Is More Feedback Seeking Always Better? Leader-Member Exchange Moderates the Relationship Between Feedback-Seeking Behavior and Performance

Abstract

Feedback is information made available to employees in their work environment, whereas feedback-seeking behaviors (FSBs) help employees to evaluate proactively whether their work has met performance standards and their behavior is considered appropriate. Prior studies have provided a perspective on how the feedback-seeking contexts affect the emergence and development of FSBs. In this study, we extend that perspective by investigating when FSBs affect job performance so that we can understand whether more feedback seeking is always better. Adopting the relational view of leadership, we hypothesize that the FSB-performance relationship should be stronger for employees with low leader-member exchange (LMX) and in groups with low aggregate LMX and low LMX differentiation. Using a multilevel research design and a sample of 379 teachers in 25 groups, we find support for most of our hypotheses. We discuss the implications of the study for the FSB and the proactive behavior literature and suggest avenues for future research.

A Dynamic Model of the Effects of Feedback-Seeking Behavior and Organizational Commitment on Newcomer Turnover

“…Third, research should explore the role of leader-member exchange in FSB’s dynamics, as it has been shown to be related to the feedback-seeking process (Z. Chen, Lam, & Zhong, 2007; Lam et al., 2017; Nifadkar, Tsui, & Ashforth, 2012). Finally, feedback valence (positive vs. negative) and focus (self vs. others) may be important to consider (Gong et al., 2017).…”

“…From a sensemaking perspective, socialization is a period during which newcomers make sense of who they are becoming as organizational members (Conroy & O’Leary-Kelly, 2014). During this period, FSB directed at supervisors is important because supervisors provide feedback that reflects organizational expectations (Dahling, Chau, & O’Malley, 2012; Lam, Peng, Wong, & Lau, 2017). By seeking such feedback, new employees obtain information about their performance, job expectations, and organizational norms and values, which facilitates social adjustment and integration in the organization (Ashforth, 2001; Ashforth, Harrison, & Corley, 2008; Ibarra & Barbulescu, 2010).…”

“…Third, this article bridges the streams of research on FSB and organizational commitment by revealing that the dynamics of their relationship is central to the understanding of newcomer turnover. Previous research mainly focused on the relationship between FSB and job performance (e.g., Dahling et al., 2012; Gong et al., 2017; Lam et al., 2017; Payne, Youngcourt, & Beaubien, 2007) but has rarely examined how FSB affects employee turnover (see Wanberg & Kammeyer-Mueller, 2000, for one exception). Based on sensemaking theory, we posit that a decline in FSB disrupts newcomers’ understanding of the firm’s values, goals, and norms, and hence weakens the ties with the organization, which in turn affects intended and actual turnover.…”

Does Proactive Personality Matter in Leadership Transitions? Effects of Proactive Personality on New Leader Identification and Responses to New Leaders and their Change Agendas

“…We used this scale, as it is typically regarded as "good proactivity." However, proactive behavior is not always appreciated; it can sometimes be regarded as "bad proactivity" (for details, see Burris, 2012;Fuller, Marler, Hester, & Otondo, 2015;Grant, Parker, & Collins, 2009;Lam, Peng, Wong, & Lau, 2015). In some situations, a proactive team might face a proactive new leader but find the situation untenable if the leader intentionally guides the team with bad proactivity.…”

When Staying Is Dissatisfying: Examining When and Why Turnover Cognitions Affect Stayers’ Career Satisfaction

“…We entered the control variables, including career satisfaction at T1 in the first step, the main effects in the second step, and the interaction term in the final step (Baron & Kenny, 1986). Since multiple two-way interactions increase the risk of Type I errors (Cohen et al, 2002), we tested our moderation hypotheses by assessing each two-way interaction independently, which is a common practice in the case of multiple interactions (e.g., Lam, Peng, Wong, & Lau, 2017; Zheng, Singh, & Chung, 2017). When the interaction terms were found to be significant ( p < .05) or to approach significance ( p < .10), we plotted the interaction, computed the simple slopes, and—for the continuous moderators—computed the regions of significance (Johnson-Neyman technique; Preacher, Curran, & Bauer, 2006).…”