search self-efficacy was measured using Saks and Ashford’s (1999) 10-item scale. This measure uses a scale proposed by Bandura (1997), which, instead of asking for agreement with a statement, asks to indicate the confidence in performing or engaging in various behaviors associated with the task domain. Confidence was rated on a scale of 1 to 10, with 1 being not at all confident. A sample item from this scale was “Make cold calls that will get you a job interview.” The measure of internal consistency Cronbach’s α for this scale was a = .92.

Networking comfort

Networking comfort was measured using an 8-item scale by Wanber, Kanfer, and Banas (2000). The items depict individual attitudes toward using networking as a job search method. A sample item of this scale was, “I am comfortable asking my friends for advice regarding my job search.” Items used a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Previous factor analytic studies demonstrated a unidimensional structure of this scale and an internal consistency index of .79. In this study, the internal consistency index was .79.

Weak/strong tie counts

To determine the number of weak and strong ties (frequency) job seekers contacted, respondents were asked to indicate the type of contacts they sought help from during their job search. Strong ties were defined as those individuals with whom the respondent interacted at least once per week during the job search for a total of at least 4 times a month (i.e., family member, friend). Weak ties were considered those individuals with whom they interacted at least once during their job search but not as often as once a week (i.e., acquaintance, friend of a friend). Respondents were asked to record the number of strong and weak ties with a number for each. This approach to determining strong and weak ties is consistent with past research (i.e., Granovetter, 1973) in that it helps to determine the strength of relationship. Although there are a number of ways to measure tie strength (i.e., frequency, intimacy/closeness, reciprocity), this study was interested in the level of networking activity (making contact) versus the composition of the relationship, thus choosing to focus on the frequency measure. Accessing the “hidden job-market,” which refers to identifying job opportunities through contacts, is currently a common method for employers to find candidates for job openings (Carlson, Smith & Rapp, 2008). Therefore, job seekers who demonstrate higher levels of networking activity are more likely to discover job-related information that could be useful to their job search efforts and could be used as a strategic advantage. As a result, participants were asked to respond to the following:
During your job search you may have reached out to contacts you know for help. The people you interact with can be defined as ‘Strong’ and ‘Weak’ contacts. Strong contacts are those individuals with whom you interact at least once a week during your job search (i.e. family member, friend). For example, if you have been looking for a job for 2 months, you would have contacted this person at least 8 times. Weak contacts are those individuals with whom you interact at least once during your job search (i.e., acquaintance, friend of a friend). For example, if you have been looking for a job for two months, you would have contacted this person only 2 times. Based on this definition please answer the following questions: 1) How many weak contacts have you connected with for help during your job search? (Indicate with a number), and 2) How many strong contacts have you connected with for help during your job search? (Indicate with a number).

Analysis

The goal of the study was to explore whether weak tie counts differ among job seekers as a function of goal orientation. Given that the outcome variable was the count of weak ties, the conventional ordinary least squares regression approach to modeling this variable is inappropriate. Count data are often distributed Poisson, which is a special distribution used to model counts of events. Such distributions often have a pronounced positive skew (Lindsey, 1997). The inspection of the weak tie counts showed such a positive skew. Therefore, the appropriate and common approach to analyzing such data is generalized linear model approach, with a log link function (Wanberg et al., 2012). This approach is also known as Poisson regression. The assumption of the Poisson regression is that the mean and variance of the response are equal. If this assumption is violated, it may indicate overdispersion, in which case Poisson parameter estimates may be incorrectly estimated (Lindsey, 1997). An alternative approach to modeling data with a large number of zeroes is using negative binomial underlying distribution with logit link function. Because overdispersion was present in the data, we analyzed the data using negative binomial generalized linear model approach. The analysis was conducted in R (v. 2.14.1), using glm.nb function in the MASS package.

Results

The correlations and other descriptive statistics for the variables in the model are presented in Table 1.

Table 1.
Descriptive Statistics and Correlations of Selected Variables in the Study.

Variable n mean SD 1 2 3 4 5

1 Weak ties 142 9.84 42.87 1

2 Strong ties 142 9.60 19.52 .42* 1

3 Self-efficacy 156 64.43 18.54 .00 .12 1

4 Learning goal orientation 149 33.65 3.68 .09 .12 .37* 1

5 Performance goal orientation 149 30.46 5.04 .13 .10 −.02 −.09

6 Networking comfort 146 26.56 2.69 .03 .06 .06 .10

**p < .01.

Our hypothesis suggested that the learning goal-oriented individuals are likely to reach out to their weak ties more often than the performance goal-oriented individuals would. The variable that was predicted in the general linear model (GLM) equation was the count of weak ties that the job seekers contacted in their job search. The results provide mixed support for Hypothesis 1. The coefficient for learning goal orientation was significant (β = .086, p <* .05). Given the count data and the negative binomial GLM, the interpretation of this coefficient is done in terms of log of counts. In other words, for every one unit change in learning goal-orientation score the count of weak ties increases by e ^.08. The coefficient for learning goal orientation suggests that with every unit increase in the predictor, the count of weak ties increases by 9%.

However, the results also indicate that performance goal orientation is a significant predictor of whether job seekers reach out to weak ties. The coefficient is somewhat smaller than that for learning goal orientation (β = .064, p < .01). This is a surprising finding in view of the hypothesized relationships between learning goal, performance goal orientation, and weak ties. It also suggests that with unit increase in scores for performance goal orientation, individuals may reach out to weak ties 6% more frequently. The results of the negative binomial GLM are presented in Table 2.

Table 2.
Negative Binomial Generalized Linear Model Results for the Count of Weak Ties as a Function of Goal Orientation and Control Variables.

Variables Coefficient SE z p 95% CI

Intercept −3.4843 1.556 −2.238 .025* [6.917 to 0.15]

Learning goal orientation 0.0862 0.034 2.478 .013* [0.009 to 0.163]

Performance goal orientation 0.0646 0.023 2.663 .007** [0.0122 to 0.114]

Job search self-efficacy −0.0104 0.007 −1.422 .154 [−0.025 to 0.005]

Note. CI = confidence interval.

*p < .05. **p < .001.

In Hypothesis 2 we stated that LGO will predict weak tie counts over and above self-efficacy and networking comfort. Using a hierarchical regression approach, we first entered self-efficacy and networking comfort into the model. We then entered LGO and conducted extra sums of squares test to determine if LGO accounted for incremental variance in the weak tie counts. Self-efficacy was not a significant predictor of the counts of weak ties reported by job seekers neither in reduced (self-efficacy and networking comfort) nor in the full model (including LGO). Research has shown that self-efficacy may not predict the behavior in the goal-monitoring phase as opposed to goal-setting phase. Perhaps networking occurs at the stage of the process where the goal has already been initiated and the actor has already engaged in goal operating and now monitors progress. Networking may therefore be invoked in response to information obtained during monitoring. Networking comfort was a significant predictor of the counts of weak ties reported by the job seekers in the reduced model (β = .0715) and in the full model (β = .0711).

Adding LGO to the model with self-efficacy and networking comfort and testing the difference in the log likelihood for the two models resulted in significant likelihood ratio statistic of 14.06049 (df = 2, p < .001). In generalized linear models, R² is normally not computed, however a pseudo R² can be computed to gauge the variance explained. For the reduced model with self-efficacy and networking comfort only, maximum likelihood pseudo R² was .099, while for the model with LGO, it increased to .14. This indicates that the model with LGO is a better model, and learning goal orientation adds incremental explanatory power over and above self-efficacy and networking comfort. Therefore, Hypothesis 2 was supported. Residual deviance of this model is 148.958 (df = 128, p > .05).

We then added the time unemployed into the model along with the interaction term of LGO composite and time unemployed. We centered the variables for which the interaction was computed to simplify the interpretation. The interaction term was significant with a parameter estimate of β = .049 (p < .01; see Table 3). The likelihood ratio test statistics was 7.9 (p < .01) indicating that the term time unemployed added significantly to the model. The plot of the interaction of LGO and time unemployed is presented in Figure 1. Hypothesis 3 was therefore also supported. This indicates that the learning goal orientation effect on weak tie counts is more pronounced for those who have been unemployed for periods over 6 months.

Table 3.
Negative Binomial Generalized Linear Model Results For The Count of Weak Ties as a Function of Goal Orientation, Control Variables, and Learning Goal Orientation × Time Unemployed Interaction.

Variables Coefficient SE z p 95% CI

Intercept −2.110 0.925 −2.281 0.023 −3.893 −0.297

Job search self-efficacy 0.005 0.006 0.885 0.376 −0.007 0.018

Networking comfort 0.073 0.023 3.203 0.001 0.031 0.116

Learning goal orientation 0.047 0.029 1.599 0.110 −0.011 0.106

Performance goal orientation 0.049 0.020 2.457 0.014 0.009 0.087

Time unemployed 0.210 0.058 3.586 0.000 0.085 0.333

LGO × Time Unemployed 0.049 0.016 3.117 0.002 0.016 0.083

Figure 1.
The interaction effects of time unemployed and learning goal orientation on the counts of weak ties.

Discussion

Many individuals who find themselves unemployed for extensive periods of time may need to rely on a variety of strategies to increase the chances of finding a job. It has been shown that networking is beneficial in providing job seekers with job-related information (Auslander & Litwin, 1988, 1991; Smith, 1989; Specht, 1986). However, it is also important to recognize that not all network connections are equally useful in the job-search process. Strong ties may not be as instrumental in the job search as weak ties because strong ties may only have access to redundant information (e.g., know about job leads that the job seeker also knows about). On the other hand, weak ties represent acquaintances and people the job seeker does not frequently interact with. These people may be a source for job-related information otherwise inaccessible to the job seeker, which is often referred to as the “hidden job market.” They may also be connected to others who the job seeker would not be able to reach if he or she only networked with strong ties.

In this study, we examined whether LGO individuals connect with weak ties more frequently during job search than performance goal-oriented individuals. The findings indicate that both learning goal orientation and performance goal orientation are significant predictors of weak tie counts. While this finding offers support to the propositions advanced in this article, it poses many more questions than it answers. Despite the fact that the increase in count of weak ties is higher for learning goal than for performance goal orientation, it is not sufficient to conclude that performance- and learning-oriented individuals differ in the extent to which they contact weak ties for job leads-related information. This preliminary finding does indicate that goal orientation is an important variable in the self-regulation of the job search process, specifically in terms of feedback-seeking behaviors.

There are several explanations that may be offered to help interpret such a finding. First, we deliberately limited the goal orientation scale to two dimensions and did not examine approach and avoidance elements of each type of goal orientation. Some research suggests that performance approach goal orientation and learning approach and avoidance goal orientation lead to similar results in terms of achievement (Burnette et al., 2012). The difference in how these variables affect behavior is manifested during the goal operating and goal monitoring phases, when individuals appraise their levels of effort, ability, distance from the goal, and speed with which they are closing the current state-goal discrepancy.

The second explanation could be in the way the weak ties and strong ties data were collected. The participants were asked to think about people they reached out to during their job search and to differentiate these people by how often they would normally interact with them. In other words, the burden of making a cognitive assessment of whether a contact was a weak tie or a strong tie was on the respondent. It is possible that some respondents did not differentiate properly between the two types of ties. Alternatively, performance goal-oriented individuals are preoccupied with their self-image (Anseel, Beatty, Shen, Lievens, & Sackett, 2015), which may be reflected in a desire to overstate the number of contacts. The respondents may have believed that indicating a larger number of contacts would somehow reflect positively on their performance during job search. This of course would inflate the relationship between the performance goal orientation and the number of weak ties being reported. In addition, a recent study (Hastings & West, 2011) showed that learning and performance goals may affect memory performance through memory self-efficacy. While the effect of performance goals on memory performance via memory self-efficacy was n marginally significant at a = .07, it is possible that performance goals may lead to poor ability to recall the number of weak or strong contacts, again leading to overstated numbers.

Finally, the data seem to suggest that the longer the person is unemployed, the more prominent learning goal orientation becomes. The participants in this study were unemployed and worked with a job search coach. The frequency of the meetings with the coach is much higher in the early stages of unemployment. This may explain the negative slope for the learning goal orientation and weak tie counts for those who were unemployed between 1 and 6 months. Perhaps in the early stages of unemployment, job seekers’ source of feedback and job-related information is the job coach. In addition, those with higher learning goal orientation scores may be focused on mastering basic processes like resume writing and interviewing skills rather than networking. For those unemployed more than 6 months the job coach is no longer involved as much and job seekers may be forced to seek feedback and job leads elsewhere. This is when learning goal orientation may be most important given that it reduces the focus on normative comparisons and positively affects feedback seeking.

This study makes a first step in the direction of explaining networking and feedback-seeking behaviors in job search using the theory of self-regulation. The findings show that goal orientation is an important variable in the goal operating and goal monitoring phases of job search, especially when it comes to feedback seeking. However, further research is needed to determine whether approach and avoidance learning and performance goal orientations provide more clarity about how individuals decide whether to reach out for help to those they do not know.

Implications for Research and Practice

This study offers several directions for further research. First, given the current findings, it is important to operationalize goal orientation as a four-dimensional construct, reflecting avoidance and approach tendencies of each type of goal orientation. Such approach may offer more discriminating power among those that rely on normative comparisons and refrain from feedback–seeking and those that approach negative feedback as a learning opportunity. Second, this study shows that an approach to measuring weak and strong tie counts should not rely on the respondents’ judgments. Rather a more objective approach should be taken. For example, one could ask the job seekers to name the people they contacted during the job search, and then ask them to indicate how often they contacted these people prior to becoming unemployed during a specified period of time. Another example, which would also strengthen these types of studies in terms of the design, would be to track the job seekers over a period of several months while they are looking for a job and collect data on the frequency with which they contact others for job search-related help.

Another approach, which is currently being taken by the authors of this study, is experimental. We have simulated the conditions of an independent complex task and offered an opportunity to ask for expert help to solve a problem. After inducing different types of goal orientation we have observed the frequency of feedback-seeking behaviors of participants in the experiment. Preliminary findings point to the significant differences between performance and learning goal conditions. Such studies and their applicability to job search depend on the fidelity with which they simulate the environment and circumstances of a job search process.

Finally, the job search process is complex. A job seeker’s ability to navigate through it is affected by multiple forces, both psychological and environmental. While we believe that self-regulatory mechanisms can contribute a lot to our understanding of job search outcomes, it is very important to consider how these mechanisms interact with individual traits like emotional stability, conscientiousness and individual perceptions of self-worth, as well as demographic variables like age and socioeconomic status.

In terms of practical implications, we see an important role for career development practitioners. If job seekers are taught to set proper goals, they may become more persistent in job search and cope better with the setbacks during the process, view their social network as a source of feedback and assistance, and in general be more successful in achievement situations, such as a job search. Setting learning goals may lead to a more structured approach to job search and may increase one’s chances to learn new marketable skills. Workshops that set learning goals and create learning goal frames are often designed to increase learning of new skills and exploration of new content (Kozlowski & Bell, 2006). The inherent nature of a learning goal frame reduces reliance on normative comparisons. This in itself may increase the likelihood and number of connections individuals make to find jobs. Finally, explaining the value of any form of feedback for learning in the job search process, including the value of feedback in the form of rejection, may increase the extent to which job seekers develop their skills while they are unemployed. Simple training interventions may help reduce not only the time it takes to find a job but also the pool of unemployed.

However, the importance of feedback seeking and networking and how to increase the extent to which people engage in these behaviors is critical to job search success. Throughout the job search, people are increasingly faced with uncertain and independent tasks. The tasks required during the job search are often not structured, and the success on such tasks relies on planning, goal setting, and evaluation of current state-goal discrepancies. One example that comes to mind is once an employment agency has prepared the job seeker to start looking for work (e.g., helped them with their resume), it is up to the individual to identify and apply for job opportunities on his or her own. In tasks like this, individuals may not have the ability necessary to complete the task successfully. In such cases, their response may either be a withdrawal from the task or self-defeating behaviors, like procrastination or, conversely, learning-oriented behaviors like feedback seeking, viewing rejection as learning opportunities and reaching out and seeking help from those who can provide resources to complete the task (Elliot, McGregor, & Gable, 1999; Rhodewalt, 1994). This response depends in part on individual goal orientations and in part on the goal content—learning versus performance-oriented goal instructions and feedback and goal monitoring conditions. Career development practitioners can play an important role in how job search-related tasks are structured, how goals are set, and the extent to which learning occurs for the job seeker while working on their own. Based on this research career development practitioners may improve employment outcomes if they consider individual trait goal orientation and how it influences job seeker performance when working on tasks on their own . Additionally, by generating a learning-oriented environment (i.e., the task of identifying hiring trends) for job seekers versus only a performance based one (i.e., number of applications submitted) may change the feedback-seeking behavior of performance-oriented individuals (Van Hooft & Noordzij, 2009).

This area of research is important in light of the new realities of how organizations recruit today. Organizations are harnessing the value of social networks to access talent and are minimizing their efforts with more traditional forms of recruitment (i.e., newspaper ads). The ability of a job seeker to access this hidden job market is essential for increasing the likelihood of finding a job in today’s market. If job seekers are not reaching out to their network, it puts them at a great disadvantage. Fortunately, self-regulation is one of the skills that can be taught and developed during the course of a lifetime. Dealing with failure, negative feedback, and engaging in continuous learning are very important skills throughout the job search process. Focusing career development research and practice on self-regulatory skills may provide significant benefits to the job seeker so that interventions can be introduced to increasing their networking ability.

	Variable	n mean	SD	1	2	3	4	5
1	Weak ties	142	9.84	42.87	1
2	Strong ties	142	9.60	19.52	.42*	1
3	Self-efficacy	156	64.43	18.54	.00	.12	1
4	Learning goal orientation	149	33.65	3.68	.09	.12	.37*	1
5	Performance goal orientation	149	30.46	5.04	.13	.10	−.02	−.09
6	Networking comfort	146	26.56	2.69	.03	.06	.06	.10

Variables	Coefficient SE	z	p	95% CI
Intercept	−3.4843 1.556	−2.238	.025*	[6.917 to 0.15]
Learning goal orientation	0.0862 0.034	2.478	.013*	[0.009 to 0.163]
Performance goal orientation	0.0646 0.023	2.663	.007**	[0.0122 to 0.114]
Job search self-efficacy	−0.0104 0.007	−1.422	.154	[−0.025 to 0.005]

Variables	Coefficient	SE	z	p	95% CI
Intercept	−2.110	0.925	−2.281	0.023	−3.893	−0.297
Job search self-efficacy	0.005	0.006	0.885	0.376	−0.007	0.018
Networking comfort	0.073	0.023	3.203	0.001	0.031	0.116
Learning goal orientation	0.047	0.029	1.599	0.110	−0.011	0.106
Performance goal orientation	0.049	0.020	2.457	0.014	0.009	0.087
Time unemployed	0.210	0.058	3.586	0.000	0.085	0.333
LGO × Time Unemployed	0.049	0.016	3.117	0.002	0.016	0.083

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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