disagreement
Model 1 includes all five predictor variables. Model 2 excludes the two that showed no significant effects in Model 1. Numbers in brackets are adjusted R 2 s.
As in Study 1, we again observe that the providers and recipients of feedback formed very different impressions about past performance. A new and important finding in this study is that feedback conversations did not merely fail to diminish provider-recipient disagreements about what led to strong and weak performance; they actually turned minor disagreements into major ones. Recipients made more self-enhancing and self-protective attributions following the performance discussion, believing more strongly than before that their successes were caused by internal factors (their ability, personality, effort, and attention) and their failures were caused by external factors (job responsibilities, employer expectations, resources provided, and bad luck). There were also modest disagreements regarding the quality and importance of different aspects of the recipient’s job performance, but these did not worsen following discussion. The most important source of disagreement between providers and recipients then, especially following the feedback conversation, was not about what happened, but about why it happened.
What led recipients of performance feedback to accept it as legitimate and helpful? The best predictor of feedback effectiveness was the extent to which the discussion was perceived as future focused. Unsurprisingly, feedback was also easier to accept when it was more favorable. As predicted, recipients were more likely to accept feedback when they and the feedback providers agreed more about what caused the past events. Greater attribution agreement, however, did not increase recipients’ intention to change. These findings suggest that reaching agreement on the causes of past performance is neither likely to happen (because feedback discussions widen causal attribution disagreement) nor is it necessary for fostering change. What does matter is the extent to which the feedback conversation focuses on generating new ideas for future success. We further explore the relations among all these variables following the reporting of Study 3.
Performance feedback serves goals other than improving performance. For example, performance reviews often serve as an opportunity for the feedback provider to justify promotion and compensation decisions. For the recipient, the conversation may provide an opportunity for image management and the chance to influence employment decisions. People may fail to distinguish between evaluation and improvement goals when providing and receiving feedback. In Study 2, the instructions were intended to be explicit in directing participants to the developmental goal of performance improvement, rather than accountability or rewards. Nevertheless, the providers’ wish to justify their evaluations and the recipients’ wish to influence them might have contributed to the differences we observed in attributions and in judgments about the feedback’s legitimacy. To address this concern, we added a page of detailed company guidelines that emphasized the primacy of the performance-improvement goal over the goals of expressing, justifying, or influencing evaluations. There were two versions of these guidelines, which did not differ in their effects.
Participants were 162 executives and MBA students enrolled in advanced Human Resources classes in Australia. An international mix of businesspeople, 74% said they grew up in Australia or New Zealand, 10% in Europe, 22% in Asia, and 7% other. (Totals sum to more than 100% because some participants indicated more than one.) Participants averaged 39 years of age, ranging from 27 to 60. Females comprised 37% of the participants.
Participants read the same scenario and instructions as in Study 2, with an added page of guidelines for giving developmental feedback ( S8 Text ). They then completed the same post-discussion questionnaires used for the pre-post group of Study 2, minus the ratings of performance quality and importance for various aspects of the job, which showed no effects in Study 2. (The full text of the questionnaires is provided in S9 and S10 Texts). Taken together, these modifications kept the procedure to about the same length as in Study 2. This study was approved by the Institutional Review Board at the University of Melbourne. Written consent was obtained.
As in Study 2, we calculated the sum of the percentages of attributions assigned to internal causes (ability and personality + effort and attention), applying an arcsine transformation. As before, we analyzed the internal attributions measure with a mixed-model ANOVA treating each dyad as a unit. There were two within-dyads variables: role (provider or recipient), and outcomes (successes or failures) and one between-dyads variable (guideline version ). There were no effects involving guideline version (all F < 1). The main effects of role ( F (1, 79) = 50.12, p < .001, η 2 = .39) and outcomes ( F (1, 79) = 113.8, p < .001, η 2 = .59) and the interaction between them ( F (1, 79) = 86.34, p < .001, η 2 = .52) are displayed in Fig 3 , along with the parallel post-feedback results from the previous two studies. As in Study 2, the two parties’ post-discussion attributions were well apart on both successes and, especially, failures ( t (80) = 3.3 and 9.4 respectively, both p ≤ .001). Again, the correlations between the provider’s and the recipient’s post-conversation performance attributions across dyads were not significant for either successes ( r (79) = -.04, p > .69) or failures ( r (79) = -.13, p > .23) suggesting that conversation does not lead the dyad to a common understanding of what led to good or poor performance.
Results are shown by role (provider vs. recipient of feedback) and valence/outcomes (positive feedback for successes vs. negative feedback for failures), following feedback conversation. Error bars show standard errors.
We conducted regression analyses of the recipient’s feedback acceptance and intention to change as in Study 2. The regression models included three predictors: future focus, attribution disagreement, and feedback favorability. Results, shown in Table 4 , replicated our Study 2 finding that future focus is the best predictor of both feedback acceptance and intention to change. As before, attribution disagreement predicted lower acceptance, but in this study it also predicted less intention to change. We again found that feedback favorability ratings were associated with greater acceptance, but this time, not with intention to change. Recipients and providers were again significantly correlated in their judgments of how future focused the conversation was ( r (79) = .299, p = .007).
Feedback Acceptance [.373] | Intention to Change [.323] | |||||
---|---|---|---|---|---|---|
Beta | (77) | Beta | (77) | |||
Future focus | .411 | 4.432 | < .001 | .549 | 5.697 | .001 |
Attribution disagreement | -.193 | -2.131 | .036 | -.198 | -2.105 | .039 |
Favorability | .284 | 3.017 | .003 | -.050 | -.516 | .607 |
Numbers in brackets are adjusted R 2 s.
Future focus, as perceived by the recipients of feedback, was once again the strongest predictor of their acceptance of the feedback and the strongest predictor of their intention to change. Conversely, attribution disagreement between the provider and recipient of feedback was associated with lower feedback acceptance and weaker intention to change. As in Studies 1 and 2, recipients made more internal attributions for successes than providers did and, especially, more external attributions for failures. The added guidelines in this study emphasizing performance-improvement goals over evaluative ones did not alleviate provider-recipient attribution differences. Indeed, those differences were considerably larger in this study than in the previous one and were more similar to those seen in Study 1 (see Fig 3 ).
The strongest predictor of feedback effectiveness is the recipient’s perception that the feedback conversation focused on plans for the future rather than analysis of the past. We seek here to elucidate the relationship between future focus and feedback effectiveness by looking at the interrelations among the three predictors of effectiveness we studied: future focus, attribution disagreement, and feedback favorability.
The analyses that follow include data from all participants who were asked for ratings of future focus, namely those in Study 3 and in the pre-post group of Study 2. We included study as a variable in our analyses; no effects involving the study variable were significant. Nonetheless, because the two studies drew from different samples and used slightly different methods, inferential statistics could be impacted by intraclass correlation within each study. Therefore, we also tested for study-specific differences in parameter estimates using hierarchical linear modeling [ 58 , 59 ]. No significant differences between studies emerged, confirming the appropriateness of combining the data. (The HLM results are provided in S2 Analyses .)
The association between future focus and feedback effectiveness could be mediated by the effects of attribution disagreement and/or feedback favorability. Specifically, it could be that perceiving the conversation as more future focused is associated with closer agreement on attributions or with perceiving the feedback as more favorable, and one or both of those latter two effects leads to improved feedback effectiveness. Tests of mediation, following the methods of Kenny and colleagues [ 60 ], suggest otherwise (see Fig 4 ). These analyses partition the total associations of future focus with feedback acceptance and with intention to change into direct effects and indirect effects. Indirect effects via reduced attribution disagreement were 6.2% of the relation of future focus to feedback acceptance and 2.2% to intention to change. Indirect effects via improved perceptions of feedback favorability were 20.8% of the relation of future focus to feedback acceptance and 4.5% to intention to change. Thus, there is little to suggest that closer agreement on attributions or improved perceptions of feedback favorability account for the benefits of future focus on feedback effectiveness.
The two feedback effectiveness measures are feedback acceptance and intention to change. Following Kenny (2018), standardized regression coefficients are shown for the relations between future focus and two hypothesized mediators, attribution disagreement and feedback favorability ( a ), the mediators and the feedback effectiveness measures controlling for future focus ( b ), future focus and the effectiveness measures ( c ), and future focus and the effectiveness measures controlling for the mediator ( c′ ). The total effect ( c ) equals the direct effect ( c′ ) plus the indirect effect ( a · b ). Data are from Studies 2 and 3. a p = .072; * p = .028; ** p < .001.
Future focus might have synergistic or moderating effects. In particular, we hypothesized that perceiving the conversation as more future focused may moderate the negative impact of attribution disagreement on feedback effectiveness. Alternatively, future focus may be especially beneficial when agreement about attributions is good, or when attribution differences are neither so big that they cannot be put aside, nor so small that the parties see eye to eye even when they focus on the past. Similarly, future focus may be especially beneficial when feedback is most unfavorable to the recipient, or when it’s most favorable, or when it is neither so negative that the recipients can’t move past it, nor so positive that the recipients accept it even when the conversation focuses on the past.
We conducted regression analyses with feedback acceptance and intention to change as dependent variables and future focus, feedback favorability, attribution disagreement, and their first-order interactions as predictors. Because some plausible interactions are nonlinear, we defined low, intermediate, and high values for each of the three predictor variables, dividing the 198 participants as evenly as possible for each. We then partitioned each predictor into linear and quadratic components with one degree of freedom each. With linear and quadratic components of three predictors plus a binary variable for Study 2 vs. Study 3, there were seven potential linear effects and 18 possible two-way interactions. We used a stepwise procedure to select which interactions to include in our regressions, using an inclusion parameter of p < .15. Results are shown in Table 5 .
Feedback acceptance | Intention to change | |||||
---|---|---|---|---|---|---|
Future focus—Linear | 0.487 | 5.09 | < .001 | 0.639 | 11.51 | < .001 |
Future focus—Quadratic | 0.024 | 0.40 | .687 | -0.068 | -1.27 | .206 |
Feedback favorability—Linear | 0.268 | 4.36 | < .001 | 0.096 | 1.74 | .083 |
Feedback favorability—Quadratic | -0.067 | -1.12 | .265 | -0.029 | -0.55 | .584 |
Attribution disagreement—Linear | -0.226 | -3.57 | .001 | -0.148 | -2.60 | .010 |
Attribution disagreement—Quadratic | -0.094 | -1.62 | .108 | -0.088 | -1.69 | .093 |
Study 2 vs. 3 | 0.073 | 1.13 | .259 | -0.078 | -1.34 | .182 |
Future focus—Linear x Feedback favorability—Linear | -0.119 | -1.91 | .057 | -0.116 | -2.09 | .038 |
Future focus—Linear x Attribution disagreement—Linear | -0.095 | -1.83 | .070 | |||
Future focus—Linear x Study | -0.136 | -1.46 | .145 | |||
Feedback favorability–Quadratic x Attribution disagreement–Quadratic | 0.084 | 1.60 | .112 |
Models include all main effects and those first-order interactions that met an entry criterion of p < .15, plus data source (Study 2 vs. Study 3). Statistically significant values are underlined.
Future focus interacted with feedback favorability—marginally for feedback acceptance and significantly for intention to change. As shown in Fig 5 , recipients who gave low or intermediate ratings for future focus accepted the feedback less when it was most negative ( t (128) = 5.21, p < .001) and similarly, reported less inclination to change ( t (128) = 3.23, p = .002). In contrast, the recipients who rated the feedback discussion as most future focused accepted their feedback and indicated high intention to change at all levels of feedback favorability. These patterns suggest that perceiving future focus moderates the deleterious effect of negative feedback on feedback effectiveness.
Results for each measure of feedback effectiveness are shown by three levels of perceived future focus and three levels of perceived feedback favorability. Error bars show standard errors. Data are from Studies 2 and 3.
On the other hand, we find no evidence that future focus moderates the negative effect of attribution disagreement on feedback effectiveness. Future focus did interact marginally with attribution disagreement for intention to change. However, the benefits of perceiving high vs. low future focus may, in fact, be stronger when there is closer agreement about attributions: The increase in intention to change between low and high future focus groups was 2.30 with high disagreement, 2.37 with intermediate disagreement, and 3.24 in dyads with low disagreement, on a scale from 1 to 7.
Regression-tree analyses can provide additional insights into the non-linear relations among variables [ 61 ], with a better visualization of the best and worst conditions to facilitate feedback acceptance and intention to change. These analyses use the predictors (here, future focus, attribution disagreement, and feedback favorability) to divide participants into subgroups empirically, maximizing the extent to which values on the dependent measure are homogeneous within subgroups and different between them. We generated regression trees for each of our two effectiveness measures, feedback acceptance and intention to change. Fig 6 shows the results, including all subgroups (nodes) with N = 10 or more.
The trees depict the effects of future focus, attribution disagreement, and feedback favorability on our two measures of feedback effectiveness. The width of branches is proportional to the number of participants in that branch. Node 0 is the full sample of 198. Values on the X axis are standardized values for each dependent measure. Data are from Studies 2 and 3.
Both trees show that future focus is the most important variable, dividing into lower and higher branches at Nodes 1 and 2, and further distinguishing highest-future groups at Nodes A8 and B6. These representations also reinforce the conclusion that perceived future focus does not operate mainly via an association with more positive feedback or with better agreement on attributions. However, attribution disagreement does play a role, with more agreement leading to better acceptance of feedback and greater intention to change, as long as future focus is at least moderately high (Nodes A3 vs. A4 and B7 vs. B8). (The lack of effect at Node B6 is likely a ceiling effect.) Unfavorable feedback makes matters worse under adverse conditions: when future focus is low (Nodes B3 vs. B4) or when future focus is moderate but attribution disagreement is large (nodes A5 vs. A6).
Our research was motivated by a need to understand why performance feedback conversations do not benefit performance to the extent intended and what might be done to improve that situation. We investigated how providers and recipients of workplace feedback differ in their judgements about the causes of performance and the credibility of feedback, and how feedback discussions impact provider-recipient (dis)agreement and feedback effectiveness. We were particularly interested in how interpretations of past performance, feedback acceptance, and intention to change are affected by the recipient’s perception of temporal focus, that is, the extent to which the feedback discussion focuses on past versus future behavior.
Management theorists typically advocate evaluating performance relative to established goals and standards, diagnosing the causes of substandard performance, and providing feedback so that people can learn from the past [ 19 ]. They also posit that feedback recipients must recognize there is a problem, accept the feedback as accurate, and find the feedback providers fair and credible in order for performance feedback to motivate improvement [ 7 , 14 , 35 ]. Unfortunately, we know that performance feedback often does not motivate improvement [ 4 ]. Our research contributes in several ways to understanding why that is and how feedback conversations might be made more effective.
Decades of attribution theory and research have elucidated the biases thought to produce discrepant explanations for performance between the providers and recipients of feedback. We show that for negative feedback, these discrepancies are prevalent in the workplace. We also show that larger attribution discrepancies are associated with greater rejection of feedback and, in our performance review simulations, with weaker intention to change. These findings support recent research and theory linking performance feedback, work-related decision making, and attribution theory: Instead of changing behavior in response to mixed or negative feedback, people make self-enhancing and self-protecting attributions and judgements they can use to justify not changing [ 8 , 14 , 62 ].
Our research suggests that the common practice of discussing the employees’ past performance, with an emphasis on how and why outcomes occurred and what that implies about the employees’ strengths and weaknesses, can be counterproductive. Although the parties to a feedback discussion may agree reasonably well about which goals and standards were met or unmet, they are unlikely to converge on an understanding of the causes of unmet goals and standards, even with engaged give and take. Instead, the feedback conversation creates or exacerbates disagreement about the causes of performance outcomes, leading feedback recipients to take more credit for their successes and less responsibility for their failures. This suggests that feedback conversations that attempt to diagnose past performance act as another form of self-threat that increases the self-serving bias [ 33 ]. Surely this runs counter to what the feedback provider intended.
At the same time, we find that self-serving attributions need not stand in the way of feedback acceptance and motivation to improve. A key discovery in our research is that the more recipients feel the feedback focuses on next steps and future actions, the more they accept the feedback and the more they intend to act on it. In fact, when feedback is perceived to be highly future focused, feedback recipients respond as well to predominantly negative feedback as to predominantly positive feedback. Future focus does not nullify self-serving attributions and their detrimental effects [see also 63 ], but it does enable productive feedback discussions despite them.
We used two complementary research methods. Study 1 used a more naturalistic and thus more ecologically valid method, collecting retrospective self-reports from hundreds of managers about actual feedback interactions in a wide variety of work situations [see 64 ]. Studies 2 and 3 used a role-play method that allowed us to give all participants identical workplace performance information, a good portion of which was undisputed and quantitative. With that design, response differences between the providers and recipients of feedback are due entirely to role, unconfounded by differences in knowledge and experience.
What role plays cannot establish is the magnitude of effects in organizational settings. Attribution misalignment and resistance to feedback might easily be much stronger in real workplace performance reviews where it would be rare for the parties to arrive with identical, largely unambiguous information. Moreover, managers’ investment in the monetary and career outcomes of performance reviews might lead feedback recipients to feel more threatened than in a role play and thus to disagree even more with unfavorable feedback. On the other hand, the desire to maintain employment and/or to maintain good relationships with supervisors might motivate managers to re-assess their past achievements, to change their private attributions, and to be more accepting of unfavorable feedback. Data from our role-play studies may not speak to the magnitude of resistance to feedback in work settings (although our survey results suggest it’s substantial), but they do show that feedback acceptance is increased when the participants perceive their feedback to be focused on the future.
There are few research topics more important to the study of organizations than performance management. Feedback conversations are a cornerstone of most individual and team performance management, yet there is still much we do not know about what should be said, how, and why. Based on research into the motivational advantages of prospective thinking, we hypothesized that feedback discussions perceived as future focused are the most effective kind for generating acceptance of feedback and fostering positive behavior change. Our findings support that hypothesis. The present research contributes to the literature on prospection by highlighting the role of interpersonal interactions in facilitating prefactual thinking and any associated advantages for goal pursuit [ 39 , 43 – 45 , 63 , 65 ]. In this section we suggest three lines of future research: (a) field studies and interventions; (b) research into the potential role of self-beliefs; and (c) exploration of the conversational dynamics associated with feedback perceived as past vs. future focused.
Testing feedback interventions in the workplace and other field settings is an important future step toward corroborating, elaborating, or correcting our findings. It will be necessary to develop effective means to foster a more future-focused style of feedback. Then, randomized controlled trials that contrast future-focused with diagnostic feedback can demonstrate the benefits that may accrue from focusing feedback more on future behavior and less on past behavior. Participant evaluations of the feedback discussions can be supplemented by those of neutral observers. Such evaluations are directly relevant to organizational goals, including employee motivation, positive supervisor-supervisee relations, and effective problem solving. Assessing subsequent behavior change and job performance is both important and complicated for evaluating feedback effectiveness: Seeing intentions through to fruition depends on many factors, including individual differences in self-regulation [ 66 , 67 ] and factors beyond people’s control, such as competing commitments, limited resources, and changing priorities [ 68 – 71 ]. Nevertheless, the ultimate proof of future-focused feedback will lie in performance improvement itself.
If future focus enhances feedback effectiveness, it may do so via self-beliefs. Growth mindset and self-efficacy, for example, are self-beliefs that influence how people think about and act on the future. Discussions that focus on what people can do in the future to improve performance may encourage people to view their own behavior as malleable and to view better results as achievable. If future focus helps people access this growth mindset, it should orient them toward mastering challenges and improving the self for the future: Whereas people exercise defensive self-esteem repair when in a fixed mindset, they prefer self-improvement when accessing a growth mindset [ 72 , 73 ]. Similarly, feedback conversations that focus on ways the feedback recipient can attain goals in the future may enhance people’s confidence in their ability to execute the appropriate strategies and necessary behaviors to succeed. Such self-efficacy expectancies have been shown to influence the goals people select, the effort and resources they devote, their persistence in the face of obstacles, and the motivation to get started [ 74 , 75 ]. Thus, research is needed to assess whether future focus alters people’s self-beliefs (or vice versa; see below) and if these, in turn, impact people’s acceptance of feedback and intention to change.
We found sizeable variation in the extent to which dyads reported focusing on the future. Pre-existing individual differences in self-beliefs may contribute to that variation. Recent research, for example, finds that professors with more growth mindsets have students who perform better and report being more motivated to do their best work [ 76 ]. In the case of a feedback conversation, we suspect that either party can initiate thinking prospectively, but both must participate in it to sustain the benefits.
Unlike most studies of people’s reactions to mixed or negative feedback, our studies use face-to-face, real-time interaction, that is to say, two people in conversation. Might conversational dynamics associated with future-focused feedback contribute to its being better accepted and more motivating than feedback focused on the past? Do managers who focus more on the future listen to other people’s ideas and perspectives in ways that are perceived as more empathic and nonjudgmental? Do these more prospective discussions elicit greater cooperative problem solving? Research on conversation in the workplace is in its early stages [ 77 ], but some studies support the idea that high quality listening and partner responsiveness might reduce defensiveness, increase self-awareness, or produce greater willingness to consider new perspectives and ideas [ 78 , 79 ].
Our studies provide the first empirical evidence that managers can make feedback more effective by focusing it on the future. Future-focused feedback, as we define it, is characterized by prospective thinking and by collaboration in generating ideas, planning, and problem-solving. We assessed the degree of future focus by asking participants to rate the extent to which the feedback discussion focused on future behavior, the two parties spent time generating new ideas for next steps, and the conversation centered on how to make the recipient successful. This differs greatly from feedback research that distinguishes past vs. future orientation “using minimal rewording of each critique comment” (e.g., you didn’t always demonstrate awareness of… vs. you should aim to demonstrate more awareness of…) [ 80 p. 1866].
Because future-focused feedback is feedback, it also differs from both advice giving and “feedforward” (although it might be advantageous to incorporate these): It differs from Kluger and Nir’s feedforward interview, which queries how the conditions that enabled a person’s positive work experiences might be replicated in the future [ 81 ], and from Goldsmith’s feedforward exercise, which involves requesting and receiving suggestions for the future, without discussion or feedback [ 82 ].
The scenario at the very start of this article asks, “What can Chris say to get through to Taylor?” A future-focused answer might include the following: Chris first clarifies that the purpose of the feedback is to improve Taylor’s future performance, with the goal of furthering Taylor’s career. Chris applauds Taylor’s successes and is forthright and specific about Taylor’s shortcomings, while avoiding discussion of causes and explanations. Chris signals belief that Taylor has the motivation and competence to improve [ 83 ]. Chris then initiates a discussion in which they work together to develop ideas for how Taylor can achieve better outcomes in the future. (For a more detailed illustration of a future-focused conversation, see S11 Text .)
Our research supports the intriguing possibility that the future of feedback could be more effective and less aversive than its past. Performance management need not be tied to unearthing the determinants of past performance and holding people to account for past failures. Rather, performance may be managed most successfully by collaborating with the feedback recipient to generate next steps, to develop opportunities for interesting and worthwhile endeavors, and to enlarge the vision of what the recipient could accomplish. Most organizations and most managers want their workers to perform well. Most workers wish to succeed at their jobs. Everyone benefits when feedback discussions develop new ideas and solutions and when the recipients of feedback are motivated to make changes based on those. A future-focused approach to feedback holds great promise for motivating future performance improvement.
S1 analyses, s2 analyses, acknowledgments.
For helpful comments on earlier drafts of this paper, we are grateful to Pino Audia, Angelo Denisi, Nick Epley, Ayelet Fishbach, Brian Gibbs, Reid Hastie, Chris Hsee, Remus Ilies, David Nussbaum, Jay Russo, Paul Schoemaker, William Swann, and Kathleen Vohs.
This research received funding from the Melbourne Business School while the first three authors were either visiting (JG, JK) or permanent (IOW) faculty there. While working on this research, the first two authors (JG, JK) also worked as owners and employees of management consulting firm Humanly Possible. Humanly Possible provided support in the form of salaries and profit-sharing compensation for authors JG and JK, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the “author contributions” section.
PONE-D-20-05644
The future of feedback: Motivating performance improvement
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Reviewer #1: 1. I enjoyed reading this manuscript, but it appears to be unnecessary long in parts and readability would benefit of a more concise style. I would recommend condensing some parts, for example in the methods section for study 2 was overly long and lacked clarity in parts. The description of the second questionnaire was a little confusing in terms of the consistency in how items were measured and the hypothesis was not clear.
2. In the ethics statement for Study 1 (line 184), please explain the rationale behind the waiver of consent.
3. Procedure (line 187) please give details of the survey platform used.
4. Results -Please include the number of participants in each group.
5. Please comment on what normality checks were performed to assess the distribution of the data.
6. Line 470, correlations are discussed but I can’t see a table to support these.
7. The discussion did not address the results in relation to previous literature and lacked a theoretical explanation of the findings (See for example ‘Korn CW, Rosenblau G, Rodriguez Buritica JM, Heekeren HR (2016) Performance Feedback Processing Is Positively Biased As Predicted by Attribution Theory. PLoS ONE 11(2)’ for a discussion of attributional style and self-serving bias. I recommend some rewrite of the discussion with more reference to theory.
8. Some acknowledgement of the effect of individual differences in self-regulation would be useful to include as this may influence how feedback is received in terms of attributions. See for example, ‘Donovan, JJ, Lorenzet, SJ, Dwight, SA, Schneider, D. The impact of goal progress and individual differences on self‐regulation in training. J Appl Soc Psychol. 2018; 48: 661– 674’.
9. The suggestions for improvement at the end of the study would be better to be condensed to give a brief suggestion of methods.
Reviewer #2: The paper reports an interesting and comprehensive work about a relevant issue in organizational psychology. Both the theoretical frame and the applied methodology are original and thorough, though the use of role-play raises some doubts about the robustness of the results (some concerns are raised by the authors themselves (lines 752-760) ). This is, in my opinion, the main limitation of studies 2 and 3. I would suggest that the authors insert a wider reasoning about the choice of using this method to collect their data and the pros and cons.
In the "General Discussion" paragraph the authors state that "We investigated the sources of agreement and disagreement between feedback provider and recipient" (lines 712-713). I strongly suggest that this sentence is being modified, since it doesn't describe the aim nor the results in Study 1 correctly.
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Reviewer #1: No
Reviewer #2: Yes: Federica Biassoni
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12 May 2020
Please see uploaded document Response to Reviewers. Text copied here.
Response to Reviewers
We wish to thank the reviewers for their very helpful and constructive comments. We especially appreciate the clarity and specificity with which they framed their suggestions. Below we respond to each reviewer recommendation.
Reviewer #1:
1. I enjoyed reading this manuscript, but it appears to be unnecessary long in parts and readability would benefit of a more concise style. I would recommend condensing some parts, for example in the methods section for study 2 was overly long and lacked clarity in parts. The description of the second questionnaire was a little confusing in terms of the consistency in how items were measured and the hypothesis was not clear.
We revised the methods section for Study 2 (former lines 274-279; 285-414, revision lines 276-281; 299-402). The new version is a full page shorter and, in line with the reviewer’s suggestion, we believe this more concise version is now more readable. It includes a revised description of the post-discussion questionnaires (former 346-367; revision 350-361), clarifying the sequence and types of questions provided to each group. It also includes revisions, mainly in the Design section (former 387-414; revision lines 377-402) to clarify how the various measures related to our hypotheses.
Study 1 was approved by the Institutional Review Board at the University of Chicago, which waived the requirement for written consent as was its customary policy for studies judged to be minimal risk, involving only individual, anonymized survey responses. Their decision cited US Code 45 CFR 46.101(b). Citing the code in our manuscript seemed overly legalistic, but we have added the rest of the rationale to the ethics statement (former lines 184-185; revision 184-186).
We now identify the platform as Cogix ViewsFlash (revision line 188).
We have added the requested information for Study 1 (revision lines 214-215). Following up on the suggestion, we also made it easier to locate the corresponding information for Study 2 (revision lines 316-317).
The general consensus is that the analyses we use, i.e. ANOVA and linear regression, are generally quite robust with regard to moderate violations of normality with Ns on the order of ours (e.g., Blanca, Alarcón, Arnau, Bono, & Bendayan, Psichothema, 2017; Schmidt & Finana, Journal of Clinical Epidemiology, 2018; Ali & Sharma, Journal of Econometrics, 1996; Schmider, Ziegler Danay, Beyer, & Bühner, Methodology, 2010). Nevertheless, we used an arcsine transformation on the variables a priori most likely to suffer from systematic deviations, namely the attribution proportions. Most authors recommend checking for major deviations from normality by plotting model-predicted values against residuals and against the normal distribution (using P-P or Q-Q plots). We did that for our analyses (graphs attached), and found no troublesome deviations, with the possible exception of one variable of minor importance to our main results or theory, namely performance quality ratings for successes in Study 2. We note in the paper that that variable may suffer from ceiling effects (former 468-469, revision 456-457). We did not add a discussion of normality to the paper because of the increased length and complexity that would involve and because it’s seldom an issue of concern with data and analyses like ours. However, we could include the graphs we’ve attached here as supplemental material if you tell us you would like us to do so.
Thank you for alerting us to this inadvertent omission. We now include complete correlation tables for all the variables analyzed in each Study in the supplemental materials: S2 Table for Study 1 (revision lines 224-225) and S11 Tables for Studies 2 and 3 separately and combined (revision lines 458-459), with provider-recipient correlations identified by color shading. (S2 was formerly the dataset for Study 1, but now data from all three studies are contained in S17.)
To better address our results in relation to previous attribution literature and theory, we have revised former lines 723-740 in the General Discussion. Now we more clearly discuss our findings in relation to self-serving bias, self-threat, and both historical and more recent formulations of attribution theory, including the helpful reference the reviewer provided (revision lines 708-735). We have also added a brief discussion of how our results relate to previous literature on future thinking (revision lines 760-762). We attempted to minimize redundancy with the Introduction section. The new material includes several new references.
We added mention in the General Discussion of individual differences in self-regulation, citing two references, including the one helpfully provided by Reviewer #1 (revision line 776). Additionally, we reworded former lines 798-799 (revision lines 793-794) to make it clearer that we are acknowledging individual differences there as well.
We condensed former lines 828-846 from 19 lines to 8 lines (revision lines 823-830), referring the interested reader to new Supporting Information S16 Text for the expanded version. We trust this solution meets the recommendation for a brief suggestion of methods, while also satisfying the interests of those seeking more detail.
Reviewer #2:
1. The paper reports an interesting and comprehensive work about a relevant issue in organizational psychology. Both the theoretical frame and the applied methodology are original and thorough, though the use of role-play raises some doubts about the robustness of the results (some concerns are raised by the authors themselves (lines 752-760)). This is, in my opinion, the main limitation of studies 2 and 3. I would suggest that the authors insert a wider reasoning about the choice of using this method to collect their data and the pros and cons.
We now include a wider reasoning about our choice to use a role-play method and the pros and cons. The new version comprises revision lines 282-298. (We also revised the subsequent paragraph for increased clarity, given the insertion of the new paragraph about the role-play method.)
2. In the "General Discussion" paragraph the authors state that "We investigated the sources of agreement and disagreement between feedback provider and recipient" (lines 712-713). I strongly suggest that this sentence is being modified, since it doesn't describe the aim nor the results in Study 1 correctly.
Thank you for your careful reading. We have re-written that sentence to more accurately capture the results of Study 1 as well as the other two studies (revised lines 697-700).
[Figures attached--please see uploaded document Response to Reviewers.]
Submitted filename: Response to Reviewers.docx
27 May 2020
The future of feedback: Survey and role-play investigations into causal attributions, feedback acceptance, motivation to improve, and the potential benefits of future focus for increasing feedback effectiveness in the workplace
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The future of feedback: Motivating performance improvement through future-focused feedback
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IMAGES
COMMENTS
Similarly, further research might explore the (relatively rare) experiences of marginalised and seldom-heard groups involved in research. Payment for public involvement in research remains a contested issue with strongly held positions for and against; it would be helpful to further explore the value research partners and researchers place on ...
Areas with little existing knowledge requiring further research may be identified during improvement activities, which in turn can form research questions for further study. QI and research also intersect in the field of improvement science, the academic study of QI methods which seeks to ensure QI is carried out as effectively as possible. 34
Creating a Culture of Continuous Improvement. by. Aravind Chandrasekaran. and. John S. Toussaint. May 24, 2019. michellealbert/Getty Images. Summary. A number of health systems have scored ...
Research on school effectiveness and improvement conducted over the past few decades demonstrates the complex, dynamic nature of learning environments (e.g., Reynolds et al., 2014). Researchers in these fields argue that various factors within schools and broader education systems and outside school impact students' learning outcomes ( Lareau ...
There are limitations to all sampling strategies and to qualitative research, in particular. The strength of this method was that the sample selection used input from a pool of reognized experts in the organization, delivery, and improvement of health care. Even with a pool of recognized experts, it is reasonable to expect that some high performing micro-systems were overlooked. It was also ...
As a result of the frustration with the dominant "What Works" paradigm of large-scale research-based improvement, practitioners, researchers, foundations, and policymakers are increasingly embracing a set of ideas and practices that can be collectively labeled continuous improvement (CI) methods. This chapter provides a comparative review ...
Improvement research projects which are typically well-designed and with some form of control groups and comparators can both address improvement priorities and generate generalisable research data at the same time. ... N.S.' research is further supported by the ASPIRES research programme (Antibiotic use across Surgical Pathways ...
Research and quality improvement provide a mechanism to support the advancement of knowledge, and to evaluate and learn from experience. The focus of research is to contribute to developing knowledge or gather evidence for theories in a field of study, whereas the focus of quality improvement is to standardize processes and reduce variation to improve outcomes for patients and health care ...
Rather, those who are focused on in improvement are part of a continuum and are driven by a range of goals from driving and demonstrating local improvements to a focus on attributing these improvements to QI methods that can be generalized and spread, as illustrated in Table 1, which also describes differences in incentives, discussed further ...
This final chapter concludes with the four research questions (sections 8.1.1 to 8.1.4) and provides general insights from across the study (section 8.1.5). ... Conclusion and suggestions for further research. In: Big Data to Improve Strategic Network Planning in Airlines. Schriftenreihe der HHL Leipzig Graduate School of Management. Springer ...
Areas with little existing knowledge requiring further research may be identified during improvement activities, which in turn can form research questions for further study. QI and research also intersect in the field of improvement science, the academic study of QI methods which seeks to ensure QI is carried out as effectively as possible.34
First, a search using the exact terms ("evidence based quality improvement," "evidence-based quality improvement," or "EBQI") was employed to identify publications published to March 2020 that explicitly refer to EBQI in the title, abstract, or keyword of the publication (i.e., the elements that are searchable in research databases).
Improvement (defined broadly as purposive efforts to secure positive change) has become an increasingly important activity and field of inquiry within healthcare. This article offers an overview of possible methods for the study of improvement interventions. The choice of available designs is wide, but debates continue about how far improvement efforts can be simultaneously practical (aimed at ...
Recommendations for future research should be: Concrete and specific. Supported with a clear rationale. Directly connected to your research. Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.
enrich school improvement research and help further development thereof. T aken . together, they also provide an o verview that can be used to systematically select the .
Quality improvement collaboratives are widely used to improve health care in both high-income and low and middle-income settings. ... Further research is needed to determine whether certain contextual factors related to capacity should be a precondition to the quality improvement collaborative approach and to test the emerging programme theory ...
At that moment a teacher can further explore the student's thinking, signaling both the expectation that struggling will produce learning, and that the student is capable of thinking further about the problem. ... Generating Improvement Through Research and Development in Education Systems. Science 340, 317-319 (2013). DOI:10.1126/science ...
Quality improvement and implementation science in cancer care: Identifying areas of synergy and opportunities for further integration. ... Quality improvement, clinical research, and quality improvement research--opportunities for integration. Pediatr Clin North Am. 2009; 56 (4):831-841 [Google Scholar] 7.
Integration of continuous improvement strategies with Industry 4.0: a systematic review and agenda for further research - Author: S. Vinodh, Jiju Antony, Rohit Agrawal, Jacqueline Ann Douglas The purpose of this paper is to provide a review of the history, trends and needs of continuous improvement (CI) and Industry 4.0.
As has been implied throughout these first three suggestions, the significance of technology in education centres on issues of change, progress and improvement. Indeed, most people are drawn to digital technology as a research topic precisely because of its association with progress, transformation and the allure of 'the new'.
Continuous quality improvement (CQI), an approach used extensively in industrial and manufacturing sectors, has been used in the health sector. Despite the attention given to CQI, uncertainties remain as to its effectiveness given the complex and diverse nature of health systems. ... Further research into the effectiveness of CQI interventions ...
The data from these 37 managers were excluded from further analysis, leaving samples of 96, 92, 91, and 103 in the provider-positive, provider-negative, recipient-positive, and recipient-negative conditions, respectively. ... we know that performance feedback often does not motivate improvement . Our research contributes in several ways to ...
Toward a Further Understanding of and Improvement in Measurement Invariance Methods and Procedures. Robert J. Vandenberg View all authors and ... In the hopes of stimulating further research on these topics, ideas are presented as to how this research may be undertaken. Get full access to this article. View all access and purchase options for ...