Understanding the Hidden Dissatisfaction of Users ... - Semantic Scholar

IDEA GROUP Journal ofPUBLISHING End User Computing, 15(2), 1-22, Apr-June 2003 701 E. Chocolate Avenue, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com

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Understanding the Hidden Dissatisfaction of Users Toward End-User Computing Nancy Shaw, George Mason University, USA Joo-Eng Lee-Partidge, Central Connecticut State University, USA James S.K. Ang, National University in Singapore, Singapore

ABSTRACT The objective of this research is to examine satisfied and dissatisfied end-users in an organization to determine if they hold different technological frames of reference towards end-user computing (EUC). This research examines the effectiveness of the computer systems at the organization, while at the same time measuring the level of end-user satisfaction with the EUC environment. Grounded theory techniques for qualitative analysis of interviews were used to assess the technological frames of reference of selected highly satisfied and highly dissatisfied users. While analysis of the satisfaction surveys alone indicated that the user population was generally satisfied with their EUC environment, follow-up interviews and service quality gap analysis highlighted several individual support areas that required remedial action. In addition, satisfied and dissatisfied users held different views or technological frames of reference towards the technology they used. Their frames of reference affected their expectations of the technology, their interactions with the MIS support staff, and their utilization of the technology on a day-today basis. Keywords: end-user computing, cognitive structures, end-user support, user satisfaction

INTRODUCTION This research examines the different views and perspectives of individuals in an organization toward end-user computing (EUC) and EUC support, and how those

views can affect end-user satisfaction. End-user satisfaction has long been used as an important surrogate measure of information system success (Zmud, 1979; Doll and Torkzadeh, 1988; DeLone and McLean, 1992; Torkzadeh and Doll, 1993; Buyukkurt and Vass, 1993; Henry and

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2 Journal of End User Computing, 15(2), 1-22, Apr-June 2003

Stone, 1994; Guimaraes and Igbaria, 1994; Mirani and King, 1994; Seddon, 1997; Blili et al., 1998; Foong, 1999; Mahmood et al., 2000; Aladwani, 2002, Shaw et al., 2002). End user satisfaction is a perceptual or subjective measure of system success, serving as a substitute for objective determinants of information systems effectiveness (Ives et al., 1983). We are interested in how an individual’s view or perspective can affect end-user satisfaction. In social cognitive research, views and perspectives, also known as frames of reference, have been used to explain an individual’s mental processes. A few studies in the IS area have been conducted to understand the views or attitudes individuals hold towards technology (Bostrom and Heinen, 1977, Dagwell and Weber, 1983; Noble, 1986; Pinch and Bijker, 1987; Kumar and BjornAnderson, 1989; Jawaher and Elango, 2001). The term “technological frame of reference” was introduced by Orlikowski and Gash (1994) to describe the underlying assumptions, expectations, and knowledge that people have about technology. In the current study, we extend the idea of technological frame of reference to assess the views users hold towards EUC. In particular, we are interested in determining if satisfied and dissatisfied users hold different views of the technology, and ultimately if those different views influence their satisfaction with that technology. In particular, we examine the effectiveness of end-user support in an organization, the satisfaction of end-users with that support and the technological frames of reference of those users. By concentrating on the differences between satisfied and dissatisfied end-users, we hope to deepen our understanding of end-user satisfaction and dissatisfaction so as to iden-

tify contributory factors that lead to dissatisfaction. We use a combination of quantitative and qualitative analysis in our case study. An instrument measuring end-user satisfaction was used to assess the satisfaction of individual users with the overall EUC environment, and service quality gap analysis was used to measure the effectiveness of the support organization in the organization. Grounded theory techniques (Glaser and Strauss, 1967) were used in the qualitative analysis of interviews to assess the frames of reference of selected satisfied and dissatisfied users.

CONCEPTUAL FRAMEWORK AND RESEARCH MODEL The objective of this research is to examine satisfied and dissatisfied end-users in an organization to determine if they hold different technological frames of reference towards end-user computing (EUC). Can their different frames of reference be used to explain their different satisfaction levels? What is the relationship between satisfaction with end-user support and satisfaction with the overall end-user computing environment? The research model is presented in Figure 1. Measuring EUC Satisfaction Several different tools have been developed to assess end-user computing satisfaction. Two validated instruments commonly used to measure satisfaction with end-user computing are the Doll and Torkzadeh (1988) instrument and the Ives, et al. (1983) instrument. These instruments can be used in one of two ways: as a straightforward measurement of the level of satisfaction within an organization, or as


Journal of End User Computing, 15(2), 1-22, Apr-June 2003 3

Figure 1. Research Model

Satisfaction with end-user support

End-User Satisfaction

End-User’s Technological Frames of References

a tool to identify factors or determinants that can affect satisfaction. This study uses a variation of the Ives et al. (1983) instrument developed by Mirani and King (1994) that was specifically adapted for the EUC context (see Appendix A). A review of the EUC satisfaction literature (shown in Table 1) surfaced several factors that are shown to significantly influence EUC satisfaction. The results from end-user satisfaction studies are quite variable, with some studies giving support to the influence of one factor while others find little or no support for the same variable. In a meta-analysis of 45 end-user satisfaction studies, Mahmood et al. (2000) separate factors that affect satisfaction into three general categories: perceived benefits and convenience, user background and involvement, and organizational attitude and support. As listed in Table 1, end-user support was shown to have significantly affected EUC satisfaction in eight studies. As we are interested in examining the existing partnership between the IS department and endusers in an organization, and given the sig-

nificance of end-user support as highlighted by previous studies, we decide to concentrate on the effect of end-user support on EUC satisfaction or dissatisfaction. Measuring the Effectiveness of EUC Support As indicated in the section above, prior research has shown that end-user support contributes to end-user satisfaction (Buyukkurt and Vass, 1993; Lederer and Spencer, 1988; Rainer and Carr, 1992; Bowman et al., 1993; Brancheau and Wetherbe, 1988; Trauth and Cole, 1992; Mirani and King, 1994; Shaw et al., 2002). One measurement of support is service quality, which measures how well the service level delivered matches customer expectations (Lewis and Booms, 1983). Service quality is more difficult to measure than product quality, as it is a function of the recipient’s perception of quality. For example, one end-user may expect installation of a new software package to take an hour and be very happy that it takes 45 minutes; another may be unhappy when expecting it



Table 1. Factors that influence EUC satisfaction End-User participation in design

Doll and Torkzadeh (1988), Montazemi (1988), Mirani and King (1994), AmoakoGyampah and White (1993), McKeen et al (1994), Lawrence and Low (1993), Guimaraes et al (1992), Hartwick and Barki (1994), Baroudi et al (1986), Yoon and Guimaraes (1995), McKeen and Guimaraes (1997), Park et al (1993 – 1994), Saleem (1996), Choe (1998) End-User computing self efficacy (i.e. the Henry and Stone (1994), Montazemi (1988) belief that one is able to master a particular behavior) Technical support Buyukkurt and Vass (1993), Lederer and Spencer (1988), Ranier and Carr (1992), Bowman et al (1993), Brancheau and Wetherbe (1988), Trauth and Cole (1992), Mirani and King (1994), Shaw et al. (2002) Documentation Torkzadeh and Doll (1993) Management Support Henry and Stone (1994), Guimaraes, et al. (1992), Lawrence and Low, (1993), Igbaria et al., (1995), Yoon and Guimaraes (1995) Ease of System Use Henry and Stone (1994), Davis (1989), Igbaria et al. (1995), Davis et al. (1989) Previous computing experience Henry and Stone (1994), Lehman and Mukthy (1989), Lawrence and Low (1993), Palvia (1996), Montazemi et al (1996), Ryker and Nath (1995), Yoon and Guimaraes (1995), Thompson et al (1994), Venkatesh (1999), Chan and Storey (1996), Igbaria et al (1989), Blili et al (1998) End-user computing attitudes Henry and Stone (1994), Lee et al (1995), Hartwick and Burki (1994), Davis et al (1989), Thompson et al (1994), Satzinger and Olfman (1995), Aladwani (2002), Shaw et al (2002) Outcome Expectancy Henry and Stone (1994) Existence of Hot Line Existence of Information Center Bergeron and Berube (1988) Number of systems analysts Montazemi (1988) Level of requirements analysis Proportion of online applications Degree of decentralization Standards and guidelines Data provision support Mirani and King (1994) Purchasing relating support Variety of software supported Post development support Training on backup and security New software upgrades New hardware upgrades Low percentage of H/S downtime



to take 15 minutes but having to wait 30 minutes for the activity to be completed. Objectively, the latter was more productive, but in the former the end-user was more satisfied. Service quality can be measured by a comparison of user expectations (or needs) with the perceived performance (or capabilities) of the department or unit providing the service. The difference between these two measurements is called the service quality gap (Parasuraman et al., 1985). Parasuraman’s work resulted in a 45-item instrument, SERVQUAL, for assessing customer expectations and perceptions of the quality of service in retailing and service organizations. Service quality has been the most researched area of services marketing (Fisk et al., 1993) Service quality measurements have been used in IS research as a measure of IS success. Recognizing that a major component of the product an IS department delivers has a service dimension, IS researchers have recently begun to look for ways to assess the quality of that service (Shaw et al., 2002). The gap analysis method was first used by Kim (1990) to measure the quality of service of an IS department. Pitt et al. (1995) used a 22item version of the SERVQUAL instrument developed by Parasuraman et al. (1985), to test the instrument’s usefulness in the MIS environment. They assessed several aspects of the instrument’s validity, including content validity, reliability, convergent validity, nomological validity and discriminant validity. They concluded that SERVQUAL could be used with confidence in the MIS environment. They also reported that the results of a service quality assessment was very useful in not only assessing current levels of service quality, but also as a diagnostic tool for determin-

ing actions for raising service quality (Pitt et al., 1995). The instrument itself has been the subject of considerable debate (Brown et al., 1993; Parasuraman et al., 1993; Fisk et al., 1993; Van Dyke et al., 1997; Pitt et al., 1997). The focus of the debate concerns calculating differences between two possibly different constructs: expectations and perceptions of performance. To counteract the concerns surrounding the validity of the instrument in an IS context, Pitt et al. (1997) demonstrated that the service quality perception-expectation subtraction is rigorously grounded. See Kohlmeyer and Blanton (2000) for a complete discussion of the SERVQUAL debate. Researchers generally agree that the instrument is a good predictor of overall service quality, and is applicable for use in the IS context (Fisk et al., 1993; Kettinger and Lee, 1997; Pitt et al., 1997). Remenyi and Money (1994) developed a service-quality instrument specifically for the EUC environment to establish the effectiveness of the computer service and to identify key problem areas with EUC. This instrument is used in the current study (see Appendix B). Assessing End-User’s Frame of Reference While measuring levels of end-user satisfaction in an organization is relatively straightforward and has been heavily documented, measuring or assessing views and perspectives of individuals is not so straightforward. This field of research originated in the social sciences domain. However, over the past few years, these concepts have been applied to several other areas of research, and more recently to those areas related to the management and use of computers.



The cognitive sciences suggest that the world as it is experienced does not consist of events that are meaningful in themselves. Cognitions, interpretations, or ways of understanding events are all guided by what happened in the past (Schutz, 1970). When faced with an unknown situation or object (artifact), we automatically create our own interpretation of what that artifact is. Which particular past experiences are called up and how those experiences are imposed onto a structure are what determine our individual cognitive structures (Gioia, 1986). Individual cognitive structures, or schemas, allow individuals to draw on knowledge and past experiences to help them make sense of information. They influence perception and memory, and can be both facilitating and constraining. Schemas can change over time; existing schemas can also inhibit the learning of new schemas (Markus and Zajonc, 1985). A minimal amount of research has been conducted on the social cognitive perspectives that individuals hold towards technology. Bostrom and Heinen (1977) first introduced the concept of frame of reference when they suggested that some of the social problems encountered during the implementation of information systems were due to the frames of reference held by the systems designers. Later work by Dagwell and Weber (1983), Kumar and Bjorn-Anderson (1989) and Boland (1978, 1979) expanded on the earlier study by examining the influence of the designer’s values and conceptual framework on the resultant systems. This earlier work became the basis for a group of studies investigating the social aspects of information technology that considered the perceptions and values of both the designers and users (Hirschheim and Klein, 1989; Kling and Iacono, 1984; Markus, 1984). While these studies proposed the idea that indi-

viduals have assumptions and expectations regarding technology, Orlikowski and Gash (1994) expanded on this concept to emphasize the social nature of technological frames, their content, and the implications of these frames on the development, implementation, and use of that technology. Technological frame of reference was introduced by Orlikowski and Gash (1994) in a study that proposed a systematic approach for examining the underlying assumptions, expectations, and knowledge that people have about technology. They argue that an understanding of an individual’s interpretation of a technology is critical to understanding their interaction with it. Of particular significance in Orlikowski and Gash’s work is the discussion of the contextual dimension of frames. Members of a social group as a whole will come to have an understanding of particular technological artifacts, including not only knowledge about the particular technology, but also a local understanding of specific uses in a given setting. Earlier work by Noble (1986), and Pinch and Bijker (1987) had shown that technological frames could strongly influence the choices made regarding the design and use of technology, including adoption rates (Jurison, 2000). In our paper, we are interested in assessing the technological frame of reference that users hold towards end-user computing. In particular, we are interested in determining if satisfied and dissatisfied endusers hold different views of the technology, and ultimately if these different views influence their satisfaction with that technology.

ORGANIZATIONAL BACKGROUND Otis Elevator, a multi-national corporation headquartered in Connecticut, par-



ticipated in the study. This research was conducted at their Pacific Area of Operations Headquarters (PAO-HQ) in Singapore. Otis Elevator, a wholly owned subsidiary of United Technologies Corporation, was founded in 1853. It is currently the world’s largest manufacturer of elevators, moving walkways and other horizontal transportation systems. Their products are offered in more than 200 countries worldwide, with over 77% of sales occurring outside of the U.S. Manufacturing facilities are located in the Americas, Europe and Asia. The operations are regionalized into seven areas, with the Asia Pacific Area covering China, Korea, all Southeast Asian countries, north Asian countries, India, Australia, and New Zealand (Otis Fact Sheet, 2001). At the time of the study, the PAOHQ offices were located on two floors in a modern high-rise office building in downtown Singapore–it has since been relocated to Hong Kong. A separate marketing office responsible for local sales was located several miles away. PAO-HQ was responsible for sales, service and manufacturing operations spread out over 18 countries in their region. The headquarters office was divided into the traditional functional areas of Marketing, Operations, Quality Control, Finance, Engineering, Training, and HR. The MIS group reported to the Finance director. PAO-HQ employed approximately 100 people, 85 with a personal computer on the LAN. Internet connections were available, but were rarely used. The PCs ran either DOS 3.1 or Windows 95 with standard Windows office applications. The majority of the users used only MS office applications (Excel, Powerpoint, Word) and e-mail. Customized software was in use for specialized functions (e.g., an OTISwide financial reporting system, an OTISwide internal management reporting sys-

tem, and a PAO-HQ developed engineering support system), with the relevant employees using those applications. The PAO-HQ MIS group comprised five employees, all co-located with the other HQ personnel in the same office building. Two employees were directly responsible for LAN administration and end-user support at PAO-HQ. The remaining employees supported infrastructure development, maintenance and training for the 18 country locations. Request for support came in over the telephone or e-mail, and were logged daily. The majority of support calls were resolved by the second day either in person or over the phone by the MIS support group. A small percentage of calls were escalated to the application developer (either Microsoft or an OTIS developer in Connecticut). After-hours and weekend support was provided through pager calls to the support person on duty.

RESEARCH METHODOLOGY AND RESULTS Data gathering for our study was carried out in two phases. The first phase utilized a survey instrument, while the second phase was an in-depth case study using grounded theory techniques. Data gathering consisted of unstructured and semistructured interviewing, documentation review, and observation. This triangulation across various data collection techniques is beneficial because it provides multiple perspectives and yields stronger substantiation of constructs (Orlikowski, 1993). Phase One: The objective of this phase was to measure the effectiveness of the computer systems at Otis, while at the same time measuring the level of enduser satisfaction with that system. As mentioned earlier, the two most common instruments used to measure satisfaction with



end-user computing are the Doll and Torkzadeh (1988) instrument and the Ives, Olsen and Baroudi (1983) instrument. The Doll and Torkzadeh instrument was developed to measure “computing satisfaction” of an end-user with a specific application. In our research, the intent was to measure end-users’ overall satisfaction with enduser computing, not with a specific application. Therefore, we used a more general measure, the short form of the User Information Satisfaction (UIS) questionnaire originally developed by Ives et al. (1983) and later modified by Mirani and King (1994) for the EUC context. A service-quality instrument developed by Remenyi and Money (1994) was used to establish the effectiveness of the computer service and to identify key problem areas with EUC. Several additional questions were included to gather information on the user’s self-rated computing expertise, prior computing experience and training, and current computing usage patterns. Phase Two: Fourteen respondents comprising a mix of satisfied and dissatisfied respondents (identified during Phase One) were interviewed. Techniques for qualitative analysis (Miles and Huberman, 1994) and grounded theory (Glaser and Srauss, 1967; Martin and Turner, 1986; Strauss, 1987) were employed in the development of the descriptive categorizations used for the technological frames of reference. The software package NUD*IST was used to assist in the content analysis of the interviews. Analysis of Data–Phase One The site consists of approximately 85 end-users, running a variety of IBM-compatible PCs on a Novell Netware LAN. The majority of the end-users utilized Microsoft applications in a Windows envi-

ronment. Fifty-seven survey instruments were returned, yielding a response rate of 67%. The purpose of the survey was to develop a general user profile of the endusers, determine the support needs of these users, and rate the performance of the IS department in meeting these needs. Most respondents had between 5-10 years of experience with personal computers, and 2-6 years experience in a networked environment. Most respondents used their computers 3-6 hours a day, and rated their general level of PC expertise in the intermediate to advanced range. The respondents were also asked to rate their level of expertise for the applications they use at Otis. Applications with the highest number of users (word processing, spreadsheets, electronic mail, and presentation software) also had the highest mean levels of expertise. In contrast, the applications that were not used by many people (databases, Internet browser, electronic fax and flowcharting) showed lower mean levels of expertise. The general user profile is summarized in Table 2. The respondents were asked to evaluate 22 separate support items (see Appendix B). The items were first evaluated on a five-point Likert scales in terms of that item’s importance to the user in the performance of his or her job. These same items were then evaluated by the user according to the performance of the IS department when providing those items. The difference between the performance scores and the importance score indicates the effectiveness of the IS department in performing the various functions. A zero gap would indicate that there is an exact match between importance and performance. A positive gap indicates that the IS department is committing more resources than are required, whereas a negative gap indicates that the performance is less than the



Table 2: General User Profile (Otis Elevator Pte Ltd)

Number of Respondents Gender (if supplied)

57 Male Female

19 5

Job Function (if supplied) Manager End-User Yrs PC Experience (mean) Yrs PC Network Experience (mean) Hrs / Day on PC Self Efficacy Rating (general PC expertise)

5.2 Beginner Novice Intermediate Advanced Expert

importance, that is the IS department is underperforming. Since the gap is determined by subtracting the importance score from the performance score, a positive gap implies user satisfaction with that item, while a negative gap implies user dissatisfaction with that item. Analysis of this dataset surfaced several significant issues. A rudimentary ranking of the data by order of importance indicated which specific support areas were most important to the users, and which areas were not considered as important. Service quality gap analysis indicated which support areas were being satisfactorily or unsatisfactorily delivered, and correlation of the service quality gap with satisfaction indicated which support factors affected end-user satisfaction. Each of these issues alone was important, but when combined, they provided a much richer picture of the support environment at any organization.

11 15 8.24 4.43

1.9 % 7.4 % 38.9 % 48.1 % 3.7 %

A basic analysis of the importance and performance scores was performed. The mean and standard deviations for each of the 22 attributes were calculated. The mean perceptual gap score and standard deviation were calculated for each item. The gap was calculated by subtracting the importance score from the performance score. The correlations between the gap scores and the overall satisfaction scores were then determined. The items as shown in Table 3 are listed in rank order of importance. Only five support items scored a positive gap, indicating satisfaction with that item. The item with the highest positive gap was “degree of personal control” (gap of .407). The other four items that indicated user satisfaction were “new hardware upgrades” (.268), “new software upgrades” (.232), “standardization of hardware” (.132), and “participation in planning system requirements” (.075). The remain-



Table 3. Service Quality Gap Analysis – listed by rank order of importance score

Attributes # Descriptor 10 Data security and privacy 13 Fast response time from IS staff to remedy problems 6 High degree of tech. competence of IS staff 11 System’s response time 5 Low percentage of hardware and software down time 1 Ease of access for users to computing facilities 19 Ability of the system to improve personal productivity 7 User confidence in system 16 Positive attitude of IS staff 12 Extent of user training 9 System responsiveness to changing user's needs 17 Users’ understanding of the system 3 New software upgrades 2 New hardware upgrades 8 Degree of personal control users have over their systems 15 Flexibility of the systems to produce prof.

* **

Rk 1

Importance Mean SD 4.25 .815

Rk 7

Performances Mean SD 3.855 .678

Perceptual Gap SD -.400 1.116

Gap Corr. w/ Satis. .1333

2

4.20

.911

14

3.545

.919

-.611

1.235

.2771**

3

4.10

.867

6

3.857

.554

-.250

1.014

.1727

4

4.07

.858

9

3.764

.769

-.296

1.002

.2167

5

4.05

1.017

2

3.909

.727

-.148

1.035

-.0934

6

4.03

.962

1

3.964

.571

-.073

.959

-.0161

7

4.01

.782

8

3.836

.688

-.185

1.047

-.1489

8

4.01

.924

3

3.893

.412

-.125

1.046

.0588

9

3.89

.809

11

3.745

.645

-.130

1.133

.2720**

10

3.81

.779

18

3.182

.819

-.604

1.115

-.0942

11

3.76

.769

13

3.585

.865

-.189

1.241

-.0610

12

3.65

.844

12

3.618

.652

-.037

.990

.2017

13

3.64

.841

5

3.875

.689

.232

1.027

.2457*

14

3.48

.831

10

3.750

.815

.268

1.152

.2346*

15

3.44

.933

4

3.889

.697

.407

1.125

.2979**

16

3.41

1.013

16

3.364

1.238

-.074

1.226

-.1677

implies correlation is significant at 10% level implies correlation is significant at 5% level



ing 17 items had a negative gap – indicating underperformance of the IS department, or dissatisfaction. The items with the largest negative gap (indicating highest level of dissatisfaction) were “fast response time from IS staff” (-.611), “extent of user training” (-.604), and “help with database or model development” (-.423). The service quality gap for three support items (fast response time from IS staff, positive attitude of IS staff, and degree of personal control) were positively correlated to satisfaction (r = .277, .272, and .298 respectively, significant at 5% level). These support items have the strongest influence on satisfaction in this environment. The results of the Mirani and King EUC satisfaction portion of the survey showed that as a whole, there was a high level of EUC satisfaction at Otis Elevator (7-point Likert scale, Mean 5.32, SD 1.07). With a score of 4 indicating neither satisfied nor dissatisfied, we have interpreted scores below 4 to indicate varying degrees of dissatisfaction and scores above 4 to indicate varying degrees of satisfaction. One individual scored below 3, indicating a high degree of dissatisfaction. Six individuals scored between 3 and 4, indicating a lesser degree of dissatisfaction; 20 individuals scored between 4 and 5, indicating a lesser degree of satisfaction; 22 individuals scored between 5 and 6, indicating a higher degree of satisfaction; and seven individuals scored above 6, indicating the highest degree of satisfaction. One respondent did not answer this portion of the survey. Analysis of Data – Phase Two While the overall score from the user information satisfaction portion of the survey revealed a generally high level of satisfaction, gap analysis of the 22 support items clearly showed specific support ar-

eas where there was user dissatisfaction. The interviews therefore concentrated on those specific areas, and the views of the end-users were gathered to assist in the assessment of their technological frames of reference. All seven respondents that scored below a four and seven randomly selected end-users that scored above a four were interviewed. Analysis of the interviews resulted in the development of the technological frame of reference of the satisfied and dissatisfied user. The principal author conducted the interviews. The interviews were tape recorded, transcribed and systematically examined for patterns in the frames of a satisfied and a dissatisfied user. The initial content analysis occurred through opencoding (Corbin and Strauss, 1990) of the interview transcripts. A research team comprising four research colleagues performed analysis of the interviews. An initial set of patterns (categories) emerged from the analysis of the coded transcripts. The transcripts were then physically formatted to conform to the standards of the qualitative analysis software package, NUD*IST. In NUD*IST, the initial categories that had emerged from the analysis were formulated into a hierarchical tree structure, and the transcripts were closedcoded and documented. The search function of the software was used to interrogate the transcripts in order to verify and substantiate the initial categories that were discovered during the analysis of the opencoding. A number of additional categories began to emerge from this analysis, as further questions were translated into queries, and a second set of categories began to emerge that addressed additional aspects of the technology that had not been initially analysed. Category frameworks were then iteratively developed, applied to the data, and revised. The tree structure continued



to grow and was refined further, as additional nodes were included and nodes that did not indicate any theoretical significance were deleted. In addition, several nodes were combined and merged, as the categories themselves began to crystallise and became clearer. The results revealed three basic categories that could be used to group satisfied and dissatisfied users: type of learner, their view of the role of the PC, and the complexity level of the applications they used. Using these categories, Table 4 outlines the frames of the two groups: satisfied and dissatisfied users. In general, the satisfied user is a selfdirected learner who continually seeks out additional learning opportunities. He views the PC as a tool that is necessary only for the completion of his work and utilizes complex applications in his work. Conversely, the dissatisfied user does not actively seek out additional learning opportunities, and generally will only take IT-related courses if they are required for the job. This user views the PC as a tool that could enhance job performance, and could contribute to their productivity. Generally, they use lesscomplex types of applications.

These different frames were not initially easily explainable. In particular, our original assumptions contradicted the findings that a satisfied user would use more complex applications and would view the PC as a “task enhancement” tool as opposed to a “task completion” tool. However, since these users viewed the PC as a tool to “get the job done” as opposed to “getting the job done better”, they expected less of the PC, and therefore were more easily satisfied. Research on users’ expectations of technology finds that users who have realistic expectations of the benefits of technology tend to be more satisfied (Compeau et al., 1999). In addition, since they used applications with a higher complexity level, their potential to initiate complex technical queries was increased. Their interactions with the MIS staff were at a “higher” technical level than those with less complex applications. From the analysis of the MIS staff interviews as well as user interviews, we had concluded that the MIS personnel had little patience with “routine” MIS queries. On the contrary, when the interactions with the MIS support staff dealt with technical issues, the MIS staff would respond more readily, and with a

Table 4. Satisfied vs. Dissatisfied Users

Type of Learner

Role of PC

Complexity of Application

Satisfied User

self-directed

task completer

more complex

Dissatisfied User

non self-directed

task enhancer

less complex



more positive attitude. The satisfied users had a self-directed learning style that facilitated the acquisition of more complex applications. Further, as a result of using more complex applications, their interactions with the MIS staff were frequent and positive, and this resulted in a more satisfied user. The technological frame of reference of the dissatisfied user is explainable in a similar fashion. Since these users were not self-directed and took only courses that were required for their job, they did not acquire more complex applications. Since their interactions with the MIS staff were at a more “routine” level, these interactions were not positive. Several users who shared this frame of reference reiterated a high level of dissatisfaction with the “superior” attitude of the MIS staff when routine queries were posed to them. In addition, these users expected more from the PC, viewing the PC as a “task enhancing” tool. They expected the PC to greatly contribute to the productivity of their job. Since these users expected more of the PC, they were more easily dissatisfied.

DISCUSSION This research study gathered service quality data on 22 specific support factors, and overall end-user satisfaction with the EUC environment at an organization. In addition, interviews of satisfied and dissatisfied end-users were assessed to develop the technological frame of reference of these two user groups. The relationship among these three items was explored. While the EUC satisfaction portion of the survey alone indicated that the user population as a whole was satisfied with their EUC environment, service-quality gap analysis and follow-up interviews surfaced several areas of dissatisfaction. Only five

out of 22 support items had a positive service-quality gap (indicating satisfaction with that item). Hidden areas of dissatisfaction were detected by performing the SERVQUAL analysis. The three support factors that showed the largest negative service-quality gap between importance and performance (indicating dissatisfaction with that item) were “fast response time from IS staff”, “extent of user training”, and “help with database or model development”. Although it is important to identify specific support areas that have large negative gaps, identifying which specific support factors influence overall satisfaction provides a richer picture. For example, the support factor “fast response time from IS staff” has one of the largest negative service-quality gaps, indicating a high level of dissatisfaction with that particular support item. In addition, the gap for this item highly correlates with overall user satisfaction. Conversely, while “extent of user training” also has a large negative service-quality gap, the gap does not correlate with overall user satisfaction. Including consideration of user satisfaction to service-quality analysis adds an important piece to the overall study of diagnosing the issues confronting MIS support teams. It is noted that specific support factors that were viewed as having the largest gap between importance and performance were not necessarily the same as those with the most influence on satisfaction. This study shows that understanding the relationship between support factors and user satisfaction in subtle, has multiple aspects and requires observation from a number of different viewpoints for complete understanding. Clearly neither listing the support factors by importance nor performance alone shows the full significance of these items. Service levels determined by the difference between im-



portance and performance highlight issues of high importance and low performance from those where both are high or both are low. The practitioner could target those items for attention rather then dilute attention for those of high importance where service is already high. Similarly, the practitioner would be able to postpone attention for those with low levels of service, but are not viewed as highly important. In particular, management at Otis would be better served in targeting additional support to “fast response time from IS staff” rather than “extent of user training” when attempting to increase overall user satisfaction. Grounded theory techniques were instrumental in creating the technological frames of reference for the satisfied and dissatisfied users. While the dissatisfied user appeared to have higher expectations regarding the contribution the technology could make to their job performance, they were not interested in obtaining additional training or acquiring more complex applications for their PC. On the other hand, the satisfied users were very interested in obtaining additional training and more complex applications, while holding lower expectation levels concerning the value the technology could bring their job performance. The two user groups held different views or perspectives towards the technology they used. This influenced their expectations of the technology, affected their interactions with the MIS support team, and changed their utilization of the technology on a day-to-day basis. The technological frame of reference of the users did indeed influence their ultimate satisfaction with their overall end-user computing environment. One of the results of the actual implementation of the end-user satisfaction sur-

vey itself was an increase in IT usage patterns. The participation in the survey process increased the awareness levels of some of the users regarding the capabilities and functionality of the technology, as well as the available support functions of the organization. This caused the users to use the PC more often, and for a larger variety of functions. This effect was positive for Otis, and welcomed by the MIS staff.

SUMMARY AND CONCLUSIONS Relying on user satisfaction surveys alone will not provide a complete picture of the end-user environment in an organization. It is necessary to look beyond the end-user satisfaction surveys to tease out hidden areas of dissatisfaction. Servicequality gap analysis can be used to identify specific support areas that need attention, as well as identify which particular support areas influence overall end-user satisfaction. In addition, practitioners should be aware that the end-user population is not a homogeneous population that can be served with a one-size-fits-all support strategy. This confirms earlier research as noted in Powell and Moore (2002) and Jurison (2000). For researchers, this study extends previous work in two areas: end user satisfaction and technological frame of reference. This study demonstrates the utility of the service-quality measure as a tool adding deeper understanding of user views and needs. The inclusion of a user satisfaction measure shows that service gaps alone only partially account for user views and attitudes. The identification of technological frames of reference and their effect on enduser satisfaction is crucial to a deeper un-



derstanding of satisfaction. This socio-cognitive thread has not been fully explored as it applies to technology, or MIS in general. In addition, the findings of different relationships between support factors and user satisfaction in different studies (see Table 1) suggest that the relationships are contextual in nature and not constant for all situations. Researchers building theory in this area may be better served by examining additional environmental variables that could affect these relationships. Limitations of the Study This research effort occurred at one research site. The empirical data collected reflects the specific organizational context and events at Otis Elevator in Singapore. The technological frames that were elicited during this research were salient to the respondents under study, in an environment where the support function had recently undergone some organizational realignment, and where new technology was being introduced on a continual basis. Two support personnel had recently been assigned as full-time support to the PAO-HQ staff, with the other personnel supporting the regions, replacing an earlier shared strategy where all personnel supported both PAOHQ and the regions. It is possible that different frames of reference could be elicited from respondents that operate in a more stable environment. The effect of gender and culture were not explored in this study. Both of these factors could contribute to the formation of technological frames of reference. While the satisfied and dissatisfied user groups were mixed in terms of gender and culture, the user population as a whole showed a distinct alignment along functional lines. All but one of the management personnel at Otis were male, non-Singaporean. All

the non-managers Singaporeans.

were

female

Directions for Future Research The discovery that technological frames of reference can impact satisfaction is only relevant if those frames of reference can be altered. Tyre and Orlikowski (1994) posit that there are “windows of opportunity” that exist where adaptation of a particular technology can be affected. The relationship among the three components (technological frame of reference, satisfaction with EUC support and overall EUC satisfaction) does not have to remain static. The introduction of a trigger or the exploitation of an existing one can open a window of opportunity, altering the frames of reference and thereby creating a cyclical relationship. Future research that concentrates on identifying specific triggers that could change the alignment of technological frames of reference is needed. Ideally, any subsequent research would first assess the technological frames of reference and their effect on satisfaction, introduce a trigger mechanism to alter the frames, and then reassess the frames and the level of satisfaction at a later date in order to measure any changes. In this way, the effect of technological frames of reference on satisfaction would be more fully explored.



Appendix A: Overall End-User Satisfaction Instrument Disagree………………Agree 1 2 3 4 5 6 7 1.

Your relationship with the Office of Information Technology staff is good.

2.

Your communication with the OIT staff is precise.

3.

The attitude of the OIT staff is positive.

4.

The degree of training provided to you is sufficient.

5.

The speed of the responses to your requests for service is good.

6.

The quality of the responses to your requests for service is good.

7.

The information that is generated from your computing activities is relevant.

8.

The information that is generated from your computing activities is accurate.

9.

The information that is generated from your computing activities is precise.

10

The information that is generated from your computing activities is complete.

11.

The information that is generated from your computing activities is reliable.

12.

You are able to carry out your computing activities with speed.

13.

Your understanding of the applications you use is good.

14.

Your perceived participation in the information systems function is high.



Appendix B: End-User Support Instrument

Tick the box that corresponds to the importance that each of the following 22 system attributes contribute to the performance of your job. Irrelevant 1

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Somewhat Important 2

Important

Very Important

Critical

3

4

5

Ease of access for users to computing facilities. New hardware upgrades. New software upgrades. Access to external databases through the system. A low percentage of hardware and software down time. A high degree of technical competence of systems support staff. User confidence in systems. The degree of personal control users have over their systems. Systems responsiveness to changing users needs. Data security and privacy. System’s response time. Extent of user training. Fast response time from system support staff to remedy problems. Participation in planning of the systems requirements. Flexibility of the system to produce professional reports, e.g. graphics and desktop publishing. Positive attitude of information systems staff to users. User’s understanding of the system. Overall cost-effectiveness of information systems. Ability of the system to improve personal productivity. Documentation to support training. Help with database or model development. Standardization of hardware.

1

2

Importance 3

4

5



REFERENCES Aladwani, A. (2002), “Organizational Actions, Computer Attitudes, and End User Satisfaction in Public Organizations: An Empirical Study”, Journal of End User Computing, 14(1), 42-49. Amoaka-Gyampah, K. and White, K. (1993), “User Involvement and user satisfaction, an exploratory contingency model”, Information & Management, 25(1), 110. Baroudi, J. J., Olson, M. H., and Ives, B. (1986), “An empirical study of the impact of user involvement on system usage and information satisfaction”, Communications of the ACM, 29, 232-238. Bergeron, F. and Berube, C. (1988), “The Management of the End-User Environment: An Empirical Investigation”, Information & Management, 14(3), 107113. Blili, S., Raymond, L., and Rivard, S. (1998), “Impact of task uncertainty, enduser involvement, and competence on the success of end-user computing”, Information and Management, 33(3),137-153. Boland, R. (1978), “The Process and Product of Systems Design”, Management Science, 24(9), 887-898. Boland, R. (1979), “Control, Causality and Information Systems Requirements”, Accounting, Organizations and Society, 4(4), 259-272. Bostrom R., and Heinen, J. (1977), “MIS Problems and Failures: A SocioTechnical Perspective, Part I — The Causes”, MIS Quarterly, 1(3), 17-32. Bowman, B., Grupe, F., Lund, D., and Moore, W. (1993), “An Examination of Sources of Support Preferred by End-User Computing Personnel”, Journal of End User Computing, 5(4), 4-12 Brancheau, J. and Wetherbe, J. (1988), “Higher and Lower-Rated Infor-

mation Centers: Exploring the Differences”, Journal of Information Management, 9(1), 53-70 Brown, T.J., Churchill, G. and Peter, J. (1993), “Research Note: Improving the Measurement of Service Quality”, Journal of Retailing, 69(1), 127-139. Buyukkurt, M. and Vass, E. (1993), “An Investigation of Factors Contributing to Satisfaction with End-User Computing Process”, Canadian Journal of Administrative Sciences, 10(3), 212-229. Chan, Y. E. and Storey, V.C. (1996), “The use of spreadsheets in organizations: Determinants and consequences”, Information and Management, 31(3), 119-134. Choe, J. M. (1998), “The effects of user participation of the design of accounting information systems”, Information and Management, 34(3), 185-198. Compeau, D., Higgins, C., and Huff, S. (1999), “Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study”, MIS Quarterly, 23(2), 145-158. Corbin, J. and Strauss, A., (1990), Basics of Qualitative Research, Newbury Park, CA, Sage Publishing. Davis, R. D. (1989), “Perceived usefulness, perceived ease of use and user acceptance of information technology”, MIS Quarterly, 13(3), 319-339. Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989), “User acceptance of computer technology: A comparison of two theoretical models”, Management Science, 35(8), 982-1003. Dagwell, R. and Weber, R. (1983), “Systems Designers’ user models: A comparative study and methodological critique”. Communications of the ACM, 26(11), 987-997. DeLone W., and McLean, E. (1992). “Information System Success: the quest for the dependent variable.” Information Sys-



tems Research, 3(1), 60-95. Doll, W. and Torkzadeh, G. (1988), “The Measurement of End-User Computing Satisfaction”, MIS Quarterly, 12(2), 259-274. Fisk, R., Brown, S., and Bitner, M. (1993), “Tracking the Evolution of the Services Marketing Literature”, Journal of Retailing, 69(1), 61-103. Foong, S. (1999), “Effect of end-user personal and systems attributes on computer-based information system success in Malaysian SMEs”, Journal of Small Business Management, 37(3), 81-87. Glaser, B. and Strauss, A. (1967), The Discovery of Grounded Theory, Chicago, Il, Aldine Publishing Company. Gioia, D. A. (1986), “Symbols, scripts, and sensemaking: Creating meaning in the organizational experience”, In The Thinking Organization, Jossey-Bass, San Francisco, CA. Guimaraes, T. and Igbaria, M. (1994), “Exploring the relationship between IC success and company performance”, Information and Management, 26(3), 133-142. Guimaraes, T., Igbaria, M., and Lu, M. (1992), “The determinants of DSS success: an integrated model”, Decision Sciences, 23(2), 409-429. Guimaraes, T., Yoon, Y. and Clevenson, A. (1996), “Factors important to expert systems success: A field test”, Information & Management, 30(3), 119131. Hartwick, J. and Barki, H. (1994), “Explaining the role of user participation in information system use”, Management Science, 40(4), 440-465. Henry, J. and Stone, R. (1994), “A Structural Equation Model of End-User Satisfaction with a Computer Based Medical Information System”, Information Resources Management Journal, 7(3), 2134.

Hirschheim, R. and Klein, H. (1989), “Four Paradigms of Information System Development”, Communications of the ACM. 32(10), 1199. Igbaria, M., Guimaraes, T., and Davis, G. B. (1995), “Testing the determinants of microcomputer usage via a structural equation model”, Journal of Management Information Systems, 11(4), 87-114. Igbaria, M., Iivari, J., and Maragahh, H. (1995), “Why do individuals use computer technology? A Finnish case study”, Information and Management, 29(5), pp. 227-238. Igbaria, M., Pavri, F. N., and Huff, S. (1989), “Microcomputer applications: an empirical look at usage”, Information and Management, 16(4), 187-196. Ives, B., Olson, H. and Baroudi, J. (1983), “The Measurement of User Information Satisfaction”, Communications of the ACM, 26(10), 785-793. Jawahar, I., and Elango, B. (2001), “The effect of attitudes, goal setting, and self-efficacy on End User performance”, Journal of End User Computing, 13(2), 40-45. Jurison, J. (2000), “Perceived Value and Technology Adoption Across Four End User Groups,” Journal of End User Computing, 12(4), 21-28. Kettinger, W. and Lee, C. (1997), “Pragmatic perspectives on the measurement of information systems service quality”, MIS Quarterly, 21(2), 223-240. Kim, K.K. (1990) “User Information Satisfaction: Towards Conceptual Clarity”, Proceedings of the International Conference on Information Systems. Kling R. and Iacono, S. (1984), “Computing as an Occasion for Social Control”, Journal of Social Issues, 40(3), pp. 7796. Kohlmeyer, J. and Blanton, J. (2000), “Improving IS Quality”, Journal of Infor-



mation Technology Theory and Application, 2(1). Kumar K., and Bjorn-Anderson, N. (1990), “A Cross-cultural Study of Designer Values Relevant to Information Systems Development”, Communications of the ACM, 33(5), 528-538. Lawrence, M. and Low, G. (1993), “Exploring individual user satisfaction within user-led development “, MIS Quarterly, 17(2), 195-207. Lederer, A. and Spencer, V. (1988), “The Effective Information Center: Targeting the Individual User for Success”, Journal of Systems Management, 39(1), 2227. Lee, S. M., Kim, Y. R., and Lee, J. (1995), “ An empirical study of the relationships among end-user information systems acceptance, training, and effectiveness”, Journal of Management Information Systems, 12(2), 189-202. Lehman, J. A. and Murthy, V. S. (1989), “Business graphics trends, two years later”, Information and Management, 16(2), 57-69. Lewis, R. and Booms, B. (1983), “The Marketing Aspects of Service Quality”, In Emerging Perspectives on Services Marketing, L. Berry, G. Shostack, and G. Upah (eds.), Chicago, American Marketing, 99107. Mahmood, M., Burn, J., Leopoldo, A., Jacquez, C. (2000), “Variables affecting information technology end-user satisfaction: a meta analysis of the empirical literature”, International Journal of HumanComputer Studies, 52, 751-771. Markus, M. (1984), “Power, Politics, and MIS Implementation”, Communications of the ACM, 26(6), 430-444. Markus, H. and Zajonc, R. (1985), “The Cognitive Perspective in Social Psychology”, in The Handbook of Social Psychology (Vol 1), G. Lindzey and E.

Aronson (eds.), New York, Random House, 137-230. Martin, P. and Turner, B. (1986), “Grounded Theory and Organizational Research”, The Journal of Applied Behavioral Science, 22(2), 141-157. McKeen, J. D. and Guimaraes, T. (1997), “Successful strategies for user participation in systems development”, Journal of Management Information Systems, 14(2), 133-150. McKeen, J. D., Guimaraes, T., and Wetherbe, J. C. (1994), “The relationship between user participation and user satisfaction: an investigation of four contingency factors”, MIS Quarterly, 18(4), 427-448. Miles, M. and Huberman, A.M. (1994), Qualitative Data Analysis, Thousand Oaks, CA, Sage Publications. Mirani, R. and King, W. (1994), “The Development of a Measure for End-User Computing Support”, Decision Sciences, 25(4), 481-499. Montazemi, A. (1988), “Factors Affecting Information Satisfaction in the Context of the Small Business Environment”, MIS Quarterly, 12(2), 239-256. Montazemi, A. R., Cameron, D. A. and Gupta, K. M. (1996), “An empirical study of factors affecting software package selection”, Journal of Management Information Systems, 13(1), 89-105. Noble, D. (1986), Forces of Production: A Social History of Industrial Automation, Oxford University Press, New York. Orlikowski, W. (1993), “CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development”, MIS Quarterly, 17(3), 309-341. Orlikowski, W. and Gash, D. (1994), “Technological Frames: Making Sense of Information Technology in Organizations”, ACM Transactions on Information Sys-



tems, 12(2), 174-207. Palvia, P. C. (1996), “A model and instrument for measuring small business user satisfaction with information technology”, Information and Management, 31(3), 151-163. Parasuraman A., Zeithaml, V.A. and Berry, L. (1985), “A Conceptual Model of Service Quality and its Implications for Future Research”, Journal of Marketing, 49(4), 41-50. Parasuraman A., Zeithaml, V.A. and Berry, L. (1993), “Research Note: More on Improving Quality Measurement”, Journal of Retailing, 69(1), pp. 140-147. Park, S. W., Jih, K., and Roy, A. (1993-1994), “ Success of management information systems an empirical investigation”, Journal of Computer Information Systems, 34(2), 33-37. Pinch, T. and Bijker, W. (1987), “The Social Construction of Facts and Artifacts”, in The Social Construction of Technological Systems, W. Bijker, T. Hughes, and T. Pinch (eds), Cambridge, MA, MIT Press, 17-50. Pitt, L., Watson, R., and Kavan, C. (1995), “Service Quality: A measure of information systems effectiveness”, MIS Quarterly, 19(2), 173 – 187. Pitt, L, Watson, R., and Kavan, C. (1997), “Measuring Information Service Quality: Concerns for a complete canvas”, MIS Quarterly, 21(2), pp. 209-222. Powell, A., and Moore, J. (2002), “The Focus of Research in End User Computing: Where Have We Come Since the 1980’s?” Journal of End User Computing, 14(1), 3-22. Rainer, R. and Carr, H. (1992), “Are information centers responsive to end user needs?” Information and Management, 22(2), 113-121. Remenyi, D. and Money, A. (1994), “Service quality and correspondence analy-

sis in determining problems with the effective use of computer services”, European Journal of Information Systems, 3(1), 212. Ryker, R. and Nath, R. (1995). “An empirical examination of the impact of computer information systems on users”, Information & Management, 29(4), 207215. Saarinen, T. (1996), “An expanded instrument for evaluating information system success”, Information & Management, 31(2), 103- 119. Saleem, N. (1996), “ An empirical test of the contingency approach to user participation in information systems development”, Journal of Management Information Systems, 13(1), 145-166. Satzinger, J. W. and Olfman, L. (1995), “Computer support for group work: perceptions of the usefulness of support scenarios and end-user tools”, Journal of Management Information Systems, 11(4), 115-148. Schutz, A. (1970), On Phenomenology and Social Relations. University of Chicago Press, Chicago, Ill. Shaw, N., Niederman, F., and DeLone, W. (2002), “And empirical study of success factors in end-user support”, DATABASES Advances in Information Systems, 33(2). Seddon, P. (1997), “A respecification and extension of the DeLone and McLean model of IS success”, Information Systems Research, 8(3), 240-253. Strauss, A. (1987), Qualitative Analysis for Social Scientists, Cambridge, Cambridge University Press. Szajna, B. and Scamell, R. (1993), “The effects of information system user experiences on their performance and perceptions”, MIS Quarterly, 17(4), 493-516. Thompson, R. L., Higgins, C. A., and Howell, J. M. (1994), “Influence of expe-



rience on personal computer utilization: testing a conceptual model”, Journal of Management Information Systems, 11(1), 167187. Torkzadeh, G., and Doll, W. (1993), “The place and value of documentation in end-user computing”, Information & Management, 24(3), 147-158. Trauth, E. and Cole, E. (1992), “The Organizational Interface: A method for supporting end users of packaged software”, MIS Quarterly, 16(1), pp. 35-54. Tyre, M. and Orlikowski, W. (1994), “Windows of Opportunity: Temporal Patterns of Technological Adaptations in Organizations”, Organization Science, 5(1), 98-118. Van Dyke, T.P., Kappelman, L.A.,

and Prybutok, V.R. (1997), “Measuring Information Systems Service Quality: Concerns on the Use of the SERVQUAL Questionnaire”, MIS Quarterly, 21(2), 195-208. Venkatesh, V. (1999), “Creation of favorable user perceptions: exploring the role of intrinsic motivation”, MIS Quarterly, 23(2), 239-260. Yoon, Y. and Guimaraes, T. (1995), “Assessing expert systems impact on users’ jobs”, Journal of Management Information Systems, 12(1), 225-249. Zmud, R.W. (1979), “Individual differences and MIS success; a review of the empirical literature”, Management Science, 25(10), 966-979.

Nancy C. Shaw is an Assistant Professor of Information Systems at George Mason University in Fairfax, Virginia. She received her Ph.D. in Information Systems from the National University of Singapore, and an MBA and a BBA from the University of Kentucky. Dr. Shaw has been a practitioner and consultant in the information systems industry for over 20 years. She has worked for AT&T, General Electric and most recently as a senior systems analyst for the Central Intelligence Agency. Dr. Shaw served as a Military Intelligence Officer in the U.S. Army Reserves during the Persian Gulf War. Her current research interests include end-user computing support and knowledge management. Dr. Shaw has published in the International Journal of Information Management and DATABASES. Joo-Eng Lee-Partridge is an MIS Professor in the School of Business at Central Connecticut State University. She received her B.Sc. (First Class Honors) degree from the National University of Singapore and her Ph.D. degree in Management Information Systems from the University of Minnesota, Twin Cities. Her research interests cover topics such as facilitation, group support systems, computer-based learning, end-user computing, negotiation and knowledge management. Her publications have appeared in MIS Quarterly, Group Decision and Negotiation, European Journal of Information Systems, Journal of Strategic Information Systems, Omega and International Journal of Information Management. James S.K. Ang is Associate Professor with the Department of Decision Sciences, School of Business, National University of Singapore. He holds the B.Sc (Mathematics and Philosophy) from the University of Singapore, and the M.A.Sc. and Ph.D. (Management Sciences) degrees from the University of Waterloo, Canada. His research interests include systems modeling using Petri nets and object-oriented formalism, information systems planning, and e-business topics. Dr. Ang has published articles in journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man and Cybernetics, Data Base, Journal of Operations Management, Decision Sciences, Data and Knowledge Engineering, Information and Management, International Journal of Production Economics, Decision Support Systems, and INFOR. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

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