GIS expertise generally assumes knowledge of one or more GIS software products, relevant computer hardware, operating systems, and programming skills. In many cases, background in a substantive discipline is desirable (i.e., urban planning, surveying, social policy, environmental science, engineering). Organizations pursue two basic strategies for acquiring the needed GIS expertise: 1) hire already trained GIS specialists; and 2) build their internal capacity by upgrading the skills of existing employees, usually through software vendors’ or conference workshops. Many GIS users, however, use a third strategy and end up being self-taught (Hearnshaw 1993). All strategies have their inherent flaws and provide only limited security to the agencies in need of GIS support staff. Self-teaching usually has a very steep learning curve. Investing in the new employee or in education of existing employees is risky too, given the high rates of employee turnover and transfer.

The other side of the resistance issue is the question of motivation to adopt a new technology What is ususally described as resistance to change, may very well be a lack of motivation to accept a particular technology, in this case GIS. Eason (1993), for instance, mentions the mismatch to organizational purposes and functions, as a possible reason for failure to accept a particular technology. In addition, the staff members may not have a clearly defined need for employing the new system. Those and other reasons for not using the technology, whether classified as subjective or objective, are important to consider. Accepting them as rational and justified behavior is a step forward toward a better understanding of the process of diffusion of innovations (Feller and Menzel 1977; Rogers 1983; Leonard-Barton and Kraus 1985).

The literature on human factors in GIS concentrates primarily on applying the knowledge about the human mental and cognitive processes toward better design of hardware, software, user interfaces, visualization, and decision-support tools (Medyckyj-Scott and Hearnshaw 1993). The human factors that are related to the individual adoption decisions have received less attention in both research and practice. Even when the social aspects are more specifically considered, the focus is often on the people as participants in the implementation process (Eason 1993), political issues (Buchanan 1993), and organizational culture (Benwell 1993), rather than on the personal psychological and experiential dispositions to accepting GIS technology and becoming a GIS user. Recent attempts to measure the implementation success of various information systems have confirmed the significance of end-users and their characteristics, motivation, experiences, and perceptions of the technology (Ives et al. 1983; Danziger and Kraemer 1986; Raymond 1987; Baroudi and Orlikowski 1988; Carey 1988; Igbaria and Nachman 1990; Budic and Godschalk 1994).

This paper addresses the personal factors that affect the individual decisions regarding adoption and future use of GIS technology It examines the significance of perceived organizational and personal benefits of GIS, values and beliefs about computerized technology, previous computer experience, perceived complexity of GIS, exposure to GIS, computer anxiety, resistance to work-related change, and communication behavior (i.e., networking). A number of management and organizational factors are also included as relevant contextual and background information. Following the review of previous research on the personal factors that influence the individual adoption of innovations, this paper describes the data collection method, and reports on the analysis and findings of a mail survey to which over 300 North Carolina local government members responded.

The respondents were staff members, both GIS users and non-users, and administrators in agencies using, and not using, a GIS. Staff members were surveyed to inquire into individual GIS adoption, while the responses from agency administrators provided insight into the likelihood of support for organizational adoption of GIS.

Data were analyzed using logistic regression modeling. The statistical coefficients were translated into odds of becoming a GIS user in the case of staff members, and into odds of becoming a GIS supporter in the case of agency administrators. The odds reflect the increase or decrease in the probability of becoming a GIS user or supporter, given the presence of particular personal, organizational, or management factors. The results indicate the personal characteristics and areas that require special attention if a widespread diffusion of GIS within an organization is desirable.

Introducing GIS technology into an organization is a complex process of organizational and individual adoption behaviors. The adoption is influenced by a variety of factors including organizational environment, internal organizational context, management activities, and personal factors (Budic 1993b; Figure 1). Basically rooted in a systems interactionism model of computing change (Kraemer et al. 1989), this framework postulates that the stimulus for considering incorporation of GIS technology comes from both external and internal organizational environments, and is shaped by deliberate management action. The outcomes of GIS diffusion and use at the individual and organizational level provide a feedback to the organization and its environment.

FIGURE 1. Diffusion of GIS Organizations (After Budic 1993b)

The success of implementing an innovation is often labeled as organizational achievement, and the decisions regarding the adoption of innovations are usually thought of as either collective or authority-based rather than individual (Rogers 1983). Organizational adoption, however, is ultimately the result of a series of adoption decisions by individual employees in professional support, management, or administrative roles (Budic 1993b; Budic and Godschalk 1994). Moore (1993) agrees that "diffusion of innovations occurs through the collective, yet individually based decisions of individual level adopters." (p. 80) Important "filtering" that opens up the possibility for GIS acquisition and implementation often happens at the administrative, decision-making, or higher management organizational levels, and is manifested as the organizational attitude toward GIS. However, the staff members who decide to become either direct or indirect (i.e., through other employee services) users of GIS are those who make the GIS happen (Budic and Godschalk 1994). Their decisions and activities cumulatively contribute to organizational adoption and utilization of GIS.

The factors that most strongly influence the decisions of individual staff members are the personal attitudes, experiences, characteristics, and behavior. Those personal factors are the main subject of the research presented here. Several attributes of organizational context and environment, and management activities are also considered, as they affect the behavior of staff members, and consequently, their adoption decisions.

Rogers (1983) classifies individual adopters into five categories regarding their innovativeness (i.e., likelihood to experiment and adopt an innovation): 1) innovators; 2) early adopters; 3) early majority; 4) late majority; and 5) laggards. The temporal distribution of adopters of a given innovation follows a bell-shaped curve and approaches normality.

The five adopter categories are characterized by three sets of variables:

· Socioeconomic status: age, education, literacy, social status, upward social mobility, size of business operation commercial orientation of business, attitude toward credit, and level of specialization of operations;

· Personality variables: empathy, dogmatism, ability to deal with abstractions, rationality, intelligence, attitude toward change, ability to cope with uncertainty, attitude toward education, fatalism, achievement motivation, aspirations; and

· Communication behavior: social participation, interconnectedness in the social system, level of urban sophistication, contacts with change agents, exposure to mass media, exposure to interpersonal communication, activity level in seeking information, knowledge of innovation, opinion leadership, belonging to highly interconnected systems.

Based on Rogers’ (1983) meta-analysis of those factors in a variety of innovation-diffusion settings, early adopters of innovations are, on average, younger individuals with higher socio-economic status, higher level of intelligence and rationality, more open to change, and more knowledgeable about innovation.

The perceived characteristics of innovations also influence the rate of individual adoption (Rogers 1983; Zaltman et al. 1973). These characteristics include:

· perceived relative advantage;

· compatibility with personal values, past experiences, and needs;

· perceived complexity of an innovation;

· trialability (i.e., opportunity for experimenting with an innovation); and

· observability of an innovation.

Feller and Menzel (1977) list an additional set of personal variables relevant for adoption of technological innovations: motivation, professionalism, informedness, resources, pride, and risk propensity.

Among the personal factors, age, gender, and education appear less significant when compared to other personal attributes (Danziger and Kraemer 1986; Igbaria and Nachman 1990). Studies of innovative behavior in general also reveal the secondary importance of individuals’ demographic characteristics. Roles and positions, for instance, are found to exert stronger influences on the involvement of individuals in the innovation process than the demographic factors (Baldridge and Burnham 1975; Srinivasan and Davis 1987; Chakrabarti and Hauschildt 1989). Employees in the role of intermediaries (also called integrators or champions), comprising both non-technical and technical knowledge, usually serve as facilitators in the implementation process (Srinivasan and Davis 1987; Chakrabarti and Hauschildt 1989; Azad 1997).

Relationships and interactions among people within an organization represent yet another relevant factor affecting the implementation of computerized technology Relationships among staff members holding different positions within organizations (especially between users and experts) are as important as the relationships between personalities in general (Chakrabarti and Hauschildt 1989). The significance of relationships is particularly apparent when varying interests and motivations of individuals become primary drivers of their behavior and, consequently, of the decisions regarding an innovation. This behavior is likely to lead to conflicts (Nolan 1973). The introduction of a new information system is usually seen as a threat to the existing balance of power (Campbell 1990). The implementation process itself may turn highly political (Markus 1983; Pinto and Azad 1994).

Finally, the personal reasons for ignoring, resisting and even sabotaging new technology have also been considered in previous research. Resistance to change is a widely recognized phenomenon (Mohr 1969; Zaltman et al. 1973; Leonard-Barton and Kraus 1985; Carey 1988; Rogers 1983; Robey 1987). Organizational members under-utilize, or even sabotage, the new technology if they feel they may be adversely affected by the change (Leonard-Barton 1987). At the individual level, the most important attitudinal factors are fear of change (Brod 1985; Mohr 1969; Peterson and Peterson 1988), and computer-related anxiety (Peterson and Peterson 1988; Mitchell 1994). Fear of change is expressed through one’s concern about safety, security, or self-esteem. It is manifested primarily through worrying about loss of skill or possible replacement by more efficient equipment. The second attitudinal factor, anxiety, is a natural feeling of uneasiness when exploring or facing unfamiliar situations. This feeling, in some persons, is intensified when confronted with new technology

Attributing the rejection of innovations only to anxiety and fear of change, however, is an oversimplified view of the process of technology transfer (Raghavan and Chand 1989). Carey (1988) finds a correlation between acceptance of change and variables such as previous use (experience), education, and current usage of a new system. She also reports commitment, exposure to change, and preparation for change are important for successful implementation of new technologies and systems.

Research on implementation of GIS technology in local government encounters mixed findings with regard to staff’s resistance to change. Campbell and Masser (1991), in their inquiry into the resistance to change, detected no problem of general staff resistance, but indicate that effective implementation of GIS technology requires changes in personnel attitudes. In their multiple case study of four local government agencies in a North Carolina county, Nedovic Budic and Godschalk (1996) confirm the mixed evidence on the employee behavioral rigidity. They tested seven additional theoretical propositions and found:

· perceived relative advantage of GIS technology and previous experience were the two most significant personal factors;

· exposure to GIS technology and more intensive communication behavior also contributed to individual adoption of GIS;

· evidence on personal values and beliefs was mixed; and

· the hypotheses on computer-related anxiety and perceived complexity of GIS were falsified.

A variety of organizational contextual factors and management activities were also found to affect the diffusion of GIS among organizational members (Campbell 1990; Budic 1993b; Budic and Godschalk 1994; Campbell 1994)

Drawing on the previous research, and extending on the Nedovic-Budic and Godschalk (1996) and Budic (1993b) case studies, a survey was designed to further test the eight propositions on the personal factors forwarded in their research. These propositions hypothesize that the individual staff members are more likely to adopt GIS technology if they:

1. Perceive GIS technology as relatively advantageous when compared to current technologies and practices used (Downs and Mohr 1979; Zaitman et al. 1973; Rogers 1983; Leonard-Barton 1987; Rivard 1987);
2. Experience GIS as compatible to their personal values and beliefs about computerized technology (Zaltrnan et al. 1973; Rogers 1983; Danziger and Kraemer 1986; Rivard 1987; Igbaria and Nachman 1990);
3. Have substantial experience with computerized technology (Zaltman et al. 1973; Ives et al. 1983; Rogers 1983; Danziger and Kraemer 1986; Leonard-Barton 1987; Carey 1988; French and Wiggins 1989; Igbaria and Nachman 1990);
4. Do not perceive GIS as a complex technology (Zaltman et al. 1973; Ives et al. 1983; Rogers 1983; Danziger and Kraemer 1986; Leonard-Barton 1987; Raymond 1987; Rivard 1987; Baroudi and Orlikowski 1988; Croswell 1991);
5. Have been exposed to or had on opportunity to try out a GIS (Ives et al. 1983; Rogers 1983; Raymond 1987; Baroudi and Orlikowski 1988; Carey 1988);
6. Do not have computer-related anxiety (Raub 1981; Danziger and Kraemer 1986; Peterson and Peterson 1988; Igbaria and Nachman 1990);
7. Have a positive attitude toward work-related change (Mohr 1969; Zaltman et al. 1973; Rogers 1983; Brod 1985; Leonard- Barton and Kraus 1985; Leonard-Barton 1987; Rivard 1987; Robey 1987; Carey 1988; Peterson and Peterson 1988; French and Wiggins 1989; Raghavan and Chand 1989; Campbell and Masser 1991; Croswell 1991); and
8. Have active communication (i.e., networking) behavior (Zaltman et al. 1973; Ives et al. 1983; Rogers 1983; Danziger and Kraemer 1986; Leonard-Barton 1987; Raymond 1987; Baroudi and Orlikowski 1988; Kearns 1989; Campbell 1990; Croswell 1991).

In addition to the personal-level variables, the survey elicited information on organizational and management variables that were found relevant in the previous research:

· Three organizational context variables: agency size (French and Wiggins 1990); staff turnover (Campbell 1990; Budic 1993b), and organizational conflict (Croswell 1991; Budic 1993b; Pinto and Azad 1994);

· Four GIS management variables: availability of GIS hardware and software (Budic 1993b; Masser and Craglia 1997), provision of GIS training (Croswell 1991; Hearnhaw 1993; Brown 1996), involvement in GIS implementation (Leonard-Barton 1987; Eason 1993; Campbell 1994), and supply of incentives for prospective GIS users (LeonardBarton 1987; Budic 1993b; Budic and Godschalk 1994).

Operational definitions of the involved concepts were developed as a series of dimensions derived from previous research and theory (Table 1). The constructs that are central to the eight propositions were measured as 14 independent variables whose values were determined either as a choice from a multiple-item menu, or on a Likert-type 1-5 scale. Six of the 14 variables were derived from multiple questions, and converted to indexes, whose validity was checked with Cronbach’s Alpha test. The Cronbach’s Alpha was 0.70 or higher for all indexes. The survey instrument is attached in Appendix A.

Three sources of data were used to elicit the needed information: mail survey, telephone interviews, and census data. Telephone interviews were conducted at the outset of the research with 56 contact persons, department heads, and section chiefs. The purpose of establishing those contacts was to identify staff members who were GIS users and non-users and to secure support for the mail survey. The key informants also helped supply information about internal organizational context and the GIS management variables—however, only in agencies using GIS. Census data were used to obtain values for environmental variables—population size in 1990, and population change from 1980 to 1990.

Information on the personal factors was elicited by mail. The mail survey of individual employees—staff members and administrators—was administered in the fall of 1992. The sampling framework was based on the population of local governments in North Carolina identified in a 1990/91 survey as using or planning to acquire GIS technology (Budic 1993a). The survey involved 15 local governments that had operational geographic information systems. Presence of multidepartmental GIS setups, joint city/county systems, and agencies that were indirect GIS users, yielded 56 organizational units surveyed (Table 2). An additional 84 agencies that did not employ a GIS, but were located within the same local governments, were also included. Among a variety of units surveyed, the majority were represented by planning, tax assessor, land records, public works, engineering, police, fire, and environmental health agencies.

Four versions of the survey questionnaire were designed to address four different target groups:

The survey was pre-tested with a group of ten employees in a county government that was excluded from the subsequent data collection. Out of 627 questionnaires sent, 349 forms returned with a 55.7% response rate (Table 3). Two hundred seventy-eight responses were used in the final analysis. Some questionnaires were excluded because they returned blank, mostly from individuals who did not work with spatially distributed data or maps. Those respondents were assumed not to need GIS technology. The question about working with spatial data or maps was used to screen the respondents for whom the questionnaire content was not applicable.

Survey respondents were grouped into three categories: direct GIS users, indirect GIS users, and GIS non-users (Table 4). Those three categories were the approximations for the level of GIS adoption, and served as the dependent variable in the modeling procedure. Respondents who filled out the forms intended for GIS users were either direct or indirect GIS users. Administrators in agencies using GIS technology were represented in all three categories. They were more frequently found in the indirect GIS-user group, although there were a few administrators who were direct GIS users, and a few non-users. The individuals who were surveyed as GIS non-users and administrators in agencies not using GIS technology obviously fell under the non-user category.

Data analysis comprised of two modeling attempts— one with information derived from the staff members, and the other with information derived from the agency administrators. Model I has the three GIS-use categories for its dependent variable: direct GIS users, indirect GIS users, and GIS non-users. It also includes all personal, organizational, and GIS management variables, a total of 24 variables (15 continuous and nine categorical variables).

The categorical nature of the dependent variable in both models required the use of logistic regression for deriving the coefficient estimates and their significance levels. Logistic regression uses the maximum-likelihood method to derive parameter estimates. Also, by assuming logistic distribution of sample disturbances, logistic regression takes care of the problem of unequal variance (i.e., heteroscedasticity) that is inherent in the situations when the dependent variable is categorical. The three-level dependent variable in Model I required the multinomial logistic procedure, which considers and generates parameters for each category separately. The decision to rely on multinomial logistic regression was based on a statistic derived by running the model with the regular logistic procedure. A Chi-Square test pointed to a problem of unequal distance between the chosen categories, which implied that less accurate coefficient estimates would result from the regular logistic procedure. The score test for the proportional odds assumption was highly significant at p 0.0001, with a value of 90.3687, and with 24 degrees of freedom. Model 2 was run with a regular logistic regression, since it had a categorical dependent variable with only two levels.

A Pearson correlation coefficient was derived for all independent variables to test for linear correlation between them (i.e., multicollinearity). The highest coefficient obtained was a correlation between the index measuring the attitude toward work-related change and an index measuring the apprehensiveness toward GIS technology. The value of the coefficient of 0.48442 was still very low to indicate a collinearity problem. Other bivariate relationships were all weaker than this one.

Both Model I and Model 2 had a high coefficient of determination—R-Squared was over 0.7. The independent variables included in the two models explained a great portion of variance in two dependent categorical variables. The value of the coefficients of determination indicate high overall goodness of fit of the two models to the data (Table 5).

Because the coefficients reported in the maximum-likelihood estimation do not, taken individually, show the substantive significance of a given variable and the probability of falling into a specific category of GIS use, the than one, have a positive effect on the probability of being a direct or an indirect GIS user versus being a GIS non-user. The variables with log odds less than zero, i.e., odds less than one, decrease the probability of being either a direct or an indirect GIS user versus being a nonuser.

The effects of the explanatory variables on the log odds are additive, while the effects of these variables on the odds are multiplicative. For example, with other variables held constant, a unit increase in perceived organizational benefits (variable ORGBEN) will result in an expected 0.3 1877224 increase in log odds of being a direct GIS user versus being a GIS non-user; the same increase in value of the ORGBEN variable will result in the odds of being a direct GIS user versus being a GIS non-user multiplied by a factor of 1.375. Similarly, for dummy variables, engagement in computer programming (variable PROG) will lead to an expected 2.0143176 increase in the log odds of being a direct GIS user versus being a non-user; the same distinction will result in the odds of being a direct GIS user versus a non-user being multiplied by a factor of 7.496. The interpretation for other continuous variables is analogous to that for ORGBEN, while the values for the remaining categorical variables in the table may be interpreted similarly to the variable PROG.

Similar to the Nedovic Budic and Godschalk (1996) case study findings, perceived personal benefits (variables 2 and 3 in Tables I and 6), and compatibility with computer experience (variable 4 in Tables 1 and 6) clearly emerged as the most significant determinants of individual adoption of GIS technology. Previous use of computers had a large effect on becoming either a direct or an indirect GIS user, confirming the importance of the experience for the acceptance of new technology (Carey 1988). Presence of programming skills increased the odds of being a direct or an indirect GIS user versus being a non-user by approximately seven-fold.

Perceived tangible personal benefits (increase in salary or advancement in position) were important for both GIS direct and indirect users, consistent to results are interpreted in terms of odds and log odds. Table 6 shows the impact of each explanatory variable on the odds and log odds of being either a direct or an indirect user of GIS technology versus being a non-user. The variables with positive log odds, i.e., odds larger

TABLE 6. Coefficient Estimates Interpreted as Log Odds and Odds and Their Statistical Significance for Direct GIS Users, Indirect GIS Users and GIS Non-users

 Leonard-Barton’s (1987) conclusion about the need for an explicit reward structure at the individual user level. As might be expected, the overall personal benefits were less important for indirect users. Those employees considered organizational benefits as more relevant in deciding whether to use GIS technology.

Contact with GIS users was the most significant element in the individual communication that affected the adoption of GIS technology, as suggested by traditional theory of diffusion of innovations (Rogers 1983). It significantly influenced individual employees’ decisions about using GIS technology, thus agreeing with the position that interactions with others do affect our attitudes and behavior (Kearns 1989). Direct communication with GIS users increased the odds of becoming a direct or an indirect GIS user threefold and tenfold, respectively. These contacts were more important for receiving GIS-related information than the other aspects of personal networking. For instance, the overall intensity of communications was related to the dependent variable in an opposite direction from expected. An extensive communication pattern did not imply connection with GIS users. A small network, but one that included crucial individuals to transfer the GIS information or knowledge, was sufficient to affect the decisions about engagement with GIS technology.

Exposure to GIS technology also exerted considerable influence on whether or not a staff member would become a GIS user, but only in case of direct GIS users. Anopportunity to try out GIS technology increased the odds of being a direct GIS user versus being a non-user by a factor of 3.5—consistent with the theoretical expectation about the trialability of innovations (Zaltman et al. 1973; Rogers 1983). In the case of indirect users, the relationship was negative. Opinion about GIS technology resulting from exposure to the technology also had a negative association with the dependent variable. High opinion decreased the odds of becoming a direct or an indirect GIS user. Similarly, a reversed relationship was found in the Nedovic-Budic and Godschalk (1996) case study, where GIS users were more skeptical about quality of GIS products than non-users.

Responses on GIS-related apprehension and anxiety were also opposite from expected. While the finding on GIS-related apprehension was statistically insignificant, GIS-related anxiety was associated to the dependent variable contrary to the hypothesized negative influence on the decisions to become a GIS user. The data suggest that GIS users expressed more anxiety about the new technology than GIS non-users, as in Nedovic-Budic and Godschalk’s (1996) case studies.

Finally, the lack of behavioral rigidity related to change situations in the work place significantly increased the odds of becoming a GIS user. In case of direct GIS users, both a general positive attitude toward work-related change and a higher preferred frequency of change raised the odds of becoming a user, by about one and three times respectively. In the case of indirect users, only the general attitude toward work-related change was statistically significant. The highly significant influence of the attitude toward work-related change on becoming a direct GIS user is consistent with the widely recognized problem of resistance to change (Eason 1993), but contrary to recent research on implementation of GIS technology conducted in British local authorities by Campbell and Masser (1991). In their survey of 514 governments, the authors encountered no problem of general staff resistance. Carey (1988) found other variables (such as experience, education, and current usage of a new system) more significantly related to the acceptance of change than behavioral rigidity. Conclusions related to this factor in Nedovic-Budic and Godschalk’s (1996) case studies were mixed.

Employees at higher administrative positions, like department heads and section chiefs, were modeled separately because their role with regard to GIS technology is different. Rather than expecting them to directly employ the technology, their role is primarily in initiating and supporting its acquisition and use. Most administrators in the agencies that implemented GIS technology were either direct or indirect users of the technology (43 of 46, with only three non-users). Model 2 disregards this difference between the administrators with respect to the GIS use category. It divides the administrators between those from agencies using GIS and those from agencies not using GIS. The model examines the influence of the eight personal and three organizational factors on administrative decisions to introduce GIS technology into an agency’s operations. Results are shown in Table 7.

Administrators who have more experience with computers were more likely to support the introduction of GIS technology. Direct operation of computerized technology by an administrator resulted in an eight-fold increase in the odds of being a head of an agency which uses a GIS versus being a head of an agency which does not rely on GIS technology. Exposure to GIS technology, although not having a high significance level, exerted a large effect on the odds of being a supporter versus not being a supporter of GIS technology Presence at a formal demonstration of GIS technology increased the odds of being an administrator who is supportive of the technology by five times. While these findings did not take into account the possibility of variable need for GIS, the screening questions at the beginning of the survey instrument were to ascertain that only the agencies that operated with spatial data and maps, and that could, therefore, benefit from using GIS technology, were included in the sample.

Anxiety about computerized and GIS technology had a high statistical significance, but in an opposite direction from expected. Consistent with findings from Nedovic-Budic and Godschalk’s (1996) case studies andregression results from Model 1, the non-users and non-supporters of GIS alike, avoided it less, feared it less, and were less confused by the technology than direct GIS users, indirect GIS users, and administrators in the agencies that rely on GIS.

Finally, all other personal factors, including the compatibility with personal values, perceived complexity of GIS technology, attitude toward work-related change, and networking were not significantly related to the dependent variable in Model 2. The result on the latter two is opposite from the finding from Model 1 with the sample of staff members, GIS users and GIS non-users. While the low significance of the behavioral rigidity of the agency administrators could perhaps be attributed to the fact that the administrators do not expect to personally operate the technology and, therefore, be affected by a possible organizational change, the insignificance of the communication-behavior factor is somewhat more surprising.

Four GIS management factors were considered in Model 1: provision of incentives, training, access to GIS equipment, and user involvement in the implementation process. Provision of incentives to staff members to start using GIS technology was the most significant of the four surveyed GIS management activities. It was also by far the most substantively significant factor of all in terms of the effect on odds of becoming either a direct or an indirect GIS user. The presence of tangible or intangible stimuli to employees (for instance, salary increases, recognition, advancement in position, or title change) increased the odds of being a direct or an indirect GIS user versus being a non-user by more than 20 times, consistent with the hypothesis in this survey research and to the findings in the previous studies (Leonard-Barton 1987; Budic 1993b; Budic and Godschalk 1994).

GIS training was clearly more relevant for direct GIS users than for indirect users. While the coefficient estimate for this factor was statistically insignificant in case of indirect GIS users, for direct users the significance was still low at the 0.16 level, but with doubled odds of becoming a GIS user. This finding affirms the importance of training, but also is a reminder of the still substantial practice of self-teaching (Hearnshaw 1993).

Having GIS equipment installed within the agency and involvement in the GIS implementation process had an unexpected direction (i.e., negative sign) and were, in the case of indirect users, highly statistically significant. Apparently for the indirect users it was irrelevant whether a GIS was located physically in their agency or not, as the majority of them probably used the services provided by other agencies. The indirect users, therefore, did not seem to require either access to the equipment or involvement in the process in order to use the technology through the employees of other organizational units.

It is also plausible that the lack of involvement in GIS implementation did not necessarily prevent employees from becoming direct GIS users. Despite the frequently mentioned importance of user involvement (Eason 1993), the findings of the studies on user involvement in GIS and other information systems are generally mixed (Ives et al. 1983; Raymond 1987; Baroudi and Orlikowski 1988; Carey 1988; Igbaria and Nachman 1990; Budic 1993b; Budic and Godschalk 1994). The insignificance of the accessibility of GIS technology for direct GIS users, however, was quite peculiar and hard to interpret. It could, perhaps, be attributed to a measurement problem.

Different organizational factors were shown as relevant for employees to become direct or indirect GIS users, and for administrators to initiate and support the use of GIS technology. Political support was the most significant factor for direct GIS users. Considering that high political support is generally more present in agencies which house the systems, and that the support is necessary for implementation to take place (Budic 1993a), the coincidence between becoming a direct GIS user and experiencing high political support appears reasonable. The importance of the political support for adoption of GIS technology, however, was not addressed in the previous research at the individual end-user level.

For the direct GIS users, all other organizational factors were statistically insignificant. The results support the conclusions that the agency size, professional staff turnover, population size, annual rate of population growth, and organizational conflict did not affect the decisions to become a direct GIS user. Overall then, except for the political support, the organizational environment and internal context did not strongly influence the direct use of GIS technology by individual employees. This finding is contrary to the previous findings, particularly to the well-acknowledged association between two environmental factors—large jurisdiction size and high population-growth rate—and the adoption of GIS technology (Howard 1995; French and Wiggins 1990; Campbell and Masser 1991; Budic 1993a), and to the confirmed disruptive effect of the organizational conflict (Markus 1983; Budic 1993b; Pinto and Azad 1994). Similar to political support, those factors also have been studied only as determinants of the overall organizational adoption and implementation of GIS.

Another significant factor for the indirect GIS users was the change in the agency environment, expressed as the annual population growth rate. There was insufficient evidence, however, to answer the question of why the intensive change of the external environment would provide stronger stimulation for indirect GIS users than for direct users. Previous research has shown that the size of jurisdiction and the environmental variability can influence the innovation-adoption decisions, but those factors may work differently at the individual and organizational levels.

Of the three organizational factors tested with the administrators, only annual population growth was related to the dependent variable in the expected manner, but with relatively low significance. Jurisdiction size had a higher significance, but with an inverse relationship. Previous research also pointed to the large size of a jurisdiction as a possible justification for postponing the introduction of GIS technology (Budic 1993b).

Following the notion that "people make or break a GIS effort" (Somers 1994) and that adoption of GIS happens through people (Budic and Godschalk 1994), this research focuses on the personal factors that determine whether employees will become direct or indirect GIS users, and whether administrators will support inclusion of GIS into their agency’s toolbox. A few of those factors are found to significantly influence adoption at the individual and the organizational level.

Falsification of the three propositions on computer-related anxiety, perceived complexity of GIS, and values and beliefs about computerized technology, contradicts the findings of the traditional diffusion of innovation research. All three factors are rooted in personal attitudes toward GIS and computerized technology in general, expressed as a personal value system, or experientially One explanation for the discrepancy conceivably lies in the change of the overall status of computerized technology since the 1960s and 1970s—when computers were first introduced in organizations, and when the traditional diffusion of innovation theory started to consolidate (Rogers 1983). In the meantime, general computing has become a common component of modern public organizations (King and Kraemer 1985), and has been internalized into the individual and societal environments. Some of the factors derived from the research and theory that were aimed at understanding the early computerization in the workplace, therefore, may not be applicable to the diffusion of GIS technology The GIS technology itself needs to be re-examined with respect to its definition as a true innovation. Treating it as an extension and new capability offered within an existing technological development may be more appropriate.

Applicability of several other organizational environmental factors, such as the jurisdiction growth rate and size, also needs clarification and further study—particularly with regard to differentiating these factors’ relevance for individual and organizational adoption of GIS. The discrepancy between the findings of previous studies on GIS diffusion and the results of this survey may not really be a discrepancy A majority of past studies have dealt primarily with an organization as the unit of analysis, while this study focused on individuals. Finally there are other factors, such as educational grounding in geography and skills in spatial cognition, that may be relevant for individual adoption of GIS and should be addressed in future research.

GIS obviously imposes a considerably different way of handling spatial data and maps, and many of the factors studied in the research presented here are important for understanding the adoption of GIS technology by organizations and their members. Based on the survey findings, several points of advice are offered to organizations that desire a widespread use of GIS by their employees:

· Create opportunities for exposure to GIS technology and contact between GIS users and non-users.

· To secure support from administrators and decision-makers, organizational benefits need to be clearly emphasized and demonstrated.

· Administrators who do not use computers themselves will need an extra enticement to realize the utility of the new
tool.

· Last, but not the least, beware of the interpersonal conflicts and work hard on understanding and eliminating their causes. The organizational conflicts are the major roadblock to wide diffusion of GIS among organizational members. Non-conflicting environments are much more conducive to mutual support and cooperation needed in GIS technology transfer.

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