Measuring Conformity to Discourse Routines 
in Decision-Making Interactions 
Sherri L. Condon Claude G. ~ech William R. Edwards 
Department of English Department of Psychology Center for Advanced Computer Studies 
condo@usl.edu cech@usl.cdu wre@cacs.usl.cdu 
University of Southwestern Louisiana/Universit~ des Acadiens 
Lafayette, LA 70504 
Abstract 
In an effort to develop measures of discourse 
level management strategies, this study examines 
a measure of the degree to which decision- 
making interactions consist of sequences of 
utterance functions that are linked in a decision- 
making routine. The measure is applied to 100 
dyadic interactions elicited in both face-to-face 
and computer-mediated environments with 
systematic variation of task complexity and 
message-window size. Every utterance in the 
interactions is coded according to a system that 
identifies decision-makmg functions and other 
routine functions of utterances. Markov 
analyses of the coded utterances make it possible 
to measure the relative fi'equencies with which 
sequences of 2 and 3 utterances trace a path in 
a Markov model of the decision routine. These 
proportions suggest that interactions in all 
conditions adhere to the model, although we find 
greater conformity in the computer-mediated 
environments, which is probably due to 
increased processing and attmfional demands for 
greater efficiency, The results suggest that 
measures based on Markov analyses of coded 
interactions can provide useful measures for 
comparing discourse level properties, for 
correlating discourse features with other textual 
features, and for analyses of discourse 
management strategies. 
Introduction 
Increasingly, research in computational 
linguistics has contributed to knowledge about 
the organization and processing of human 
interaction through quantitative analyses of 
annotated texts and dialogues (e.g. Carletta et 
al., 1997; Cohen et al., 1990, Maier et al., 
1997; Nakatani et al., 1995; Passonneau, 
1996; Walker, 1996). This program of 
research presents opportunities to examine the 
relation between linguistic form and pragmatic 
functions using large corpora to test 
hypotheses and to detect covariance among 
discourse features. For example, Di Eugenio 
et al. (1997) demonstrate that utterances 
coded as acceptances were more likely to 
corefer to an item in a previous turn. Grosz 
and Hirschberg (1992) investigate intonational 
correlates of discourse structure. These 
researchers recognize that discourse-level 
structures and strategies influence syntactic 
and phonological encoding. The regularities 
observed can be exploited to resolve language 
processing problems such as ambiguity and 
coreference, to integrate high level planning 
with encoding and interpretation strategies, or 
to refine statistics-based systems. 
In order to identify and utilize discourse- 
based structures and strategies, researchers 
need methods of linking observable forms with 
discourse functions, and our focus on 
discourse management strategies has 
motivated similar goals. Condon & (~ech 
(1996a,b) use annotated decision-making 
interactions to investigate properties of 
discourse routines and to examine the effects 
of communication features such as screen size 
on computer-mediated interactions (~ech & 
Condon, 1997). In this paper we present a 
method for measuring the degree to which an 
238 
interaction conforms to a discourse routine, 
which not only allows more refined analyses of 
routine behavior, but also permits fine-grained 
comparison of discourses obtained under 
different conditions. 
In our research, discourse routines have 
emerged as a fundamental strategy for managing 
verbal interaction, resulting in the kind of 
behavior that researchers label adjacencypaJrs 
such as question/answer or request/compliance 
as well as more complex sequences of functions. 
Discourse routines occur when a particular act 
or function is routinely continued by another, 
and as "predictable defaults," routine 
continuations maximize efficiency by requiring 
minimal encoding while receiving highest 
priority among possible interpretations. 
Moreover, discourse routines can be exploited 
by failing to conform to routine expectations 
(Schegloff, 1986). Consequently, interactions 
will not necessarily conform to routines at every 
opportunity, which raises the problem of 
measuring the extent to which they do conform 
Condon et al. (1997) develop a measure 
based on Markov analyses of coded interactions, 
• and the measure is employed here with a larger 
corpus in which students engage in a more 
complex decision-making task. These measures 
provide evidence for the claim that participants 
in computer-mediated decision-making 
interactions rely on a simple decision routine 
more than participants in face-to-face decision- 
making interactions. The measures suggest that 
conformity to the routine is not strongly affected 
by any of the other variables examined in the 
study (task complexity, screen size), even 
though some participants in the computer- 
mediated conditions of the more complex task 
adopted turn management strategies that would 
be untenable in face-to-face interaction. 
Data Collection 
The initial corpus of 32 interactions involving 
simple decision-making tasks was obtained 
under conditions which were similar, but not 
identical, to the conditions under which the 68 
interactions involving a more complex task 
were obtained. One obvious difference is that 
participants in the first study completed 2 
simple tasks planning a social event (a getaway 
weekend, a barbecue), while participants in the 
second study completed a single, more 
complex task: planning a televised ceremony 
to present the MTV music video awards. 
Furthermore, all interactions in the first study 
were mixed sex pairs, whereas interactions in 
the MTV study include mixed and same sex 
pairs. All participants were native English 
speakers at the University of Southwestern 
Louisiana who received credit in Introductory 
Psychology classes for their participation. 
In both studies, the dyads who interacted 
face-to-face sat together at a table with a tape 
recorder, while the pairs who interacted 
electronically were seated at microcomputers 
in separate rooms. The latter communicated 
by typing messages which appeared on the 
sender's monitor as they were typed, but did 
not appear on the receiver's monitor until the 
sender pressed a SEND key. The soft-ware 
incorporated this feature to provide well- 
defined turns and to make it possible to 
capture and change messages in future studies. 
In addition, to minimize message permanence 
and more closely approximate face-to-face 
interaction, text. on the screen is always 
produced by only one participant at a time. 
In the original study, the message area 
was approximately 4 lines long, and it was not 
clear how much this factor influenced our 
results. Consequently, in the MTV study, the 
message area of the screen was either 4, 10, or 
18 lines. Other differences in the computer- 
mediated conditions of the two studies include 
differences in the arrangement of information 
on the screen such as a brief description of the 
MTV problem which remained at the bottom 
of the screen. We also used an answer form in 
the first study, but not the second. More 
details about the communication systems in the 
two studies are provided Condon& ~ech 
(1996a) and (~ech & Condon (1998). 
239 
Data Analysis 
Face-to-face interactions were transcribed from 
audio recordings into computer files using a set 
of conventions established in a training manual 
(Condon & Cech, 1992). All interactions were 
divided into utterance units defined as single 
clauses with all complements and adjuncts, 
including sentential complements and 
subordinate clauses. Interjections like yeah, now, 
well, and ok were considered to be separate 
utterances due to the salience of their 
interactional, as opposed to propositional, 
content. 
The coding system includes categories for 
request routines and a decision routine involving 
3 acts or functions (Condon, 1986, Condon & 
(~ech, 1996a,b). We believe that the decision 
routine observed in the interactions instantiates 
a more general schema for decision-making that 
may be routinized in various ways. In the 
abstract schema, each decision has a goal; 
proposals to satisfy the goal must be provided, 
these proposals must be evaluated, and there 
must be conventions for determining, from the 
evaluations, whether the proposals are adopted 
as decisions. Routines make it possible to map 
from the general schema to sequences of routine 
utterance functions. Default principles 
associated with routines can determine the 
encoding of these routine functions in sequences 
of utterances. 
According to the model we are developing, 
a sequence of routine continuations is mapped 
into a sequence of adjacent utterances in one-to- 
one fashion by default. If the routine specifies 
that a routine continuation must be provided by 
a different speaker, as in adjacency pairs, then 
the default is for the different speaker to produce 
the routine continuation immediately after the 
first pair-part. Since these are defaults, we can 
expect that they may be weakened or overridden 
in specific circumstances. At the same time, if 
our reasoning is correct, we should be able to 
find evidence of routines operating in the manner 
we have described. 
(1) provides an excerpt from a computer- 
mediated interaction in which utterances are 
labeled to illustrate the routine sequence. P 1 
and P2 designate first and second speaker (an 
utterance that is a continuation by the same 
speaker is not annotated for speaker). 
(1) a. P1: \[orientation\] who should win best 
Alternative video. 
b. P2: \[suggestion\] Pres. of the united states 
c. PI: \[agreement\] ok 
d. P2: \[orientation\] who else should 
nominate. 
e. \[suggestion\] bush. goo-goodolls 
oasis 
f. Pl: \[agreement\] sounds good, \[...1 
we 
and 
(2) provides an annotated excerpt from a 
face-to-face interaction. 
(2) a. Pl: \[orientationl who's going to win? 
b. \[suggestion\] Mariah? 
c. P2: \[agreement\] yeahprobably 
d. PI: \[orientation\] alright Mariah winswhat 
song? 
e. P2: \[suggestion\] uh Fantasy or whatever? 
f. Pl: \[agreement\] that's it that's the same 
song I was thinking of 
g. \[orientation\] alright alternative? 
h. \[suggestion\] Alanis? 
Coded as "Orients Suggestion," orientations, 
like (la,2a) establish goals for each decision, 
while suggestions like (lb,e) and (2b, e,h) 
formulate proposals within these constraints. 
Agreements like (lc,f) and (2c,f), which are 
coded "Agrees with Suggestion," and 
disagreements ("Disagrees with Suggestion") 
evaluate a proposal and establish consensus. 
The routine does not specify that a suggestion 
which routinely continues an orientation must 
be produced by a different speaker: the 
suggestion may be elicited from a different 
speaker, as in (la,b) and (2d,e) or it may be 
provided by the same speaker, as in (ld,e) and 
(2a,b). However, an agreement that routinely 
continues a suggestion is produced by a 
different speaker, as (lb,c), (le,f), (2b,c) and 
(2e,f) attest. 
Other routine functions are also classified 
in the coding system. Utterances coded as 
"Requests Action" propose behaviors in the 
speech event such as (3). 
240 
(3) a. well list your two down there (oral) 
b. ok, now we need to decide another band to 
perform (computer-mediated) c. Give some suggestions 
(computer-mediated) 
metalanguage, and orientations 
somewhat less reliable. 
Results 
were 
Utterances coded as "Requests Information" 
seek information not already provided in the 
discourse, as in (la,2a). Utterances that seek 
confirmation or verification of provided 
information, however, are coded as "Requests 
Validation." The category "Elaborates- 
Repeats" serves as a catch-all for utterances 
with comprehensible content that do not 
function as requests or suggestions or as 
responses to these. 
Two categories are included to assess 
affective functions: "Requests/Offers Personal 
Information" for personal comments not 
required to complete the task and "Jokes 
Exaggerates" for utterances that inject humor. 
The category "Discourse Marker" is used for a 
limited set of forms: Ok, well, anyway, so, now, 
let's see, and alright. Another category, 
Metalanguage, was used to code utterances 
about the talk such as (3b,c). 
In the initial corpus, the categories 
described above are organized into 3 classes: 
MOVE, RESPONSE, and OTHER, and each 
utterance was assigned a function in each of 
these three groups of categories. In cases 
involving no clear function in a class, the 
utterance was assigned a No Clear code. A 
complete list of categories is presented at the 
bottom of Figure 1 and more complete 
descriptions can be found in Condon and Cech 
(1992). In the modified system used to code the 
MTV corpus, the criteria for classifying all of 
these categories remain the same. 
The data were coded by students who 
received course credit as research assistants. 
Coders were trained by coding and discussing 
excerpts from the data. Reliability tests were 
administered frequently during the coding 
process. Reliability scores were high (80-100% 
agreement with a standard) for frequently 
occurring move and response functions, 
discourse markers, and the two categories 
designed to identify affective functions. Scores 
for infrequent move and response functions, 
In the initial study, the 16 face-to-face 
interactions produced a corpus of 4141 
utterances (ave. 259 per discourse), while the 
16 computer-mediated interactions consisted 
of 918 utterances (ave. 57). In the MTV 
study, the 8 face-to-face interactions produced 
3593 utterances (ave. 449), the 20 
interactions in the 4-line condition included 
2556 utterances (ave. 128), the 20 interactions 
in the 10-line condition produced 3041 
utterances (ave. 152) and the 20 interactions in 
the 18-line condition included 2498 utterances 
(ave. 125). Clearly, completing the more 
complex MTV task required more talk. 
Figure 1 presents proportions of utterance 
functions averaged per interaction for each 
modality in the initial study. Analyses of 
variance that treated discourse (dyad) as the 
random variable were performed on the data 
within each of the three broad categories, 
excluding the No Clear MOVE/RESPONSE/ 
OTHER functions where inclusion would 
force levels of the between-discourse factor to 
the same value. We found no significant effect 
of problem t?/pe or order (for details see 
Condon & Cech, 1996). However, the 
interaction of function type with discourse 
modality was significant at the .001-level for 
all three (MOVE, RESPONSE, OTHER) 
function classes. Tests of simple effects of 
modality type for each function indicated that 
only four proportions were identical in the two 
modalities: Requests Validation in the MOVE 
class, Disagrees in the RESPONSE class, and, 
in the OTHER class, Personal Information and 
Jokes-Exaggerates. 
Figure 2 presents the proportions of 
utterance functions for the MTV corpus using 
the same categories of functions as in Figure 1. 
The similarity of the results in the two figures 
is remarkable, especially considering 
differences in methods of data collection 
described above. First, it can be observed that 
241 
I o 
00.2. 
" : oo.1. 
. \ 
o I l I I I I ! I 
MOVES RESPONSES OTHER 
6 
i .f 
I I iA dv ,sos c. Ao dt is i, 
MOVES RESPONSES OTHER 
MOVE FUNCTIONS 
SA Suggests Action 
RA Requests Action 
RV Requests Validation 
RI Requests laformation 
ER Elaborates, Repeats 
OTHER FUNCTIONS 
DM Discourse Marker 
MI, Metalanguage 
OS Orients Suggestion 
Pl Personal Information 
Jig Jokes, Exaggerates 
RESPONSE FUNCTIONS 
AS Agrees with Suggestion 
DS Disagrees with Suggestion 
CR Complies with Request 
AO Acknowledges Only 
Figure 1: Propo~ons of code categories in face-to- 
face (squares) and computer-mea~ated interactions 
(asterisks) in the original study 
the screen size in the MTV-condition did not 
influence the proportions of functions in the 4- 
line and 18-line conditions. The results in both 
those conditions are nearly identical. Second, 
similar differences are obtained between face-to- 
face and computer-mediated conditions in both 
corpora. For example, all of the computer- 
mediated interactions produced suggestions at 
a proportion of approximately .3, while the face- 
to-face interactions produced suggestions at 
closer to half that frequency. Similar patterns of 
difference between face-to-face and computer- 
Figure 2: Proportions of code categories in face-to- 
face (Mangles), 4-line (squares) and 18-line 
(circles) conditions 
mediated conditions occur in both corpora for 
the 3 types of requests in the coding system, 
tOO. 
We anticipated an increase in discourse 
management functions due to the complexity 
of the task, and the increase in metalanguage 
from .05 to. 15 in the face-to-face conditions 
suggests that the more complex task pressured 
participants to engage in more explicit 
management strategies. In the computer- 
mediated interactions, the proportion of 
functions coded as metalanguage also 
increases with the complexity of the task, 
though not as much. The greater proportion 
of discourse markers in the computer-mediated 
interactions also reflects an increase in 
discourse management activity for the more 
complex task. 
The failure to observe an increase in the 
proportion of utterances coded as "Orients 
Suggestion" in the MTV interactions is 
probably a result of the emergence of a turn 
strategy not observed in the interactions with 
simpler decision-making tasks. Specifically, 
while all of the computer-mediated interactions 
in the initial study and many of the computer- 
mediated interactions in the MTV study 
242 
consisted of relatively short turns, some of the 
latter display a strategy of employing long turns 
in which participants encode routine functions 
for several decisions in the same turn, as in (4). 
(4) Best Female Video Either we could have Celine 
Dione's song rts all coming back to me or the other 
one that was in that movie up close and personal. 
Aany of the clips with her in them would be good. 
Toni Braxton with that song..gosh I can't think of 
any of the names of anybody's songs. And show the 
same clip as before. What about jewel. Who will 
save your soul. Personally I think she should win we 
could use the clip of her playing the guitar in the 
bathroom. We need one more female singer. Did we 
pick who should present the award? I think Bush 
should play after the award. 
These more parallel management strategies can 
reduce the number of orientations if a single 
orientation can hold for several suggestions and 
a single agreement can accept them all. Of 
course, this is exactly what happens when 
participants provide a list of suggestions in a 
short turn, too. Therefore, the parallel strategy 
is a minor modification of the decision routine, 
but it may influence the proportions of routine 
functions by reducing the number of orientations 
and agreements. 
In fact, the proportions of utterances coded 
as "Agrees with Suggestion" and "Complies 
with Request" are lower in the computer- 
mediated MTV interactions than in the 
computer-mediated interactions of the initial 
corpus. Though these proportions are still 
slightly higher than those in the face-to-face 
MTV condition, preserving the pattern observed 
in the initial corpus, the differences are smaller. 
These differences are reflected even more 
dramatically if we compare the ratios of 
suggestions to agreements in the MTV corpus. 
At approximately 1.5, the ratio of suggestions to 
agreements in the face-to-face condition of the 
MTV study resembles the ratio in the face-to- 
face condition of the earlier study (1.64). 
Similarly, the ratio of suggestions to agreements 
in the computer-mediated interactions of the 
original study is 1.71. In contrast, the ratios of 
suggestions to agreements in the 4- and 18-line 
conditions of the MTV corpus are much larger, 
both at approximately 2.5. We believe that 
much of the difference observed is the result of 
longer turns employing parallel decision 
management in the MTV corpus. 
These results raise the question of the 
extent to which the interactions conform to a 
model of the decision routine we have 
described. The measure developed in Condon 
et al. (1997) begins by combining the 3 code 
annotations as a triple and treating those 
triples as the output of a probabilistic source. 
Then 0-, 1 st- and 2nd-order Markov analyses 
are performed on the resulting sequences of 
triples. While the 0-order analyses simply give 
the proportions of each triple in the 
interactions, the lSt-order analyses make it 
possible to examine adjacent pairs of triples to 
determine the probability that a particular 
combination of functions will be followed by 
another particular combination of functions. 
Similarly, the 2hal-order analyses examine 
sequences of 3 utterances. 
Orientation ~ Suggestion~Agre_ement 
Figure 3: A More Complex Decision Routine Based 
on Frequency Analyses 
Examination of the 2ha-order analyses in 
the original study revealed that all of the 7 
most frequent sequences of 3 utterances trace 
a path in the model in Figure 3. Using the 
model in Figure 3, we then calculated the 
proportions of 0-, 1 st- and 2nd-order sequences 
that trace a path through the model. Of course, 
the 0-order frequencies simply provide the 
proportions of utterances that are coded as 
243 
Discourse Morality 
Markov Order Oral Electronic 
0 (Single Function) 
1 (Sequence of Two) 
2 (Sequence of Three) 
.34 (.09) .53 (.13) 
.16 (.06) .32 (.13) 
.07(.04) .21(.11) 
Table 1: Proportions of Utterance Events Averaged 
Per Discourse (Standard Deviations in Parentheses) 
that Conform to the Model in Figure 3 from the 
Original Corpus 
either orientations, suggestions or agreements, 
but the 1 st- and 2"a-order analyses make it 
possible to examine the extent to which pairs 
and sequences of 3 utterances conform to the 
model in Figure 3. Table 1 presents the results 
of obtaining the measure just described from the 
initial corpus of face-to-face and computer- 
mediated interactions. The proportions therefore 
reflect the average (and standard deviation) per 
discourse of events that conform to a sequence 
of routine continuations in Figure 3. 
Since conforming to the model is less and 
less likely as more functions are linked in 
sequence, it is not surprising that the proportions 
decrease as the order of the Markov analysis 
increases. Still, it is encouraging that the 
proportions of routine continuations in the 1 st- 
order analyses are approximately equal to the 
proportions of suggestions in the two types of 
interactions, since the latter provide an 
estimate of the number of opportunities to 
engage in the routine. 
Table 2 presents the results of computing 
the same analyses on the face-to-face, 4-line, 
10-line, and 18-line computer-mediated 
interactions in the MTV corpus. The 0-order 
results are much the same for both corpora 
with about 1/3 of the utterances in face-to-face 
interactions functioning in the decision routine 
compared to ½ in the computer-mediated 
interactions. Similarly, proportions of 
utterance pairs that conform to the routine 
remain fairly close to the proportions of 
suggestions in each condition. Screen size 
appears to have no effect on the results 
obtained with this measure. 
Conclusions 
The results are promising both as evidence for 
our theory of routines and as an initial attempt 
to devise a measure of conformity to routines. 
In particular, the fact that an additional corpus 
with a more complex task has provided 
measures which are very similar to those 
obtained in the initial corpus increases our 
confidence that these methods are tapping into 
some stable phenomena. Moreover, the 
similarities of the conformity measures in 
Tables 1 and 2 occur in spite of the emergence 
Marker Order 
Discourse Modality 
Oral 4-1me 1 O-line 18 -line 
0 (Single Function) 
1 (Sequence of Two) 
2 (Sequence of Three) 
.29 (.07) .50 (.12) .48 (.11) .45 (.ll) 
.11 (.05) .27 (.10) .25 (.10) .21 (.11) 
.04 (.03) .17 (.10) .14 (.08) .12 (.10) 
Table 2: Proportions of Utterance Events Averaged Per Discourse 
(Standard Deviations in Parentheses) that Conform to 
the Model in Figure 3 from the MT~ Corpus 
244 
of new computer-mediated discourse 
management strategies in which long turns 
encode decision sequences in parallel. Though 
these strategies seem to have a strong effect on 
the ratio of suggestions to agreements in the 
computer-mediated interactions of the MTV 
corpus, the conformity measures are still quite 
similar to the measures obtained in the 
computer-mediated interactions of the initial 
study. 
The MTV data also confirm the result 
obtained in the original study that computer- 
mediated interactions rely more heavily on 
routines than face-to-face interactions. The 
much higher conformity measures for all three 
Markov orders provide clear evidence for this 
claim with respect to the decision routine. 
Moreover, a comparison of Figures l and 2 
shows that the computer-mediated interactions 
have higher proportions of requests, especially 
requests for information. If these proportions 
are indicative of the extent to which request 
routines are relied on in the interactions, then 
these data also support the claim that computer- 
mediated interactions rely on discourse routines 
more than face-to-face interactions. Given our 
claims about the effectiveness of discourse 
routines, it makes sense that participants in an 
unfamiliar communication environment will 
employ their most efficient strategies. 
The conformity measure that has been 
devised does not make use of all the information 
available in the Markov analyses, and we 
continue to experiment with different measures. 
It seems clear that Markov analyses can provide 
sensitive measures that will be useful for 
identifying differences between interactions and 
for measuring the effects of experimental factors 
on interactions. 

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