Ridhima Mehra, Auckland University of Technology, New Zealand
ridmeh85@aut.ac.nz

Mehra, R. (2006, July), Impact of User Participation on Consensus in Group Collaboration: An Adaptive Structuration Perspective. Bulletinof Applied Computing and Information Technology Vol. 4, Issue 1. ISSN 1176-4120. Retrieved from

ABSTRACT

Understanding how user participation influences consensus in group collaboration and how an information system influences this process, is receiving much attention currently. To study the potential link between user participation and group outcome, Adaptive Structuration Theory (AST) is used as a framework. A modified version of AST is used, that permits a more meaningful examination of the role of user participation towards group outcome. It is proposed that user participation is a critical determinant of the success of a group in completion of its task. The study also brings to light that users' perceptions about a technology are a critical determinant of the future use of any information system and have a tendency to influence the user participation in a collaborative environment.

Keywords

AST, Adaptive structuration theory, user participation, group collaboration, macro analysis, consensus

1. INTRODUCTION

The importance of user participation on group outcomes has long been recognized and has ignited among researchers a push to investigate and study the potential for applying information technology to support group work. Much research has gone into assessing how collaborative groupware systems and Group Decision Support Systems (GDSS) can affect user participation and subsequently outcome (in terms of consensus) of a group.

The process of decision-making gets complex when one moves from an individual to a group level. According to DeSanctis & Poole (1994) it is difficult to observe an appropriation process as it is subtle, but group interaction or participation is evidence of it. According to Chudoba (1999) and Hartwick & Barki (1994) “User participation” has considerable impact on the decision outcome. However, Lin & Shao (2000) mention that there is still ambiguity in association between user participation and outcomes, and despite the thorough research being carried out in this area, the findings remain inconclusive.

This highlights the research fous of this study: 'The impact of user participation on the outcome of a group wher 'outcome' is a measure of the 'consensus' achieved by the group.

The study uses the Adaptive Structuration Theory (AST) proposed by DeSanctis & Poole (1994), to look at discussions by group members and to arrive at a consensus while using a GDSS for collaborative learning. The data used were obtained from the collaborative trials conducted in semester 2 of 2003 between students from AUT and Uppsala University in Sweden. A macro-level coding scheme (Chudoba, 1999) to study discussion threads combined with qualitative analysis was undertaken to unfold the process of user interaction and participation while trying to reach a common consensus.

The next section of the article details AST as a framework discussing how it applies to the research question and variables of study. This then leads on to the description of context of the study and then a discussion of the data analysis techniques used. A section of the report analyses and interprets the results leading on to highlighting viable future research and limitations from this study, followed by a conclusion.

2. CONTEXT OF THE STUDY

This section discusses the environment under which this study was conducted and answers questions like who, what and how. The second section of the study relates the online activity with respect to a team organization framework adapted from Ilgen, Hollenbeck et al. (2005), to present the reader with a clear picture of where this study begins and where it is heading.

Following is a description of the context of data with respect to this study:

WHERE: The data used were obtained after semester 2 in 2003 from the collaborative trials conducted between students from AUT and Uppsala University in Sweden. WHY: The purpose of the collaborative trial was to introduce the students to web-based groupware technology and study the effectiveness of online collaborative learning. HOW: For the purpose of the same, students were linked across geographical borders where there were differences with respect to time, space, culture etc and were to jointly undertake a task.

The online collaborative trial had two phases to it, namely:

Phase 1: Icebreaking which involved the participating students to introduce themselves and familiarize thmeselveswith the Database.

Phase 2: Preference ranking which involved a collaborative task to evaluate and rank websites. In this phase the desired outcome was to reach a common group consensus on website ranking.

Following are the sources of data used in this study:

• Cyber Data: Questionnaires and feedback that students submitted at the end of the ice-breaking
• Final Data: Questionnaires and feedback that students submitted at the end of the collaborative trial
• Qualitative data available from discussion boards

These questionnaires and discussions were a reflection of the collaborative trial and the process of virtual collaboration. The participants were interacting using two media - email and the online collaborative trial database. Students were also encouraged to submit weekly feedback reports and keep in constant touch with the lecturer.

FOCUS: This research studies the impact of user participation on the outcome of a group, which is measured by the level of consensus attained during group participation for ranking of the websites in this collaborative trial.

According to Ilgen, Hollenbeck et al. (2005), user participation in a GDSS can be depicted as an IPOI framework (input—process -output- input) framework. The GDSS technology acting as an input generates processes in a group; this in turn leads to certain outputs, the predictability of which is based on the stability of appropriation (Gopal, Bostrom et al. 1992).

Figure 1. An IPOI Model for collaborative virtual interaction. Adapted from Ilgen, Hollenbeck et al. (2005)

The IPOI model by Ilgen, Hollenbeck et al. (2005), as illustrated in Figure 1 above, is a graphical representation of the two phases of the collaborative trial - Icebreaking and Preference Ranking, as explained previously.

At the input stage time zero (T0) some ice-breaking activities were initiated among the participants, which led to an output at Tx, where teams knew each other relatively well. Tx now becomes the input of phase 2 where participants were asked to rank the websites and reach a consensus, which represents the final output at Tn. It is important to highlight that this figure represents a mid process snapshot an emergent state at Time x (Tx) of a relationally additive framework.

This research analyses phase 2, trying to determine how user participation in a task oriented environment influences the outcome, Tn.

Whereas AST is a static model the process of user participation is dynamic and changes over time because sources of structure and appropriation influence user participation and vice-versa. Also the outcome has a tendency to influence the sources of structure or the way they are appropriated by the group. Figure 1 illustrates the same.

3. ADAPTIVE STRUCTURATION THEORY

Adaptive Structuration Theory, more commonly referred to as AST, has its roots in Anthony Giddens’ Structuration Theory in which Giddens speculates that human actions while institutionally constrained, also influence and alter institutional arrangements or structures, according to Giddens (1979) cited in DeSanctis & Poole (1994).

This framework proposed by DeSanctis and Poole (1994), intertwines together information technologies, social structures and human interactions, and helps study the relationship between them. DeSanctis and Poole (1994) mention that there is a recursive relationship between technology and action, each iteratively influential on the other.

The main focus of this theory is on interaction between group members, which in the context of social technology is “participation” (DeSanctis and Poole, 1994). AST looks into the process of human usage of computer systems and at the nature of group-computer interaction (DeSanctis and Poole, 1994). In other words AST opposes the “techno centric” view of technology and concentrates on what may be referred to as the “socio-centric” view of technology. This study uses a GDSS designed specifically in Lotus Notes to illustrate the principles of AST and study how user participation can affect group outcome. Two concepts central to the notion of AST are “Appropriation” and “Spirit”.

Salisbury & Stollak (1999) define appropriation as the “mode or fashion in which a group recreates a GDSS structure for its use”. When using GDSS groups tend to create perceptions about their role and the utility of technology. DeSanctis and Poole (1994) suggest that these self-developed notions have a tendency to influence the outcome of the group decision-making process.

With respect to AST, user participation is an instance of “appropriation”. According to DeSanctis and Poole (1994) the concept of “appropriation” means studying the user as the analysis unit that is studying the structurational impacts of a collaborative system at the individual level. Viewing “appropriation” as a part of AST helps to create a better understanding of “appropriation” since AST focuses on actions and interactions.

“Spirit” refers to the general goals and attitudes that the technology aims to promote. DeSanctis and Poole (1994) state that “Spirit” is concerned with issues like “what goals does the technology promote? What kinds of values are supported?” rather than looking at the technological functionalities of the collaborative system like “what does the system look like or what modules it contains”.

Scholars and practitioners have long recognized the benefits of facilitating collaboration and participation among group members. However several literatures have been published on GDSS and other technologies, but the research gap lies in identifying how these systems can impact the outcome of the organizations and groups that use them.

The aim of this research is to study, using AST, how participation of group members (“user participation”) can impact the decision outcome of the group.

In the context of this research paper “user participation” is explained using the model by Hartwick & Barki (1994) as below.

Hartwick & Barki (1994) define “user participation” as the “behaviors, assignments and activities that users or their representatives perform” in a collaborative information system. For example with reference to the collaborative trial, user participation is an aspect of the LVTs (Local Virtual Team) and GVTs (Global Virtual Team) ranking websites and achieving a consensus. Figure 2 depicts the three dimensions to user participation as proposed by Hartwick & Barki (1994).

Figure 2. Dimensions to user participation. Adapted from Hartwick & Barki (2005)

These three dimensions of “user participation” proposed by Hartwick & Barki (1994) are used as a broad categorization for classifying various elements comprising the AST model. These are discussed in detail next.

Dimension 1: Overall Responsibility identified by elements of “Structural Features” in AST measured by the following factors:

• Capability of the collaborative system to offer group decision making;
• Capability of the collaborative system to offer an effective means of support for group work.

Dimension 2: User-IS relationship identified by elements of “Spirit” and “Style of Interaction” in AST measured by the following factors:

• Was the collaborative trial enjoyable and worthwhile in nature?
• Did the collaborative trial promote group interaction?

Dimension 3: Hands on Activity identified by the element of “Knowledge and Experience with Structures” in AST measured by the following factors:

• Learning gained through collaborative trial.

Based on the user-participation model above I propose certain modifications to AST for use as a broad conceptual framework on the following work examining user-participation and its impact on the outcome of a group. Due to the complex nature of the framework only specific aspects of AST have been chosen in the study. Figure 3 is a graphical representation of how AST is deployed in relation to the chosen research question. In the context of AST the use of collaborative software can be depicted as an input-output process. (Gopal, Bostrom et al. 1992) The ideas for the representation in Figure 3 have been adapted from Avolio & Kahai (2000) and DeSanctis & Poole (1994).

Each of the sources of structure in a traditional AST is clarified to define exactly what they shall represent with respect to this research. For example, “Spirit” is measured with respect to the capability of the system to promote enjoyment. Similarly “knowledge and experience with structures” is understood as the capability of the system in being able to succeed in providing better learning than the classroom learning environment. Likewise with DeSanctis & Poole (1994), organizational environment is a source of social structure, which has the tendency to influence the appropriation moves of a group. On the other hand a group’s internal system has the tendency to influence the appropriation process. DeSanctis & Poole (1994) suggest that the greater the degree to which members agree on appropriation the higher the consistency in the group’s decision process.

To summarize the above, if the group interaction process (a source of appropriation) is consistent with the structural features and spirit of the technology, then the outcomes of a group decision process are more favorable. It is important to emphasize that for the purpose of this research, outcome is a measure of the consensus that is measured by the number of groups who succeeded in ranking the websites at the LVT (local virtual team) and the GVT (global virtual team) level in the collaborative trial.

Figure 3. AST - Chosen aspects for study. Adapted from DeSanctis & Poole ( 1994) and Avolio & Kahai (2000)

4. RESEARCH METHODOLOGY

From a total of 93 students participating in the trial there were 5 GVTs and 3 LVTs for each location - Auckland and Uppsala (Table 1).

Table 1. GVTs by topic and location

A sub-group within a specific location for a topic area is referred to as a LVT, whereas a group comprising LVT’s categorized by topic area irrespective of the location refers to a GVT.

Example, DNZ + HNZ + KNZ = LVT and

DNZ + HNZ + KNZ + DSE + HSE + KSE = GVT

Data were analyzed using macro analysis and qualitative data analysis, as  explained next.

4.1. Macro Analysis

Following is an explanation of the macro analysis coding technique deployed in this study.

According to Chudoba (1999) macro analysis is a scheme that revolves around junctures that occur during group meetings whether face-to-face or online. It is not only easy to use but also gives the researcher insight into appropriation and structuring activities in a group. Chudoba (1999) describes juncture as an actual change in what the group is doing or an attempt by a group member to change the group’s direction or activity. Researchers in the past have found junctures very helpful in studying the interaction within groups. Chudoba (1999) also highlights that junctures are an important aspect in assessing appropriations and patterns within groups.

Following are a few noteworthy points when using the macro analysis methodology:

• Only one juncture is applicable to a specific discussion;
• Junctures categorize the entire topic of the set of discussion;
• Two codes can not be used to identify the juncture;
• Use of / implies the group moved back and forth during discussion and - indicates the movement from one phase to another.

According to Chudoba (1999) electronic discussions have 5 types of junctures identified with them and detailed as follows:

• EC: Electronic conflict or dissatisfaction;
• EK: electronic task (what the group is working on);
• EP: Electronic process (how the group should accomplish its task);
• ES: Electronic social (social conversation in the group which is not related to the task);
• ET: Electronic technology (issues relating to software, hardware etc.).

Table 2 presents an example of the coded discussion, which was obtained from the online discussion forum.

Table 2. Coded transcripts for neural networks, BNZ

Table 3 presents the summary of appropriations that occurred within the LVTs during the process of ranking the websites.

Table 3. Summary of junctures during group participation

The above results indicate a clear approach to identify the group’s interaction process during the collaborative exercise. Examples are shown as to how the junctures were identified in discussions (table 2) followed by a summary of the appropriations within the group (presented in Table 3).

The goal was to identify what junctures were being used during the group interaction process and assign a type of juncture to the various discussion threads within each topic group.

4.2. Qualitative Data Analysis

This section details on the qualitative data analysis used in this research.

The data were obtained from the cyber- and the final questionnaires that students were asked to fill in at phase 1(Tx) and phase 2 (Tn) of the collaborative trial (refer IPOI model, Figure 1). There were a total of 10 questions on a 5 point scale, namely: Strongly Disagree, Disagree, No Firm Opinion, Agree and Strongly Agree.

The data from the online website was captured using MS Excel and then transferred to MINITAB, for a thorough data analysis. Using MINITAB data filtering was done to study the questions relevant to the purpose of the research. Out of a total of 10 questions the research paper focuses on 6. The remaining four were eliminated because of their minimal/nil contribution to the focus of this study.

In this research the following factors were analyzed, as identified by the question numbers below:

• Decision Making offered by the collaborative system Without Email - Question 6 and 7;
• Enjoyable nature of the collaboration - Question 8;
• Effective working in group - Question 4;
• Learning offered by the virtual collaboration - Question 3;
• Worthwhile nature of the trial - Question 9.

The next step was to convert the data from text to numeric on a scale of 1 through to 5 where 1 stands for “strongly disagree” and 5 stands for “strongly agree”. Percentages were used to quantify each of the relevant questions. Histogram plots were also graphed with and without “Location - Auckland or Uppsala” being a determining variable. The reason for this is more evident in the discussion section of this report. Following is an example - Figure 4:

Figure 4.  Sample percentage graph for Question 4

Based on this example and the macro analysis, the following section presents an analysis and discussions of some of the key findings.

5. DISCUSSION

This section aims to give an understanding behind the discussion process of the group, the identified junctures and dimensions of user participation in Figure 2. The results obtained from the qualitative data analysis of the questionnaires are then tied back to the chosen framework of AST.

Table 4 is a summary of the four main concepts that this research paper revolves around - dimensions of user participation, relevant elements from AST, junctures in macro analysis and the relevant questions from the feedback questionnaire that the students were asked to fill out.

Column 1 and Column 2 tie the dimensions of user participation to the elements in the AST model, as explained previously in the paper on page 5. Questions that were relevant to each element in the AST are detailed in column 3. Column 4 then identifies the type of juncture applicable to the question/element from AST or the dimension of the “user participation” model.

In a nutshell, Table 4 ties together the theoretical and the analytical aspects of the research, in an attempt to identify the patterns of evolution of the participation process and its impact on the outcome of a group.

Table 4. Relational conjunction between user participation, AST and data analysis methodologies (macro analysis and qualitative data analysis)

Analysis of Table 3 reveals that out of a total of 40 identified junctures a majority (62.5%) are electronic task oriented. The second most share is taken by 'electronic conflict' or 'dissatisfaction' with 17.5% followed by 'electronic process' and 'electronic technology' taking an equal share of 7.5% each and the least share for electronic social of only 5%. Table 5 below provides a summary of the junctures:

Table 5: Summary of junctures as percentages

The tables form a basis for a discussion of the findings as detailed in the following sub-sections.

5.1 Completion Rate

Detailing “Completion Rate”, the discussion threads are strong evidence to the LVTs agreeing and reaching at a common consensus. A closer comparison between table 1 and table 3 indicates that 14 out of 30 LVTs responded to the task of ranking and discussing websites virtually and of these 13 LVTs were from Auckland and one from Sweden. In other words the overall completion rate was 47% of which Auckland students took a major share of 43%.

This low completion rate within the Swedish students can also be attributed to the fact that they did not make a serious commitment towards the success of the collaborative trial. The figures were 31% and 15% based on the cyber and the final questionnaires respectively. 

Figure 5.  Group work in LVTs by area

Even though the Auckland LVTs showed high levels of commitment in the beginning it did drop towards the end where none of the LVTs managed to succeed at the GVT level. (Lack of commitment towards the collaborative trial went up from 13% in cyber evaluation to 22% in final evaluation for Auckland LVTs.)

Not many LVTs managed to reach a common consensus on the GVT level. This is also supported by the analysis of the questionnaire at the end of the collaborative trial where students were asked whether the project enabled them to work effectively in their virtual group. More than half of the students that is 54% thought that the project did not enable them to work effectively in their group. Only 20% of the students agreed to this and approximately 26% had no say or no opinion on the matter.

Figure 5 depicts the pattern for the cyber and final questionnaires for the above. The red bar represents Auckland LVTs and the green Swedish LVTs.

Studying the figures by location, it can be establised that while 25% of the Auckland students agreed on the collaborative exercise promoting effective working in a group, only 14% of the Swedish students agreed to this in the final questionnaire. Surprisingly the percentages for disagreement from both locations were similar.

Hartwick & Barki (1994) in their participation, involvement and system use model suggest that the failure of an information system, for example a GDSS, to promote effective group work can consequently be a cause of negative attitude towards the use of the system, affecting its use by the intended audiences or participation and ultimately affecting the outcome of a group. This suggests that user participation directly or indirectly does have an influence on the outcome of a group.

More participation at an LVT level for Auckland students led to a higher number of teams managing to reach a common consensus at a LVT level. In the case of the Swedish students, where participation was absent at a LVT level, the failure of groups to reach a common consensus was evident.

Even though the consensus was reached at the LVT level it was lacking at the GVT level. This can be attributed to several factors such as lack of user participation and commitment, and failure of the project to support effective group work as highlighted previously.  However other factors such as "Motivation" and “Spirit” of the technology do contribute to this and these are highlighted in the following section. This was also confirmed during the discussions between the coordinators of the course at both universities.

5.2 Spirit of the System

According to DeSanctis and Poole (1994) the mode in which structures are appropriated is determined along three dimensions - faithfulness of appropriation, the group’s attitude towards the GDSS and the group’s level of consensus on the appropriation. “Faithfulness” refers to the extent to which a group uses GDSS in the “Spirit” which it is indented to be used.

Based on DeSanctis & Poole (1994), “Spirit” of a collaborative system refers to the user’s perceptions and interpretations of the system. A “faithful” appropriation signifies adherence to the “Spirit”. For this study the chosen variables “the collaborative trial is enjoyable” and “students think it to be worthwhile” are a measure of the "Spirit" of the system. Looking at Figure 6, it is evident that a majority of the responses go against the "Spirit" of the GDSS.

Figure 6. Charts representing the “Spirit” of the technology (values as percentages)

Groups did not have the right attitude for the GDSS. On closer analysis, the feeling of disagreement for the collaborative trial not being enjoyable or worthwhile was much higher among the Swedish students than the Auckland Students, approximately twice as much. In the cyber evaluations 31% of the Auckland Students disagreed to the collaborative trial being enjoyable, but 63% of the Swedish students thought the same. Similarly, where for the cyber evaluation 31% of the Auckland Students did not think that the collaborative trial was worthwhile the percentage was much higher among Swedish students approximating to 75%.

A similar trend was observed for the final questionnaires for the enjoyable and worthwhile nature of the collaborative exercise. (Disagreement percentage was 40 for Auckland and 46 for Sweden for enjoyable and 28 and 68 respectively for the worthwhile nature of the collaborative exercise - refer Figure 6.

As supported from the above figures, it would be fair to comment that overall the GDSS was not used in the “Spirit” that it was intended to be. At the LVT level the “appropriation” of GDSS was faithful to some extent but unfaithful at the GVT level.

5.3 Task Oriented Discussions

“Task Oriented Discussions” can also be related to the somewhat obvious nature of the collaborative exercise. The instructors presented the virtual teams with a set of clear instructions and a sequence of steps to follow to help them complete the task. Therefore the groups did not need to engage in the procedural or technical discussions one usually requires to complete a task. With reference to the article by Chudoba (1999), it can be rightly said that as the groups moved closer to completing the task, their “next-steps” were more obvious.

Previous discussions on the worthwhile nature of activity, “Spirit” etc. also explain somewhat why discussions were task oriented and revolved around finishing the task of ranking the websites and failed to create a social atmosphere among the participants.

5.4 Lack of Socialization

It can be presumed that the groups used some other medium as for example email, chat and sms to interact or have a light moment among them. Personal communications with the module co-coordinators did reveal the use of other mediums by the students (T. Clear, personal communication, December 13, 2005). This is also supported by the analysis of the questionnaire. The GDSS did not meet the expectations of the participants in providing socialization and efficient decision-making on its own. More than half, that is 54%, of the participants agreed that the use of email enhanced the ability of GDSS to offer decision-making support. A major proportion of the participants either agreed (39%) or had no firm opinion (34%) on the same. However, why the group did not use the collaborative software for social interactions is an area that needs to be explored.

With reference to the User Participation Model illustrated in Figure 1, even though the system met one aspect of “Hands-On-Activity” for encouraging user participation because of it’s primarily task oriented nature, it did not maintain the expectations of building what is referred to as “User-IS” Relationship. In terms of “overall responsibility” of the system, the GDSS succeeded at LVT level but failed to go to the GVT consensus level and provide a means of being a source of better learning for the participants. More than half of the participants (55%) mentioned that the project using GDSS did not offer itself as a means of better learning. The trends were similar for students from both countries.

5.5 Summary of Findings

Tables 4 and 5 and the qualitative/quantitative analysis forms the basis for the summary of the findings presented as follows:

1. Completion Rate: On a LVT level, almost all the subgroups in Auckland were able to reach a common consensus on the website ranking. Only one Swedish subgroup measured at the LVT level was able to complete the task of ranking the website. However the groups were not able to succeed on a GVT level.
2. “Spirit” of the System: Both the Auckland and Swedish LVTs and GVTs did not use the GDSS in the “Spirit” in which it was intended to be used.
3. Task Oriented Discussions: Members and discussions at the LVT level were task oriented and revolved around finishing the task of ranking the websites. The focus was on the task rather than the process to get the task done.
4. Lack of Socialization: The collaborative software was unable to initiate socializing among or within the groups at both the LVT and GVT Level.

It can be conlcluded that user participation affects the outcomes of a group at the LVT and GVT level. The data analysis and interpretation of the results has suggested that the lack of user participation in the collaborative exercise was definitely a cause for the failure of more than half of the LVTs and almost all the GVTs to reach a definite consensus on the task of ranking the websites.

6. GENERALIZATION AND LIMITATIONS

The above dataset is a representative of a small population from the virtual collaborative community. However there are some inconsistencies in the dataset where students have not filled in fields like “Location”. Moreover one does come across entries where the responses are inconsistent and it seems that students may have filled in questionnaires for the sake of it without giving them much meaning. This is likely to cause some bias in the analysis and interpretation of results.

The “suggestion for improvement”, revealed that a lot of students made comments about improving the design of the interface and struggled to work their way round it. This is one issue that needs to be investigated to discover if and what impacts it has on user participation and how it can affect consensus.

Based on the macro analysis and qualitative data analysis it can be said that the discussion groups reflected passive styles of leadership where not every LVT/GVT chose to have a virtual team leader. However some participants were more active and questioning than others or even tried to take ownership to get the group going, by asking questions like” do you agree on this”, “let me know how the group feels about this”. Whether or not leadership influences user participation in a virtual collaboration, and what impact this has on outcome of a LVT or GVT is an area that needs to be explored in further research.

However through the feedback available from the students and the online discussions it was evident that there were serious motivation issues especially with the Swedish LVTs and this hindered the LVTs and GVTs from meeting the final goal of the trial. This was also raised as a concern in the article by Clear & Kassabova (2005).

This study focuses on the area of “user participation” and how that could affect the outcome of a group. As further research it would be ideal to study the cumulative effect of the further issues raised above on user participation and the outcome of a group.

7. CONCLUSION

This study investigating the impact of user participation upon the outcomes of Global Virtual Teams (GVTs) collaborating between New Zealand and Sweden, points out new facets in relation to the use of Adaptive Structuration Theory or AST as a theoretical model, specifically because it uses AST to study a dynamic process of user participation over time.

As is evident from the qualitative and quantitative analysis of the group decision process, user participation does influence the outcome of a group in terms of reaching a common consensus with respect to the specific scenario of the collaborative trial in this study. This is consistent with the literature in the field where group outcome (or consensus specifically) is directly proportional to the user participation.

Groups where the users participated reached a consensus at the Local Virtual Team or LVT level whereas others did not. Most of these were the Auckland LVTs. However the groups did not succeed at a GVT level because of lack of participation from the students. Reasons for the same have been highlighted previously.

The study also identifies limitations and avenues for further research. Analysis of the other aspects linked to user participation like motivation especially with the Swedish LVTs, and ease of use of technology can help us better understand how and why user participation influences group outcomes. Findings from the data analysis and online meetings indicate these are productive avenues for future research. 

8. ACKNOWLEDGEMENTS

The author wishes to show gratitude to Tony Clear for his unwavering support and valuable advice and to thank him and also Dr. Philip Carter for their excellent and inspiring lectures. The author would also like to thank Krassie Petrova for being an inspiration to reach excellence and Gordon Grimsey for prompt resolution of any issues in the working lab. The comments and suggestions of all the reviewers, fellow students and the lecturers in the Collaborative Computing and Workflow paper of AUT’s Master of Computer & Information Sciences programme have been greatly appreciated and valued.

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Copyright © 2006 Ridhima Mehra
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