IEEE TRANSACTIONS ON PROFESSIONAL COMMUNICATION, VOL. 47, NO. 2, JUNE 2004 95

Building Trust in Virtual Teams

—NANCY W. COPPOLA, SENIOR MEMBER, IEEE, STARR ROXANNE HILTZ, AND NAOMI G. ROTTER

Abstract—This paper presents a study of trust development in online courses. It reviews the concept of swifttrust and examines changes in faculty roles as professors go online. An exploratory content analysis looks atindicators of the development of swift trust in the highest rated of a large number of online courses studiedover a three-year period, and contrasts these results with one of the poorest-rated online courses. Establishingswift trust at the beginning of an online course appears to be related to subsequent course success. Strategiesfor trust formation are also suggested.

Index Terms—Asynchronous learning networks (ALNs), e-learning, swift trust.

An asynchronous learning network (ALN) uses theworld wide web and the internet to deliver coursesanytime rather than “same time,” with an emphasison student-student as well as student-teacherinteraction. The most successful courses useextensive class discussions and group assignments(in teams varying in size from dyads to the wholeclass) to build a learning community [1]. This paperpresents an exploratory qualitative study of whetherthe formation of SWIFT TRUST in the first few weeksof an online course can help explain the successof online courses. Though it is limited primarilyto an intensive study of trust formation indicatorsin one especially successful course, the study hasimplications for other types of online work in whichwork groups or teams plan to interact over a periodof several months.

Swift trust is a concept developed by Meyerson et al.for temporary teams who form around a clear purposeand common task with a finite life span [2]. Swifttrust was originally developed to describe high-riskand high-stake temporary groups such as film crewsor cockpit crews. The researchers discovered thattemporary teams are tied together by a form of trustwith unusual properties. Swift trust is a unique formof collective perception, rather than scaled-downtrust, for temporary, but not trivial, situations.Meyerson et al. frame swift trust in temporary systemsin terms of the following social characteristics:

• vulnerability—the belief (hope) that others willcare for what is being entrusted with good will.

• uncertainty—a willingness to suspend doubt inorder to execute the task performance.

• risk—a willingness to take risks.• expectations—A positive expectation of benefits

of temporary group activity.

Manuscript received September 25, 2002; revised November 18,2003. The authors are with the New Jersey Instituteof Technology, Newark NJ 07102-1982 USA(email: coppola@admin.njit.edu).

IEEE DOI 10.1109/TPC.2004.828203

Especially for students and faculty who are new tolearning online, the above set of characteristics isimportant to members of new “temporary teams.”Compared with a traditional classroom, the virtualclassroom presents more uncertainty, risk, andexpectations. This is certainly the case for the firsttime ALN user who is uncertain about how to proceedand what to expect. Moreover, this new ALN user maybe concerned with how to interact successfully in anunfamiliar and ambiguous setting, where many ofthe usual social cues are missing. Even if a facultymember or a student has had a prior course usingthe ALN approach, they are never sure whether orhow this new set of characters is going to be able towork together.

While trust in general is an important ingredientin the functioning of social relationships, swifttrust takes on heightened significance in the ALNenvironment because the situation can leave studentsvulnerable to feelings of isolation. This sense ofisolation has been discussed extensively in theliterature on telecommuters (e.g., Kugelmass [3]).The telelearner working alone is analogous to thetelecommuter. The dynamic of swift trust can alleviatethis potential aspect of the ALN environment.

Because people in temporary groups have a limitedamount of time to become familiar with one another,they import trust and assign perceptions based onpast personal and professional stereotypes. Sincethese groups come together to get a job done, swifttrust is sustained and reinforced by a high level ofactivity. According to Meyerson et al., “The moreforceful the action, the greater the willingness to trustand the more rapidly does trust develop” [2, p. 180].

High levels of action are associated withhigh-performing teams in the Iacona and Weisbandstudy of temporary electronic teams [4]. Theseresearchers wanted to understand what temporary,distributed teams do to produce and maintain trust.They studied 14 teams of graduate and undergraduatebusiness students in three geographically diverse

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universities for instances of active interaction. Theycoded email between team members for instances ofinitiations and responses to initiations. They foundthat high levels of trust were maintained in teamsthat engaged in continuous and frequent interaction,what they describe as “doing trust work,” [4, p.413]. In summary, while trust, swiftly or slowlybuilt, combines both feelings and beliefs regardingthe extent of confidence to be placed in another’swords and actions, it derives from reliable acts andcommunications.

Jarvenpaa and Leidner extend the original conceptof swift trust to an examination of global virtualteams [5]. They define a global virtual team asa temporary, culturally diverse, geographicallydispersed, electronically communicating work group(following Kristof et al. [6]).

Jarvenpaa and Leidner’s research analyzes behaviorsand actions both in early and later stages of groupwork.

Early behavior and actions that facilitate trustin-group

Communication• Social communication; social exchanges• Communication conveying enthusiasm

Member actions• Coping with technical and task uncertainty• Individual initiative; members suggest topics,

volunteer

Later behaviors and actions that facilitate trustin-group

Communication• Predictable communication; regular pattern of

communication; warning of absences• Substantive and timely responses; explicit and

prompt responses that the messages were readand evaluated

Member actions• Leadership rotated among members• Transition from procedural to task focus;

movement from rules to emphasis on the task• Phlegmatic reaction to crisis; ability to ride out

turbulence

Jarvenpaa and Leidner’s study suggests that swifttrust forms in global virtual teams with uniquecommunication and behaviors. First, communicationvia the earliest keystrokes begins to establishtrust. Task communication maintains trust whilesocial communications (and explicit statements ofcommitment, excitement, and optimism) strengthentrust. Finally, the members’ initial actions as well astheir responses to one another are critical to trustdevelopment.

CONTEXT OF THIS STUDY

For over a decade, a research team at New JerseyInstitute of Technology (NJIT) has been involved inconstructing a specific version of an ALN, called theVirtual Classroom, and studying its use in a widevariety of courses [7]. NJIT can be considered a“strategic research site” for studying determinantsof the effectiveness of online courses, since it hasone of the largest sets of online undergraduate andgraduate programs in the US (according to U.S.News and World Report), mostly in informationtechnology degrees such as the B.A., B.S., and M.S.in information systems and computer science. As ofthe 2001–2002 academic year, when this study wasconducted, 136 instructors offered ALN courses, withapproximately 2,800 students enrolled.

We found that students are generally enthusiasticabout the opportunity to learn online. Our findingscorrespond to those for over 30 empirical studies ofthe effectiveness of ALNs as compared to traditionalface-to-face courses [8]–[10]. The research literature,which uses grades and student evaluations aboutequally to measure course effectiveness, tends to showeither no significant difference in learning outcomesor significant advantages of ALNs over traditionalface-to-face classrooms in student satisfaction. Forexample, Wade and Power found that students in ALNenvironments received more in-depth exposure to thecourse content area [11]. Bourne et al. showed thatALN is more effective than the traditional lecture orlaboratory and that peer-to-peer learning is enhancedthrough ALN [12]. Other studies evaluated gradesand test performances in ALNs. Alavi found thatfinal grades of students using computer-mediatedcollaborative learning were significantly higher thanthose of students who did not use computer-mediatedlearning [13].

Relatively little research has been published thatdocuments exactly how the technology changesthe teaching process and the role of the universityfaculty member. Among the few studies that doexamine these questions are our prior study of rolechanges for virtual professors, described below, andrecent content analysis of discussion transcripts byHeckman and Annabi [14]. They coded the lengthand type of “utterances” in a course transcript usingfour major categories: social processes, cognitiveprocesses, teaching, and discourse. They found ashift in student and faculty roles: the presence of theteacher was much more pervasive in face-to-face, butwhile in ALN, the student utterances were longer andteacher utterances were shorter.

Our research path to the investigation of swifttrust began with questions concerning studentoutcomes. If studies describe students gainingsignificantly higher grades using computer-mediatedlearning, what factors might explain this? Through

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examination of faculty experiences, we uncoveredshifts in self-described faculty role behavior as theymoved from the traditional setting to the electronicclassroom. These shifts, described below, led usto look for associated effects in virtual classroominteractions.

Role Changes for Virtual Professors To understandhow ALN technology changes the teaching processand the role of faculty, the authors designed,conducted, transcribed, and coded 20 semistructuredinterviews with faculty who have prepared anddelivered at least one online course [15]. We wantedto hear from ALN faculty about how they perceive theteaching and learning process to have been alteredby using online communication as the primarymode of communication with their students. Theinterviews and their analysis suggested that the rolesenacted by instructors in traditional settings arealso enacted in ALN environments, though each ofthese roles is transformed. The cognitive role, whichrelates to mental processes of learning, informationstorage, and thinking, shifts to one of deeper cognitivecomplexity for virtual professors. The affective role,which relates to influencing the relationships betweenstudents and the instructor and the classroomatmosphere, required them to find new tools toexpress emotion, yet they found the relationshipwith students more intimate. The managerial role,which deals with class and course management,requires greater attention to detail, more structure,and additional student monitoring.

The cumulative roles may be described as a persona.Overall, faculty reported a change in their teachingpersona. They noted the need for precision anda certain formality in outlining expectations forstudents. This is probably because there is littleopportunity for students to raise spontaneousquestions about details of requested activities; unlessthey are clearly detailed, they may be misunderstoodand lead to disorganization. However, while in someways the online teaching persona is more formal interms of precision of instructions given to students,in other ways it is less formal, especially in terms ofmoving toward more “give and take,” a kind of “DigitalSocrates” who shifts from conveying information toraising questions and engaging in dialogue.

Answers Lead to Questions We knew that faculty,in their own words, found changes in their onlineteaching roles and persona. However, we wonderedif there were any independent data to supportfaculty perceptions. Faculty talked about buildingcommunity, relationships, and trust. Could we findexamples of these interactions?

These questions lead to the literature of teamworkin corporations, whereby team members graduallydevelop interpersonal relationships and trust overtime. Research into building trust among members of

temporary teams has produced the theory called swifttrust. In temporary teams, a variant of trust, swifttrust, is necessary to predict successful teamwork.We set out to explore whether the development ofswift trust in the beginning of an online course couldhelp to explain why some ALN courses are especiallysuccessful, as judged by the students.

RESEARCH METHODS

Our current research thread attempts tooperationalize the concept of swift trust in virtuallearning communities. Our research premises are asfollows.

(1) If faculty are to become successful “DigitalSocrates,” they must overcome the coldnessin the electronic medium with socialcommunication clues in the conferences.

(2) The most effective online teachers get a goodstart in the very first week of online classes,which is the essence of swift trust, with onlineconferencing.

(3) Once established, swift trust will carry over inthe remainder of the semester.

Measuring Teaching Effectiveness For thisexploratory study, our objective was to identify the“most effective” teacher in the ALN environment wehad examined, and to develop and apply a codingscheme to measure the extent of swift trust formationin the first few weeks of one of the courses taughtby this instructor. We also decided to contrast thisto a coding of the same period of interaction in acourse in which the teaching effectiveness rating wasvery low. To determine the most effective teacher,we used data collected from over 1300 postcoursequestionnaires that rated instructor’s effectivenessfor ALN and “matching” face-to-face sections of bothundergraduate and graduate NJIT courses over aperiod of three years. Sections of the questionnairecontained sets of items designed to measure variousaspects of the process and outcomes of courses:perceived quality of the instructor’s performance,course outcomes, and overall evaluation of the“Virtual Classroom” ALN experience (the latter only forsections using ALN). The unidimensionality of thesesets of items was validated by an item analysis usingChronbach’s Alpha and exploratory factor analysis,before being combined into an index.

Two key indexes for measuring the effectiveness ofthe instructor in an ALN section are the “instructoroverall” index and the “Virtual Classroom overall”index. The items in the instructor index aresimilar to those used for decades to obtain studentcourse ratings at many institutions and representmodifications of the standard teaching evaluationinstruments used by various departments at NJIT;the modifications were designed to take into accountthe delivery medium [16]. From a methodological

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point of view, there are more items than would beneeded to get an overall measure of teaching, andthe positive nature of all of the items can introducebias. However, organizational context must also betaken into account in devising a measure of teachingeffectiveness. That is, for promotion and tenurepurposes, it is important to make the measurementssimilar to those already used in traditional classes.

The Virtual Classroom overall index (Tables IV and V)was designed at NJIT; of the six items, half are statedpositively and half are stated negatively, in orderto minimize response bias. Looking at the VirtualClassroom overall questions, the results for this studyare very similar to those for the previous project thatinvolved undergraduate CIS students only. Theseresults show the ratings to be generally favorable forthe ALN courses, in terms of the students feeling thatthey are as good or better than traditional courses.For example, on the question, “Did the use of thesystem increase the quality of your education,” a 1–7Likert-type scale, 56% indicated positive agreement,and 25% saw no difference; 19% disagreed. With 1.0the score for the most positive response and 4.0 thevalue for a no difference or “unsure” response, themean of 3.3 is on the positive side and compares toa slightly less positive mean of 3.4 for the previousstudy of undergraduates only.

For those instructors with at least eight validstudent questionnaires, an analysis of variance(ANOVA) was performed to determine if there weresignificant differences among instructors. A Duncan’sMultiple Range test showed that the mean ratingsfor instructors are significantly different from oneanother for both indexes. Since the rank order for

instructors was somewhat different on the two majormeasures of instructor effectiveness, we selected theinstructor with the highest average ranking as themost effective online instructor. We will refer to thisinstructor as “Professor T.”

For comparison, we selected Professor X. On thekey indices, Professor X was in a group of the threelowest ranking instructors, which were significantlylower than most other instructors, according to theWaller-Duncan ratio T-test. For example, on theinstructor overall index, Professor X had a mean of60, compared to the mean of 70 for Professor T.

Determining the Codes To analyze thecommunication within the group, we adapted aclassic, well-established system by Bales calledinteraction process analysis, which makes adistinction between task achievement and socialmaintenance [17]. This is one of the most widelyused interaction content analysis schemes, even fiftyyears after its first introduction, with approximately100 citations in the 2000–2002 social scienceliterature. Our coding scheme, shown in Table I,distinguishes seven categories of behavior andcommunication in group interaction. To establish acommon understanding in applying the codes, thethree researchers individually coded part of the sameconference transcript and discussed discrepancies.These discussions set the guidelines for the rest of thecoding of the conference. We then continued codingindependently.

Analysis: The conference transcripts (onlinediscussions) were transcribed and coded usingQSR NVivo software (1999), which has been used

TABLE ISwift trust coding in virtual classrooms

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extensively in educational research. This programassists in qualitative data analysis when the dataare transcribed interviews. The researcher decideswhat becomes a meaningful section of text for coding.In this case, we selected messages, both instructorand student new posts (that is the initiation of aresponse) and replies (responses to posts), as thetext section. Then, each text unit can be coded usingeither a researcher-created code or the program touncover themes within the interviews. This researchused coding categories shown in Table I. In addition,we counted responses to determine the level ofinteraction. We realized that we needed to read thesubject lines of the messages in the conferences andto read the messages carefully for nuance and tenorof comment. We make no claims that our researchis discourse analysis; although we multiply codedsections of text rather than using mutually exclusivecategories, our qualitative research is content analysis[18].

FINDINGS

Effective Online Instructor For analysis, weselected Professor T’s course on managementof information systems. Professor T uses acommercial computer-mediated software systemcalled WebBoard to organize his online discussionforums or conferences. The following conferenceswere structured to contain a small number of relatedtopics: Instructor’s Instructions; General Discussion;Introductions; Questions on Assignments; Questionson book Chapters; Questions on Lectures; Questionson Readings (i.e., professional papers assigned);Management Jokes (related to IS); and Café andPractice.

The Introduction conference is critical in onlineteaching because it establishes the atmosphere,interaction, and dialogue for the entire course.Typically, the instructor introduces himself/herself,providing a model of expected response, and outliningquestions for response. Students then introducethemselves, perhaps informing the class of theirexperience in the field, their objective for takingthe course, and their topics of interest in thecourse. Professor T knows the importance of theseintroductions. “The first participatory conferenceis important in that it helps create a welcomingatmosphere, so I make every effort to respond tomost introductions. The students also know there areassignments where they can work as teams so theyrealize their introduction is useful for team formation”(Explanation by Professor T, 2001).

Early Communication: Using the swift trust codingscheme, we looked for evidence of swift trust inthe Introductions conference, which takes place inthe first two weeks of the course. The conferencewas examined for the kinds of behaviors and

actions that facilitated early trust formation inJarvenpaa and Leidner’s research. Their earlycommunication activities of social exchanges andsocial communication correlate with our codingcategories of social emotional positive response toeach other (Code 3) and social emotional negativeresponse to one another (Code 4). Out of 297 codedpassages in the Introductions conference, we found35 instances of instructor social emotional positive(32) or negative (3) and 74 instances of student socialemotional positive (73) or negative (1). As one mightexpect, the most frequent social communication,occurring in 18 out of 34 passages, is the socialexchange “hello” or a variation such as “greetings.” Anexample of the instructor making a social emotionalpositive response to a student, in which he showspraise, encouragement, or approval, can be found inthe following passage:

You’re doing fine, Jinny. For someone here only afew weeks, your English is good. I could not resistusing your words to make a point for the benefitof the class even though I suspected you probablyshould not have been taken “literally.”

Even the instructor’s social emotional negativecomments are couched in positive language:

Jia, the colorful screen is very pretty but trying toread pink on gray is very difficult for those my agewho need about twice the contrast level that youcan use when you read. When you get a chance totake CIS 732, you will learn those things!

Students showed social emotional positive responsesboth to the instructor and to one another. Onestudent agreed with the instructor’s observation oranalysis: As you surmised, I do like being a systemsanalyst better than being a manager. They frequentlyexpressed solidarity with their peers: Nice to meetyou! Like you, I have about 14 years in IS. I also havechildren (3) and I live in South Jersey, taking thiscourse as a distance-learning student. I hope to keepin touch.

Expectations: Jarvenpaa and Leidner also foundthe communication behaviors of communicationconveying enthusiasm to facilitate early trust; thesecorrelate with our codes of positive expectationsabout the course (Code 1) and social emotionalpositive reaction to each other (Code 3). Findingsregarding Code 3 are presented in the previoussection “Early Communication.” We found evidenceof positive expectations about the course (Code 1) in34 instructor instances and 89 student instances.The instructor connotes enthusiasm for the course inthe following passage: This is one of the reasons I likemerging the face to face and the distance students inthat we do get this rich mix of backgrounds. Studentsexpress positive course expectations, as exemplifiedin this passage: I’m looking forward to getting to know

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you. I hope I will have interesting discussions aboutdiverse issues in the class.

Task-Related Communication: In early trust formation,Jarvenpaa and Leidner found member actions ofcoping with technical and task uncertainty and oftaking individual initiative to be important. Ourtask area codes correlate with these codes: Asksfor help (Code 5), Gives help or information (Code6), and technical/logistical/procedural (Code 7). Inthe Introductions conference, the task is instructorand student introduction with personal backgroundinformation and course expectations. We foundevidence of coping with task uncertainty mostfrequently in Giving information (Code 6), with 15instructor passages and 53 student passages found.Typically, students give information in their initialposting, or new post, in the conference. In theirreplies to the instructor or to one another, studentsgive suggestions or opinions: If you don’t have projectmanagement experience, then I suggest you take thecourse Project Management. The instructor is a veryexperienced project manager. The instructor mightgive task direction: You can go to the directory andchange your nickname to something more humanthat is easier for us all to remember. In Asking forhelp or information, we found 12 student and 6instructor instances. The instructor frequently asksa student to expand or clarify task informationgiven: I’m also interested in SW dev life cycle. Couldyou briefly describe “Rapid Application DevelopmentMethodology?” Students ask for confirmation of theirunderstanding: I don’t think this course will teachyou how to be a project manager. Am I right on this?In technical issues (Code 7), we found only oneinstructor instance and none from students.

We believe that this initial analysis shows thatsuccessful “virtual professors” establish communitywith their virtual groups by forming a unique kindof swift trust in the very first week of online classes.Would this swift trust carry over in the remainderof the semester? Would weeks one and two predictweeks nine and ten?

Later Communication: Later trust formation ischaracterized by predictable communication,according to Jarvenpaa and Leidner. We found thisregular pattern of communication by comparing theinstructor’s General Discussion conference over thecourse, a conference specified for questions or issuesthat do not fit into the other topic-related forums.The frequency count table in Table II shows a fairlyconsistent pattern in General Discussion in weeksone and two and in weeks nine and ten. Later memberactions that facilitated trust, according to Jarvenpaaand Leidner, are task centered. We found significantevidence of task focus in examining the conferenceDevelopment Process discussion. The conferencetopic, management of software development process,

TABLE IISwift trust coding frequency counts for Professor T

relates to task. The tabulated new posts and repliesin Table II show a rich pattern of discussion relatingto a case-study assignment.

Ineffective Online Teacher For comparison, weselected Professor X’s course on Computers andSociety. Similar to Professor T, the instructorwith the highest ranking, Professor X used acomputer-mediated system in a “mixed” mediumcourse that combined face-to-face and onlineinteraction. Unlike Professor T, however, ProfessorX had only one discussion conference that includedwelcome and introduction as well as task instructions.

As we noted earlier, the Introduction conference issingularly important in establishing the atmospherefor the entire course. Here, the professor establishesher or his persona, sets the tone for the coursediscussion, and provides a model for the dialogue thatfollows. Professor X began his online discussion withthis introduction: Welcome. Please introduce yourselfto the members of this conference. Tell us a little aboutyourself. (i.e., what’s your major? why are you takingthe class? what do you want to do after you graduate?,etc.) He did not include any information about himself

TABLE IIISwift trust coding frequency counts

Professor X

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in this posting or in follow-up messages. Perhaps, itis not surprising that the first student response tohis introduction was, My name is XXX. . . you don’tneed to know my last name. . .. I am CIS major and Ihave no free time to enjoy to myself. . .I work and I got15 credits to worry about.

Early Communication: Using the swift trust codingscheme, we examined the conference transcript forexamples of behaviors and actions that facilitate earlytrust formation. These early communication activitiesof social exchanges and social communicationcorrelate to our coding categories of social emotionalpositive response to each other (Code 3) and socialemotional negative response to one another (Code4). Out of 129 coded passages in the conference, wefound four instances of instructor social emotionalpositive and two instances of instructor socialemotional negative. For student postings, we found 52instances of social emotional positive and 6 instancesof social emotional negative.

Professor X’s early communication is difficultto characterize. In reply to one student whoenthusiastically proclaims that America is a greatplace to explore, he genially inquires, So, where haveyou explored already? To a student who has lost afamily member, Professor X is sympathetic, I’m verysorry to hear about your uncle. What a horrible thingto have happened. However, when students makemistakes with the conferencing system, he chidesthem: You responded to your own comment? or Youdidn’t write anything?!? Professor X’s longest postingin the conference is a failed attempt at humor: Oneof the things we’ll have to work on this semester isbringing you out of your shell. You have to learn not tohold back and to say what is on your mind. Yes, I’mbeing sarcastic. I can’t wait to hear your opinions onthe articles. (That is not sarcastic.) This message, lessthan a week into the semester, was the professor’slast response to students’ introductions.

In the instructor’s absence, however, the studentscontinued to post their introductions, giving the

TABLE IVIndexes for measuring course effectiveness

All items were reversed in scoring to make the high numbers equal “best.”Croncbach’s Alpha = 0:95

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information that was requested and making a socialemotional positive greeting or welcome. Typical is thisstudent’s introduction: Hello!!!!! This is XXXX. I amCIS major at Rutgers. Hope you are having fun in thisclass. Bye. Communication that conveys enthusiasm,which correlates with our code of positive expectationsabout the course (Code 1), was found in ten studentnew posts and replies: . . .I am looking forward to therest of the semester. . .Hope you are having fun in thisclass. . .I have a feeling that this class is going to beinteresting and it will be fun to be with everybody inthe class. This is my first semester taking classes herebut I feel good with the class.

Task-Related Communication: In the first two weeksof the conference, students coped with technical andtask uncertainty by asking for help and giving help toone another. Our task area codes correlate with these:Asks for help (Code 5); Gives help or information (Code6); and Technical/logistical/procedural (Code 7).

A requirement of this course on computers in societyis to post articles in the conference regarding ethical,legal, and social issues for computing systems.Therefore, the most frequently used code is Code6, which gives information. In addition to studentsgiving information in their initial posting in theconference, as well as in giving information to fulfillan assignment, students also gave help or informationto students who asked. For example, in response tothe question Hey do you know how to find the article

that he gave us today? another student responded:Just do the same with the previous assignment. Go toACM and just search it. Other students offered adviceabout obtaining the articles from home computers orfrom another campus: I just downloaded the articlefrom ACM and if you login from home through NJITto Internet, you are able to download the articlesdirectly to your pc You asked if we can telnet toPegasus from NJIT. Yes, you can. You have to telnet attelnet.pegasus.rutgers.edu.

In analysis of the course, there is little evidence tosuggest that the class ever formed a team, otherthan the artificially prescribed gathering of studentsposting assignments that also include introductions.Students seem to sense that the discussion boardwas an academic exercise not integral to learning, assuggested by the first student’s response indicatinghe had no free time and a later reply from anotherstudent This conference is way too cluttered withpostings. Watch NASCAR Winston Cup racing; it’s agood sport. We know that early communication iscritical to establishing trust, yet the instructor doesnot appear until after the course begins. He exits thediscussion within a week of the initial posting, toappear again only in week three to post a new task. Weexamined the conference transcript in weeks nine andten for an example of later trust formation. However,there were no postings during that period. There arescattered postings during the last weeks of class with

TABLE VItems included in the virtual classroom overall index

All items were reversed in scoring to make the high numbers equal “best.”Croncbach’s Alpha = 0:83

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this final conference posting, Just wondering how wecould get our grades. The next semester is just a fewdays away, and we still haven’t gotten our grades yet.

CONCLUSION

Our initial analysis shows that swift trust canform in a successful virtual learning community,but with unique communication and behaviors ascompared to face-to-face groups. If faculty membersbecome successful “Digital Socrates,” they overcomethe coldness in the electronic medium with socialcommunication clues in the conferences. The mosteffective online teachers get a good start in the veryfirst week, which is the essence of swift trust, withonline communication. Once established, swift trustwill carry over into the remainder of the semester ifhigh levels of action are maintained.

These analyses yield strategies that are applicableto all who are concerned with building trust andestablishing community in virtual teams. Thefollowing are four more suggestions.

(1) Establish early communication. Team membersneed to perceive the instructor’s presence assoon as they enter the course.

(2) Develop a positive social atmosphere. Teammembers respond to perceived caring in

instructional context. Instructors need to modelsolidarity, congeniality, and affiliation.

(3) Reinforce predictable patterns incommunication and action. Studentsneed carefully structured activities and regularresponses and feedback.

(4) Involve team members in tasks. To sustainearly trust formation, group members need tobe involved in meaningful tasks. Instructorsneed to motivate, encourage, and requireparticipation.

Our research has many limitations. We have analyzedthe conference transcripts of only one professor whowas rated the most effective online instructor and onewho was rated very poorly, at one institution, andin one field of study (information systems). To see ifour findings are able to be generalized would requiresimilar content coding of several courses with varyingdegrees of measured “success” at different institutionsand with different fields of study. However, we believethat based on the evidence of the importance ofcommunity, relationships, and trust evidenced in alltwenty of the faculty we originally interviewed, andthe initial analysis of communication between andamong instructor and students, we can conclude thatswift trust does form frequently in virtual learningcommunities and can predict the success of theremainder of the virtual group work.

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[15] N. Coppola, S. R. Hiltz, and N. Rotter, “Becoming a virtual professor: Pedagogical roles and asynchronouslearning networks,” J. Manag. Inform. Syst., vol. 18, pp. 169–189, 2002.

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Addison-Wesley, 1950.[18] L. A. Wood and R. O. Kroger, Doing Discourse Analysis. Thousand Oaks, CA: Sage, 2000.

Nancy W. Coppola (M’98–SM’01) researches the communication of online pedagogy and technology transfer. She directs the Master

of Science program in Professional and Technical Communication at New Jersey Institute of Technology (NJIT) where she teaches

all of her undergraduate and graduate technical communication courses online.

Starr Roxanne Hiltz is a Distinguished Professor at NJIT. She has spent most of the last 25 years researching applications and

social impacts of computer-mediated communication. Her latest book on online learning (co-edited with Ricki Goldman) is Learning

Together Online: Research on Asynchronous Learning Networks (Erlbaum, expected publication 2004).

Naomi G. Rotter is a Professor of Management in NJIT’s School of Management. She received her Ph.D. in Industrial and Organiza-

tional Psychology from New York University. Her interest in asynchronous communication extends from research in the workplace

using telecommuting to education using distance learning. In particular she has studied what behavioral changes occur as the result

of remote links between manager and employee and between professor and student with the focus on the manager and the professor.

Her homepage is http://eies.njit.edu/~rotter.