JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
2020, VOL. 40, NOS. 3–4, 233–248
https://doi.org/10.1080/01608061.2020.1784351

The Effects of Feedback Modality on Performance
Garrett D. Warrilow, Douglas A. Johnson, and Lauren M. Eagle
Department of Psychology, Western Michigan University, Kalamazoo, Michigan, USA
ABSTRACTKEYWORDS

Giving employees information about their performance is
a common method for employers seeking to improve or change
performance. Today, internet- and computer-delivered feed­back is often provided through e-mails, text messages, andvideo meetings. While feedback has continued to evolve bothwithin and across organizations, little work has been done toassess how various modalities might impact its effectiveness.
Understanding the effective delivery of feedback is importantfor supervisors looking to conduct evaluations with remoteworkers or simply save time as emerging technology presents
new delivery options. This study explored and evaluated therelationship between the modality in which objective feedback
is delivered, and the differential effects it produced on perfor­
mance of a check entering task. This experiment wasa laboratory study employing a between-group repeated mea­sures design with random assignment to one of the following
four experimental conditions; 1) no feedback, 2) computerdelivered feedback, 3) feedback via cell phone text message
and, 4) feedback via face-to-face interaction. Results demon­
strated the superiority of face-to-face feedback delivery andsuggest interesting patterns for other feedback modalities.

Feedback; remotesupervision; computerdelivered feedback; text
message feedback; face-toface feedback

Feedback has long remained one of the most common organizational
approaches for improving employee performance, fostering learning anddevelopment, and enhancing job satisfaction across many business and indus­
try settings (Andiola, 2014; Baker, Perreault, Reid, & Blanchard, 2013; Mulder& Ellinger, 2013; Park, Johnson, Moon, & Lee, 2019). Techniques for provid­
ing feedback can range from annual performance reviews in whicha supervisor gives a summary of the worker’s performance over the past year
during a face-to-face meeting to more comprehensive methods such as pro­viding information on an ongoing and real-time basis (e.g., supplying counters
for units packed, widgets built, or similar tasks). Feedback is the most fre­quently used independent variable in the Journal of Organizational Behavior
Management’s published works, accounting for 65%, 71%, and 68% of thestudies published across the first three decades of publication (1977–2009;CONTACT Douglas A. Johnson
49019© 2020 Taylor & Francis

behavioranalyst@gmail.com

Western Michigan University, Kalamazoo, MI234

G. D. WARRILOW ET AL.

Balcazar, Shupert, Daniels, Mawhinney, & Hopkins, 1989; Nolan, Jarema, &Austin, 1999; VanStelle et al., 2012).
It has frequently been recognized that feedback does not always lend itself tosimple analyses when trying to pinpoint its behavioral function (AljadeffAbergel, Peterson, Wiskirchen, Hagen, & Cole, 2017; Slowiak & Lakowske,2017). According to Peterson (1982), feedback can act as any other physicalstimulus does and therefore can serve multiple functions. Depending on its
temporal relation with behavior, feedback could be used as an antecedent for
subsequent behavior or a consequence for prior behavior. Given the appro­
priate history, feedback could serve as a conditioned reinforcer, conditioned
punisher, discriminative stimulus, or motivating operation (Johnson, 2013;
McGee & Johnson, 2015). Feedback could also exert control over respondent
behavior, for example, when feedback functions as a conditioned stimulus. For
example, the presentation of feedback may immediately elicit feelings typicallylabeled as “anxiety,” “anger,” or “pride” (Choi, Johnson, Moon, & Oah, 2018).
As is the case with other stimuli, it can change functions over time as well asserve multiple functions simultaneously. Due to the temporal gaps between
feedback stimuli and behavior, many argue that verbal mediation is likely what
explains many of the function-altering effects of feedback (Agnew & Redmon,
1992; Malott, 1992; Peterson, 1982).
Despite the volume of studies examining the effect of feedback, there islittle consensus as to the typical mechanism under which feedback functions
to change behavior; nor what variables in the feedback are necessary for it
to be effective (Alvero, Bucklin, & Austin, 2001; Johnson, 2013). Put differ­
ently, while there is overwhelming research on the implementation of
feedback, there remains an absence of research on both the functional andformal elements that make feedback optimally effective. Part of the difficultyin elucidating these issues may be that many previous studies have notdifferentiated the type and format of feedback used in the experiment. For
example, Wilson, Boni, and Hogg (1997) describe their study as using
corrective feedback, but a closer look at their methodology reveals thatthe managers delivered objective results, praise with performance improve­
ment, and corrective feedback. Gerson (1978) termed his intervention“objective feedback”, but since his “objective feedback” was presented aseither positive (good performance) or negative (bad performance), thisintervention was more fitting of what would be defined as evaluative
feedback.
These discrepancies create a nebulous framework for understanding the
aspects of feedback that impact the performance of interest. The imprecisionassociated with feedback has led some authors to call for research to analyze
and identify the numerous components subsumed under the broad label of
feedback (Johnson, 2013). For the current study, objective feedback will bedefined as measurable data of participants’ past performance (e.g., youJOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT

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completed 231 tasks) lacking any evaluative language or gestures (e.g., without
a thumbs up, saying good job, etc.).
Daniels and Bailey (2014) stated that for feedback to be effective, it should
convey specific information about performance. Objective feedback provides
the performer with specific information as to what contributes to desirable
performance, as defined by management, and provides a value against which
performance can be evaluated. Objective feedback has been the subject ofmany past research studies with varying conclusions regarding its effectivenessin isolation. In one early example by Chapanis (1964), participants spent hourlong sessions typing random digits into a teletype, an electromechanical type­writer that could send and receive typed messages through various commu­
nication channels. Participants in one experimental condition never received
feedback on their performance. In the remaining three experimental condi­
tions, participants received objective and individualized feedback on theirperformance in the form of a physical counter mounted above their work­
station. In one of these feedback conditions, the counter kept a cumulative
total of their progress across sessions. In the second feedback condition, thecounter was reset to zero every session to provide a numerical summary
specific to that session’s progress, rather than cumulative progress acrosssessions. In the last feedback condition, the counter was reset, and participants
were requested to write down their current total every 15 minutes. No sig­
nificant differences between conditions were demonstrated. As such, Chapanis
found that objective, individualized feedback in the form of a physical counter
did not improve performance in comparison to no feedback.
Johnson, Dickinson, and Huitema (2008) examined feedback in a 2 × 2
factorial design. In their study, four separate conditions were utilized includ­ing, a) incentive pay without objective feedback, b) incentive pay with objec­
tive feedback, c) fixed pay without objective feedback, and d) fixed pay with
objective feedback. Participants were asked to use keyboards to type in thedollar amounts of checks displayed in a data entry program on the computer.
The participants’ completion rate (checks per minute) and total checks com­pleted were displayed via the computer screen and updated every 30 seconds
throughout the session. While the monetary incentives proved to be effective
in increasing the number and rate of entered checks, the conditions in which
objective feedback was provided showed no significant difference compared tothose conditions without feedback. The authors concluded that objective,
individualized feedback in the form of a computer display did not improve
performance in comparison to no feedback.
Crowell, Anderson, Abel, and Sergio (1988) tested the effects of task clar­
ification, social praise, and objective feedback on bank teller-customer inter­actions. The experimenters posted anonymous individualized charts of meanscores for the tellers’ transaction-interactions in a room accessible to employ­ees only. Furthermore, the bank managers would verbally acknowledge this236

G. D. WARRILOW ET AL.

objective feedback during daily meetings. However, they were instructed not
to evaluate the performance of their employees (the praise condition was
implemented later). These scores were based on 11 behaviors targeted andobserved in each conversation the tellers had with customers over the previous
week. Crowell et al. found a gradual increase in teller-customer interaction
scores when objective feedback was used, resulting in a 6% increase in inter­action quality overall. Additionally, Crowell et al. found that when feedback
was removed, performance diminished but improved upon its reintroduction.
As such, Crowell et al. found that objective, individualized feedback in theform of data posted on a wall and face-to-face interactions did improve
performance in comparison to no feedback.
Johnson (2013) conducted a laboratory experiment at a large university
examining the effects of objective feedback alone, evaluative feedback alone,
combined evaluative and objective feedback, and a no feedback condition on
the same data entry task as Johnson et al. (2008). In all three feedback
conditions, the feedback about specific performance was personally deliveredby the researcher during face-to-face conversations in a small private room.
While participants in the no feedback condition saw a decline in performance,
the evaluative alone, objective alone, and combined evaluative and objective
feedback conditions each saw increases in performance at 85, 88 and 175 total
checks completed respectively, translating to a 1.89, 1.95, and 3.89 checks
per minute increase. Johnson found that objective, individualized feedback in
the form of face-to-face interactions improved performance in comparison to
no feedback.
Although objective and individualized feedback were used across all thestudies mentioned above and all involved tasks representative of the work­
place, the effects of feedback were not uniform. Half of these studies cited
above found the feedback to be effective in improving performance, whereas
half of the studies found the feedback to be ineffective. This parallels thefindings from literature reviews of feedback, which have found feedback inisolation to be inconsistently effective (Alvero et al., 2001; Mulder & Ellinger,2013). Such inconsistency suggests that a source of uncontrolled variance isoperating across these different studies examining the same phenomena. One
potential source of variability is the form through which feedback is delivered.
Across the above studies, a variety of methods such as visible counters, dis­
plays on computer screens, public wall postings, and personal interactions
were used to deliver feedback. These differences may have been a contributing
factor for the mixed results regarding feedback implementation.
Research participants have extensive histories with idiosyncratic and com­
mon cultural contingencies that may interact with different forms of feedback.
This collection of experiences likely involve authority figures from occupa­
tional, educational, and family settings who readily have potent sources ofreinforcement and punishment at their disposal. It is reasonable to speculate

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that, generally speaking, when authority figures show an interest (e.g., spend
time observing, etc.) in an individual’s specific performance, additional con­sequences will be provided by authority figures based upon the observed
performance. Supervisors start observing behavior of individual employee
performance to determine if rewards or discipline are warranted, teachers
start observing the behavior of individual student performance to determine if
praise or correction is needed, parents start paying attention to observing theperformance of their children to provide either admonition or affection, andso forth. One result of these culture-wide contingencies is that many, if not
most, participants bring a common experience to experiments. When theauthority figure is paying attention, related evaluation and consequences will
be forthcoming. Potentially, rules derived from such a history may mean thatany form of feedback that requires the attention of an authority figure may be
more effective than feedback that is automated.
It is important to understand the potential effects that the mode of feedback
delivery has on performance, especially as modern technology expands thepotential options for delivering feedback. Traditional forms of feedback deliv­
ery, such as print media and face-to-face interactions remain, but the rapid
development of the internet and computer technology has opened a host of
new options. These new modes for expressing feedback include, but are notlimited to, computer displays, e-mail correspondence, text messages, videoconferencing, and social media communications. Furthermore, many compa­
nies have begun employing remote workers, and events such as the COVID-19
pandemic will likely spur on this trend (Buscaglia, 2020; Fisher, 2020; Ortutay,2020). It is important to see what is gained and what is lost when supervision isprovided remotely. It is possible that different modes of feedback delivery will
have different effects on performance. While feedback has continued todevelop within organizations and research studies, little has been done toassess its delivery through various modalities and modern technology options.
Therefore, the current study employed alternative modes for delivering feed­back to compare their differential impact on performance.

MethodParticipants and settingThis study took place in a laboratory setting at midwestern university. Participants
were recruited via flyers posted around the university as well as via in-personclassroom presentations. A total of 102 participants completed the study in its
entirely and were distributed as follows: (a) no feedback (n = 23), (b) feedback via
face-to-face interaction (n = 21), (c) computer-delivered feedback (n = 30), and (d)cell phone text message feedback (n = 28). Ninety participants agreed to provide
demographic information. Of those participants, the ages ranged from 18 to238

G. D. WARRILOW ET AL.

41 years of age, averaging 20 years old with a standard deviation of 2.81 years.
When asked what ethnicity the participants primarily identified themselves as,
66% responded as White or Caucasian, 13% as Black or African American, 6% asAsian, 4% as Hispanic, 4% as multiracial, 2% as Indian, 1% as Palestinian
American, 1% as Filipino, 1% as Sikh, or 1% as Native American. In the experi­mental space there were four computers, each having an adjustable chair, sepa­
rated by cubicle walls. There were no windows in the laboratory room and thedoor to the laboratory was closed during the experimental sessions to assure
subjects had a reasonable degree of privacy. The researcher was located behinda cubicle wall that did not allow them a direct view of the participants’ actions but
would permit them to hear if participants were discussing details related to theexperiment itself (none did during observation).
Experimental task

The experimental task was a check-proofing task, similar to the job of a proof
operator at a bank and used in many other studies on feedback (Johnson, 2013;
Johnson et al., 2008; McGee, Dickinson, Huitema, & Culig, 2006; Slowiak,
Dickinson, & Huitema, 2011). A computer program developed using Visual
Basic displayed a graphic of a check with randomly generated dollar amounts
between 10.00 USD and 999.99. USD The participant entered the value using
the keypad and pressed the “Enter” key or used the mouse to click “NextCheck” at which point another check appeared for the participant to enter. No
signal would be provided during the session to indicate whether the dollar
amount was entered correctly or not. The computer continued to present
checks until the experimenter or research assistant entered the room, stopped
the subject where they were, and ended the session.
Experimental design, measures, and analysis

This study utilized a between-group repeated measures design with random
assignment to the following four experimental conditions; 1) no feedback, 2)
computer-delivered feedback, 3) feedback via cell phone text message and, 4)
feedback via face-to-face interaction. The main dependent measure was the mean
number of checks completed correctly per session. Data collected were evaluatedby completing a one-factor ANCOVA on performance during experimental
conditions and utilizing baseline performance mean as covariate measures.
Experimental conditions
Baseline sessions

During the first session, participants were told it was an introductory session,
had the experimental task explained to them, and had any questions answered

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until they fully understood the task. The subject was told of the importance ofattempting to enter as many checks as possible until the experimenter returned
to end the session. There was no additional training beyond the verbal
explanation of how the task worked. The participants then completed thecheck entering task for 45 consecutive minutes. When the participant cameback for their second scheduled session they were asked if they had anyquestions, again reminded of the importance of entering as many checks as
possible, and then allowed to engage in the check entering task for 45 minutes.
General procedures

In each of the experimental conditions, participants completed five 45-minutesessions in which they engaged in the experimental task. Before the beginningof each session, participants were reminded that they should do the best they
can for the duration of the time. After the conclusion of each session, feedback
was given (except for those in the no feedback condition) following theprocedures of the experimental condition to which they were assigned.
Feedback via face-to-face interaction

At the end of the participant’s second baseline session, the experimenterterminated the session and had the subject sit in another room for a few
minutes. Once the subject left the room the experimenter collected the per­formance data. After the data were gathered the experimenter joined thesubject in the other room and vocally stated how many total checks theparticipant entered and the total correct checks entered with a statement
such as, “You entered ____ total checks during today’s session with ___total checks entered correctly.” Participants could ask questions and commenton this feedback; however, the experimenter did not solicit any specificresponse. The experimenters made every effort to use an even tone withminimal body language (e.g., a flat affect with minimal gestures) to ensure
no evaluative components were included in the feedback delivered. If any
participants asked for an assessment of their performance, the experimenter
replied that they could not say (in practice, participant behavior neverrequired the implementation of this procedure). This process was repeated
for the five remaining sessions.
Computer-delivered feedback

Participants received the same feedback information regarding their total
checks entered and the rate of checks completed as the face-to-face condition,
however it was provided via the computer instead of vocally from the experi­
menter. At the conclusion of the 45-minute session the computer programautomatically generated a message which stated, “Number of checks com­pleted: ### (the number they completed).” Under this was the statement“Number of checks completed correctly: ### (the number they entered

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correctly was be displayed).” Under this line the statement, “Check completion
rate: ## (with the rate in checks per minute the subject completed displayed).”When the experimenter entered the room he or she asked, “Did the program
tell you how you did?” and sought some form of acknowledgment (e.g., a head
nod, an “ok” hand signal or thumbs up, a verbal response “yes” or “yeah”, etc.)
but no additional information was solicited. The participants could ask ques­
tions about the feedback. The experimenters answered those questions with aneven tone and flat affect to eliminate any possible evaluative components being
included in the feedback. If the participant asked if his or her total checks
entered and/or rate was “good,” the experimenter replied that they were notallowed to say. This process was repeated for the five remaining sessions.
Cell phone text message feedback

In this condition, participants received feedback via a text message sent fromthe experimenter. At the conclusion of the second baseline session and eachexperimental session, the experimenters informed the participants that theywould send them a text from their phone number (they told the subjects thephone number from which to expect the text) within the next two to fiveminutes with how many cumulative checks and the number of checks com­pleted correctly during the day’s session. The text message read, “You entered____ total checks during today’s session with ___ total checks entered cor­
rectly.” This message was sent after two minutes and within five minutes of theparticipant leaving the sight of the experimenter. This process was repeated forthe five remaining sessions.
No feedback

Participants in this condition received no feedback about their performance at
the conclusion of each session.
Debriefing

After participants completed all experimental conditions, they were asked tofill out a post-experiment survey, which asked them to rate questions related tothe challenge of the task, interest in the task, reaction to feedback, experi­mental purpose, and more on a Likert scale. These questions also included an
option to explain their rating in an open-ended response. Following theircompletion of the survey, the experiment’s purpose was briefly explained toparticipants and they were thanked for their participation.
Results

In order to demonstrate the initial gains, total gains, and gains within theexperimental sessions, Table 1 displays the differences in terms of percentage

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Table 1. Percentage of performance improvement for the average checks correctly completed.
Condition
Feedback via Face-to-Face Interaction
Computer Delivered
Feedback
Cell Phone Text
MessageFeedback
No Feedback

Percentage Gain: Final
Baseline to First
Experimental
11.1%

Percentage Gain: First
Baseline to Final
Experimental
23.4%

Percentage Gain: First
Experimental to Final
Experimental
5.3%

6.7%

15.8%

3.4%

4.7%

13.7%

8.9%

2.9%

5.6%

0.1%

gains for the mean number of checks correctly entered (a) between the final
baseline condition and the initial experimental condition, (b) between the firstbaseline session and the final experimental session, and (c) between the firstexperimental session and the final experimental session for all four conditions.
Results show improvements for all conditions, with the greatest initial andfinal gains for the face-to-face feedback condition and comparable gains forthe other two feedback conditions. Figure 1 demonstrates this pattern acrossthe baseline and experimental sessions. A one-factor analysis of covariance
was conducted using the mean performance during baseline sessions as thecovariate and the mean performance during experimental sessions as thedependent measure. The analysis found a statistically significant difference
between groups for the number of checks correctly completed (p =.05). Tukey
pairwise comparisons indicated a significant difference between the face-toface interaction condition and the no feedback condition (p = .04). No othersignificant differences were obtained. Effect size calculations for the number of800

No Feedback

Average Number Correct

750
Computer Feedback
Text Feedback
700
Face-to-face

650

600

550
Baseline

Baseline

Session 1 Session 2 Session 3 Session 4 Session 5

Figure 1. Average number of checks correctly completed over time.

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checks correctly completed indicated a ηp2 of .08 for the overall group differ­ences and a ηp2 of .07 for the pairwise comparison between the face-to-faceinteraction condition and the no feedback condition. One-factor analyses ofcovariance were also conducted using the mean percentage correct and meantime off task and no significant differences were found (p = .07 for percentage
correct and p = .60 for time off task). The mean percentage correct during theexperimental session was 96.7% (SD = 6.1%) for no feedback, 98.4%(SD = 1.6%) for computer-delivered feedback, 98.7% (SD = 0.9%) for text
message feedback, and 98.4% (SD = 0.6%) for face-to-face feedback. No
statistically significant differences were found for any of the questions on the
post-experimental survey.
Discussion

The results indicated that the face-to-face feedback condition improved per­formance relative to the no feedback condition for the number of correct
checks entered, which was statistically significant. The no feedback group had
a negligible gain of 5.6% mean checks correctly entered between the firstbaseline session and the last experimental session, as contrasted with theperformance gain of 23.4% mean checks correctly entered for face-to-facefeedback on the same measure. The face-to-face feedback condition showed
the largest increase in initial performance (11.1%) upon the introduction of
the experimental variables compared to other experimental conditions.
Viewing the performance metrics graphically also reveals interesting differ­ences in patterns between conditions. Both the no feedback and computerdelivered feedback groups showed stable trends in performance across experi­mental sessions, with a slight decrease in performance for the no feedback
condition and a very gradual increase for the computer-delivered feedback
condition. Both text message feedback and face-to-face feedback groups
showed moderate increases in performance across experimental sessions. In
summary, the face-to-face feedback condition had the greatest change in level,
an increasing trend across experimental sessions, and was the only feedback
condition to show a statistically significant difference relative to the no feed­back condition. This finding aligns with previous studies conducted byCrowell et al. (1988) and Johnson (2013).
The computer-delivered feedback condition had the second highest perfor­
mance gains, both when considering the initial gains and final gains. Relative
to the no feedback condition, computer-delivered feedback had a 3.8% higher
initial gain and a 10.2% higher final gain in performance. These differencessuggest a possible contradiction with the findings of Chapanis (1964) andJohnson et al. (2008). However, this conclusion should be interpreted withcaution because there was not a statistically significant difference between thecomputer-delivered feedback and no feedback conditions.

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Much like Kaufman, Codding, Markus, Tryon, and Kyse (2013) found, thehighest level of performance immediately following the introduction of feed­back was in the face-to-face interaction group. Conversely, the no feedback,text message feedback and computer feedback groups were all within 3.8%
performance improvement of each other for gains on the first experimental
sessions (i.e., second ranked computer feedback contrasted against lowest
ranked no feedback), the face-to-face feedback group outperformed the next
highest by 4.4% (i.e., first ranked face-to-face versus second ranked computer
feedback) and outperformed the lowest by 8.2% (i.e., first ranked face-to-faceversus lowest ranked no feedback). In other words, the face-to-face groupoutperformed the second highest, the computer feedback group, by a large
margin relative to other experimental comparisons.
The computer feedback and text message feedback conditions appear to
mirror each other. Both the computer and text message feedback groups
demonstrated similar results in correct checks entered when comparing
gains between baseline and experimental sessions, although text message
feedback group showed a greater increase in trend across experimental
sessions.
It is interesting to consider the response patterns of the groups within thecontext of Michael’s (1993) statement regarding generalization occurring on
a gradient directly correlated with the similarity between the original stimulus(i.e., face-to-face feedback from a past authority figure) and the novel stimulus(in this case, feedback from an electronic source or an experimenter directly).
The face-to-face group showed an immediate improvement followed by mod­erate increases in performance across experimental sessions (albeit withdecreases in a few sessions). The computer feedback group had a more
moderate initial increase and demonstrated only a minor increase acrossexperimental sessions. The text message group, which would fall somewhere
between face-to-face feedback (most similar to past feedback from authority
figures) and computer feedback (least similar to past feedback from authority
figures) on the stimulus continuum, demonstrated a blend, or a response
pattern that would fall on the gradient Michael referred to, of these two. The
text message group was the lowest of the feedback groups in terms of con­trasting performance between baseline and experimental sessions, but had thegreatest increasing trend within experimental sessions. The text message feed­back group was the only condition to show consistent increases across the last
four experimental sessions. It is assumed that many participants in the study
brought a common experience to this experiment – namely that whena supervisor is attending to their performance, evaluation and consequences
will likely follow. In this study, no evaluation was presented through the
feedback, nor consequences for improvements or decreases in performance.
This may explain some of the response patterns observed. For example, when
the feedback was delivered via face-to-face interaction, performance

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substantially exceeded the other groups, perhaps as a result of generalizationfrom the previously paired feedback-consequence/evaluation relation.
The consistent gains across experimental sessions for the text message
group might be attributed to a potential confound for this condition: withthe text messages the participants had a easily accessible record of how they
had been doing and could thus generate goals or establish verbal rules toperform better than last time. Although the participants could potentially also
generate a record (i.e., they could simply later write down what the experi­
menter or computer screen told them), the text messages would facilitate sucha record with much greater ease. During debriefing, one participant’s response
to how often they looked at the feedback illustrates this idea, “I would always
look at the number of checks I entered correctly, and I would calculate thenumber of checks I entered incorrectly. I would then, when the next session
would finish, check my number of correct checks entered, and I would often
compare it to the weeks before. Since it was all in the same text and easy tocompare.”Because there was no written feedback in the face-to-face condition, theparticipants had to recall two pieces of three-digit data between sessions,which could be as long as a week apart and similar to prior feedback. For
example, if a participant entered 715 checks total and 705 correctly this week,
it may prove difficult to recall and differentiate whether he or she improved
when told they entered 718 total checks with 708 correct during the next
session, not to mention across several sessions in which her or she may
perform in the mid-700s for both total checks entered and correct checks
entered. Subjects unable to recall whether their performance improved may
again encounter an extinction contingency if they do not realize their results
have improved, the putative conditioned reinforcer. If one considers the
feedback as a motivating operation, failure to contact the reinforcing proper­
ties of the feedback (signs of improvement) will fail to have an evocative effecton behaviors that lead to increased performance, and thus will fail to increase
the effectiveness of said feedback. An ongoing record thus may evoke further
performance increase, as evidenced by the participant who expressed, “ . . .
I tried to make it a challenge each time to try and complete more checks thanpreviously as well as to try and not enter any wrong.”
There were several factors which may have limited this study’s ability todraw clear conclusions for the effects of feedback’s modality on performance.
This study was designed to approximate work conditions. Therefore, partici­
pants could be off-task while the “supervisor” was not watching them.
However, there were a fair number of participants who were on their phones
for extended periods of time, talked with other participants, and evenstreamed movies and TV shows during their sessions. While allowing this totake place helps justify the experiments external validity, it may represent
a threat to the experiment’s internal validity.

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In order to clarify and develop scientific evidence for the effects of various
characteristics of feedback, objective feedback was the only type of feedback
examined in this study. As noted above, it is possible that the lack of formal
evaluation or consequence for performance on the task may have led todecreases and differential performance. Therefore, future research should
evaluate these modalities with evaluative feedback, to determine if different
patterns of responses are yielded. The results of Johnson (2013) suggests that
adding an evaluative component can have large effects in comparison toreceiving only objective or only evaluative feedback.
While this study used face-to-face interactions, text messages, and compu­
ter pop-ups to deliver feedback, additional modalities for feedback delivery areavailable. It would be interesting to continue the evaluation of modalities
falling on the continuum between face-to-face and computer feedback, suchas live-video feedback, previously recorded video feedback, or e-mails.
Because it is possible that the responding demonstrated was a result of
verbal mediation and goal-setting engaged in by the participants, it would also
useful to experimentally manipulate and examine the effects of formally andinformally established goals. For example, the record of feedback available in
the text message condition may have facilitated goal-setting by the participants
and sustained more consistent performance increases (as opposed to the faceto-face condition, which had inconsistent, although superior overall, effects).
It would be worthwhile to investigate whether adding an ongoing record could
enhance the effects of face-to-face feedback.
There is a need for both field and laboratory research to continue examining
feedback, particularly regarding the details for feedback implementation
rather than just feedback in general. Field research is necessary to extrapolate
what is known to benefit consumers and to verify that findings hold acrosssettings. For example, the current study would suggest caution is warranted forsupervisors who assume that any form of feedback will be sufficient. If all
feedback is seen as equal, then automated or e-mail feedback might be tempt­
ing to utilize as either a time-saving measure or as a strategy for interactingwith remote workers. However, this study found that these feedback modal­
ities would be less effective and that managers would do well to invest in faceto-face meetings, or at least use feedback methods with greater social presence(such as video conferencing) when direct contact is not possible. Further
laboratory research is necessary, despite decades of research dedicated to the
topic, because feedback is an omnibus term capturing many definitions, forms,delivery methods/agents, and social dynamics. In practice, this means that
there are often many potential confounds, which are difficult to tease out
against the multitude of organizational initiatives, policies, and procedures all
operating at the same time. Laboratory research can help separate suchprocesses out from one another and make the operation of independent
variables such as feedback more obvious.

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In conclusion, it appears there is a significant effect on performance whencomparing those who receive face-to-face objective feedback to those who
receive no feedback at all, whereas objective feedback through text message
and computer-delivered modalities did not produce significant effects. In the
future, it would be worthwhile to continue to explore the relationship between
modality of feedback delivery and effects on performance. Providing addi­
tional evidence for the specific response patterns observed in this experiment,
as well as evaluating additional feedback practices that are becoming more andmore common in the workplace, would be useful for those in scientificcommunity looking to explain past and contradictory findings, as well as
those in applied settings looking to get the most out of feedback.

Disclosure statement
No potential conflict of interest was reported by the authors.

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