Abstract

The focus of this research was an analysis of why some GIS projects fail and other suc-ceed. GIS industry survey data were collected and analyzed as part of a Masters disserta-tion to see if maturity models and structured methods play an important part in project success. Members of GIS organizations in the U.S. (URISA, GITA) provided data includ-ing the respondent’s organizational structure, the success of projects, the type of projects undertaken, and the general attributes of the respondents. Most of the respondents were from county and city governments with a majority reporting that their projects were ei-ther on-budget or under budget, but also reported that projects did not meet deadlines. County, city and transportation sectors were evaluated as having the lowest project man-agement maturity rank as compared to private industry. Data indicate that there is a possi-ble correlation between maturity and project success.

The Application of Maturity Models to GIS ProjectsJames A. Russell1909 Sage Circle

Golden, CO 80401csmminer@msn.com

INTRODUCTION

The application of project management techniques to Geographic Information Systems (GIS) has been a subject of discussion by those in the GIS industry for several years, but little useful data has been published. As a result of this lack of useful data, a research project was initiated by the author as part of a Masters of Science dissertation at Man-chester Metropolitan University, U.K. to collect and analyze data on the use of project management techniques in the GIS industry. This paper presents some of the more inter-esting results of this research and can be used as a starting point for discussion about the application of project management techniques.

Anecdotal information from GIS professionals indicate that GIS projects have experi-enced difficulties in implementation and scheduling, but published data specific to GIS project failures are lacking or not readily available. Thus, it is difficult to determine pre-cisely the reasons of failure and how to propose mechanisms to correct these failures. Nu-merous articles have been written about the management of GIS projects and articles reg-ularly appear that describe best practices needed to produce successful GIS projects. Case studies about successful projects appear in conference proceedings, but these studies tend to be specific in nature and lessons learned may not be easily transferable to other situa-tions. A deeper analysis of GIS project failures and successes that are supported with use-ful, current data would be beneficial to the industry and may result in greater understand-ing of the factors that produce cost overruns and extended time to completion problems.

GIS is used by organizations to solve a broad spectrum of problems not typical of the In-formation Technology (IT) industry. Even though, GIS is increasingly thought of as a subset of the greater IT world. Researchers have examined the problems present in the IT industry and have used data on IT successes and failures as proxies for similar problems in GIS (Hamil, D., 2001). The data relating to IT project successes and failures have been collected and published in the last decade and it has been asserted by some that GIS projects fit a similar pattern. Much information on the success of GIS projects is project specific and a thorough study is needed to determine if current project management tech-niques can be applied to GIS projects to improve their performance.

While experiencing similar management problems to IT projects, GIS projects may not be the same. GIS projects often are managed by those with varied management experi-ence, and involve personnel with a broad set of professional and educational back-grounds. These factors may distinguish GIS professionals and their projects from their IT industry colleagues and professionals.

GIS PROJECT MANAGEMENT AND MATURITY MODELS

A current trend in the project management industry is the development of management models that rank and assess organizations based upon their knowledge and application of project management principals, techniques, and benchmarking. These models, referred to as maturity models, may be adapted to the GIS industry and could lead to greater project management effectiveness. Organizations that use GIS may find improvement in manag-ing projects by implementing management models and associated structured development methodologies suited to the GIS industry.

The project management methods that were developed in the IT industry are used to con-trol costs, time, and quality. The methods employed have evolved to a point that other in-dustries now find great value in them and have incorporated them into their culture. For at least thirty years, researchers and writers have tried to develop a method of characteriz-ing organizations and establishing their place in a spectrum that represents their level of management sophistication and maturity. This placement, which is based upon a set of characteristics, establishes a road map that the organization can use to progressively im-prove success and quality (Kerzner, 2005).

Establishing one’s rank by use of maturity models assumes that all organizations react the same way and are willing or capable of following the same path to greater success. This may or may not be the case. Organizational culture plays an important role in work meth-ods and should be considered in the determination of the path leading to success. There may be multiple pathways to success depending upon the organization’s structure just as there may be multiple pathways to failure. Many in the project management discipline have argued for the use of maturity models as a means of advancing an organization to higher efficiency and increased project successes. Indeed, the development of maturity models was the result of project failures and a need to increase the success rate of projects. Early promoters of increasing success through the characterization of organiza-tions and their projects first appeared more than twenty-five years ago (Crosby 1979). Since then, the IT industry advanced the concept of improving efficiency by defining a series of steps and processes that organizations must advance along to improve success rates of projects. This ‘means to an end’ seems not to consider the differing nature of how organizations are structured or operate.

A deeper understanding of the culture and nature of organizations that use GIS is impor-tant to fully understand how they can increase the chances of success. This article will ex-plore the nature of GIS project successes and failures based upon questionnaires sent to industry and governments, and based upon these results suggest management techniques useful to improve success rates. Data from questionnaires may help answer the following questions:

1. Is it possible to define a set of practices and tools that can be implemented to achieve greater success rates across differing types and cultures of an organization?

2. Is organizational culture important to the implementation of a maturity model approach for GIS projects?

3. Who should or should not use maturity models?

4. What are the roadblocks to implementing a maturity model approach to greater efficiency?

5. How can one forecast greater success? 6. Is it possible, based upon the responses, to develop a modified maturity

model that could describe a useful path to greater success for organiza-tions that implement GIS?

HYPOTHESIS DESIGN

The following hypotheses were used to apply focus to the nature or success or failures in the GIS industry:

1. Hypothesis 1: Organizations with a set of attributes that represent a higher level of project management maturity have greater success in GIS projects than those that do not.

2. Hypothesis 2: Organizations that have higher maturity levels use project manage-ment techniques more effectively to become more efficient whilst others that do not follow project management maturity model concepts at all will have less suc-cess.

3. Hypothesis 3: Groups that do not have an organizational structure characterized as goal-centered, systematic, and analytical; have poorer success rates than those that do not.

The design of the survey is an important part of the research and was used to test these hypotheses. The IT culture is considered measurement-based, goal oriented, and produc-tivity aware. The assumption of an industry-wide, uniform, organizational culture may not be a valid one. Organizations that do not have such a culture may need a different ap-proach to project management.

Defining a success or failure can range from being readily apparent to very subtle. In many cases, failure is obvious to all concerned. In some cases, success or failure may be determined by one’s point of view and includes a certain amount of subjectivity. Intro-ducing the human element of organizations is important to the research because this com-ponent gives an incomplete picture of the problem. Quantification of success and failure, therefore, can be problematic.

By collecting data on GIS project successes and failures, organizational characteristics of groups using GIS, and what techniques used to manage GIS projects, a better picture may be developed of GIS project management conditions that currently exist in industry and government.

THE DESIGN FOR DATA COLLECTION AND RESEARCH METHODOLO-GIES

As mentioned earlier in this article, a series of questions were given that define the nature of this research. These questions became the basis of data collection by a written survey submitted to 250 members of the Urban and Regional Information Systems Association (URISA), GIS professionals in the Geographic Information Technology Association

(GITA), and other professionals through list servers on the Internet. Since all but two re-spondents are from the United States, any conclusions based upon the results of the sur-vey should only apply to GIS organizations within the United States.

The survey was designed to collect specific information from a set of respondents. This information was used to calculate a maturity rank for the respondent’s organization, and other information about the type, nature, and success of the respondent’s organization. A great deal of time and thought was spent not only on the design, but also on how the sur-vey was to be distributed. Returned surveys were tabulated, and a set of statistics calcu-lated in hopes of shedding light on the questions listed above.

Survey Design

Survey data covering several general categories were used to describe the respondent’s organization and most of the questions produced an approximation of the organization’s project management maturity level. Survey questions were crafted with a minimum of project management jargon. All that was known about the potential pool of respondents is their membership in the GIS professional societies listed above. Therefore, no assump-tions could be made as to the respondent’s knowledge of project management. It was as-sumed that this survey was distributed to many who may have no formal project experi-ence or management knowledge, but may know how GIS projects are carried out within their organization.

A complete project management maturity assessment could involve many more questions than those chosen, but would be a deterrent to completion of the rest of the survey. The maturity models that formed the basis of these questions are described in Kerzner (2005), Office of Government Commerce (2002), and Project Management Institute (2003).

Survey Distribution Methods

The survey was distributed through several methods in an effort to save money on postage and to receive surveys from a wide variety of respondents, both geographically and by organizational type. Two efforts were made to distribute the survey via email and the web. This produced a total of 30 responses. To increase the response rate, 250 surveys were posted to members of URISA with included self-addressed, stamped return en-velopes. The membership mailing list used for this effort was purchased from URISA. This effort was highly successful in that 94 surveys out of 250 (37.6 percent) had been re-turned. Surveys were mailed to most U.S. states in an approximate proportion to the state’s membership in the URISA.

ANALYSIS OF RESULTS Analysis of the data required examining more than 45 different values for each survey. Data collected included a combination of categorical data and interval measures. Exam-ples of categorical data include management style or project type. Maturity rank for each

survey was calculated from the respondent’s answers to 29 questions a range of 1 to 100. Data analysis can be broken into two main categories:

1. Data that presents a generalized picture of the respondent’s organization, and2. Data used to determine if there are any significant relationships between ma-

turity rank and either an organization’s culture or an organization’s manage-ment style.

A total of 124 responses were used for this analysis. While this data provided important information about the nature of GIS organizations and their project management sophisti-cation, the number of responses and the variances calculated in the data set did not present clear correlations between the data variables. General trends can be inferred from the descriptive statistics, but hard correlations proved difficult. While the return rate was acceptable given the distribution mechanism, a higher response rate and a larger number of mailed questionnaires may have helped to reduce the data variances encountered in the survey results.

Organization TypeA significant number of survey respondents were from the county and city categories (Figure 1). More than 71.7 percent of the respondents are employed in county, city, state or federal government while 28.3 percent represent non-governmental organizations. This distribution is reflective of the membership of URISA. A smaller percent of respondents indicated that their organizations operated in the private sector or listed ‘other’ as a cate-gory (18.3 percent).

Figure 1

Respondent Organization Type

The initial design for data collection was aimed at the GIS industry in general and not specifically at any sector of the industry. Since such a high percentage of respondents were from the governmental sector, much of the data analysis and conclusions should take this fact into account. It may be difficult to draw useful conclusions about project management maturity of the GIS industry as a whole with a distribution that is not reflec-tive of the total group.

Organization Budgets

Data collected about GIS budgets indicate that 87.9 percent of GIS budgets were greater than $10,000 and that half of the budgets were between $50,000 and $1,000,000. The sur-vey requested a budget breakdown for GIS-related projects, not GIS departmental bud-gets, and therefore should not include department-wide costs such as non-project over-head expenses.

Counties and cities are the two organization categories that represent 62.1 percent of all of the respondents. Data indicates that 76.6 percent of the respondents who work for county governments report that their budgets are within the $10,000 to $1,000,000 range with 29.8 percent reporting in the $200,000 to $1,000,000 range. Budgets for city govern-ments show a slightly different distribution. Survey data Indicates that 63.3 percent of re-spondents working for cities report that their budgets are within the $10,000 and $200,000 range. The greatest number (36.7 percent) reported that their budgets are within the $50,000 to $200,000 range.

Management Structure

As part of the analysis, data were collected on management structure and organizational culture. The data are very important to this analysis, since management structure or orga-nizational culture may have an influence on the project management maturity of the orga-nization or the organization’s success. Management structure categories for this research were based on Kerzner (2003). These management structure types are defined as follows:

1. Traditional – The organization is managed by a general manager that has all the functional entities necessary to perform research and development, to develop and produce a product. All activities are performed within the functional divisions and are managed by department heads. Functional managers maintain absolute control over the budget. All communications must be channelled through upper manage-ment.

2. Line-Staff – The project manager serves as a focal point for activity control – a center of information. The project manager keeps the division manager informed of project status and tries to influence managers to keep project activities on schedule. Functional managers still control work assignments, but the project manager may given authority by the division manager for certain project func-tions.

3. Matrix Organisation – The organization/department is ‘project-driven’. Each project manager reports directly to a general manager. Power and authority used

by the project manager come directly from the general manager. Individuals are chosen from functional departments for the project and will spend full time on the project. The style used is one of collaboration. Decision-making and direction rests with the team. All project team operates as a separate entity except for ad-ministrative purposes.

The survey data show that the traditional management structure is characteristic of more than 50 percent of the respondent’s organizations. Of those respondents whose organiza-tion is reported as county, city, state, or federal government, 57.3 percent report a tradi-tional structure, 15.7 percent report a matrix structure, 22.5 percent report a line structure, and 4.5 percent report ‘other’ as their management structure. Of those respondents whose organization is not reported as county, city, state, or federal, 34.3 percent report a traditional structure, 42.9 percent report a matrix structure, 17.2 percent report a line, and 5.7 percent report a management structure as ‘other’. These data indicate that non-gov-ernmental organizations are more matrix oriented and less traditional or line oriented (Ta-ble 1). The impact of management structure on project management efficiency is not clear and may or may not have an influence of the maturity level of an organization. Other factors such as culture or managerial leadership may also play as important a role as the structure of an organization.

Organization Type Traditional Matrix Line OtherGovernmental 57.73 15.73 22.47 4.45

Non-Governmental 34.29 42.86 17.15 5.71

Table 1Organization Structure by Governmental or Non-Governmental Class

Organizational Culture

Five organizational culture categories that were used for this survey were based on the cultural traits defined by R.E. Quinn and M.R. McGrath (1985). The organizational cul-tures are as follows:

1. Hierarchal – Characterized by top-down decision-making, adherence to rules, resistant to change, conservative and cautious leadership, formal rules and proced-ures, and emphasis on stability and control.

2. Rational – Characterized by productivity and efficiency, complex tasks, goal-centred, systematic and analytical, goal-oriented leadership, responsibilities based on expertise, open to goal-driven change.

3. Consensual – Characterized by cohesion and morale, collaborative work groups, participatory decision-making, deliberative, and open to change. A team builder, supportive leadership style.

4. Developmental – Characterized by flexibility, adaptability, and intuitive de-cision-making. Leadership style is idealistic, risk oriented, and empowering.

Respondents to the survey indicated that a high percentage of their organizations were ei-ther hierarchal (32.3 percent) or rational (29.8 percent) with a slightly lesser amount re-porting a consensual organizational culture (25 percent). Those reporting a developmen-

tal culture totaled 9.7 percent. IT or GIS departments are viewed by many as having an analytical or technical approach to carrying out their functions within an organization. This technical approach to performing project work may lend itself to a rational or hierar-chal culture.

Completion Success

One measure of project success is if the project met its projected budget (Table 2). A high percent of respondents reported that their projects were completed on budget or un-der budget (87 percent). A second measure of success may be the timeliness of project completion (Table 3).

Budget Completion Success Frequency PercentOn budget 95 76.6Over budget 11 8.9Under budget 13 10.5No response/other 5 4.0Total 124 100.0

Table 2Budget Completion Success

More than 58 percent of respondents reported that their projects were meeting their com-pletion times or exceeded their expectations for completion times (Table 3). The data in-dicated that there was no significant relationship between the type of organization and the missing of deadlines. As an example, 84.1 percent of those that missed their deadlines worked for government organizations. The total percent of respondents that worked for government organizations represented 89 percent of the total survey. If both budget suc-cess and project timeliness are measures of project success, then a majority of organiza-tions have found ways of efficiently controlling their projects.

Completion Success Frequency PercentExceeded expectations 19 15.3On time 57 46.0Misses 46 37.1No response/other 2 1.6Total 124 100.0

Table 3Completion Success of Respondents (Time)

GIS Project Management Maturity Measures

A major part of the survey was the estimation of the project management maturity of or-ganizations based upon a set of assessment questions. These questions were designed to give an approximation of the maturity level of the respondent’s organizations. Those who manage projects may best assess project management maturity. In this survey 75.8 per-cent listed their positions as either project manager or manager.

Data from the survey were summed and normalized to a scale of 1 to 100 with a score of 100 being the highest level of maturity and 1 the lowest level. These values were corre-lated with other variables from the data set derived from the survey questions.

The first data combination that was examined was organization type versus mean matu-rity rank (Table 4). It should be noted that this table does not reflect the number within each organization type, but it does show the actual maturity of each type. One interesting observation is that those types that have the highest maturity rank are those that represent private business. Government organizations such as counties had a much lower maturity rank (51.8). Two possible explanations for the federal government category receiving a higher maturity rank than counties or cities may be because of sample size differences or that federal agencies tend to contract out more work than county governments although data was not collected to determine if this was the case.

Category Maturity RankOther 74.0Service Industry 73.0Com. Software Development 65.3Federal 64.8Environmental 64.5Contractor 63.1State 58.2Remote Sensing 56.7Utility 56.4Engineering 52.7County 51.8City 48.2Transportation 47.0

Table 4Organization and Maturity Rank

Budget data were also examined to see what, if any, relationship might exist between ma-turity rank and budgetary success of the respondent’s organization (Table 5). Data in Ta-ble 5 indicate that there is a possible relationship between maturity rank and budgetary success of the respondent’s organization. Those reporting that their projects were under budget had the highest maturity while those that reported that their projects were over budget received the lowest maturity rankings.

Maturity Statistic Under Budget On Budget Over BudgetMean 57.1 54.65 45.34Median 56.9 53.45 45.26Mode 47 49.14 24.14

Standard Deviation 13.26 12.62 16.65Count 13 95 10

Table 5Statistical Results of Mean Maturity Rank and Budget Success

Similar to budget completion success data, survey respondents that reported that their projects were on time or exceeded their expectations had a higher maturity ranking than those that missed their project deadlines (Table 6).

Maturity Statistic On Time Exceeded Expectations Missed DeadlinesMean 58.15 53.18 49.38Median 56.03 54.31 45.69Mode 56.03 63.79 60.35Standard Deviation 12.52 14.92 16.8Count 57 19 46

Table 6Statistical Results of Mean Maturity Rank and Project Timeliness

The data suggest that success, either the measures of meeting budgets or project timeli-ness, may have some correlation with project management maturity levels. One part of this research was an examination of any relationship between project management matu-rity and either management structure or organizational culture. An analysis of project management maturity and management structure indicates that there may be such a rela-tionship, but a weak one (Table 7).

Maturity Statistic Matrix Management Traditional Management Line ManagementMean 60.79 51.77 51.31Median 63.79 50.86 50.86Mode 61.21 40.52 47.41Standard Deviation 14.53 16.02 12.26Count 29 63 25

Table 7Statistical Results of Mean Maturity Rank and Management Structure

One category referred to as ‘other’ was not included since they were not defined by the respondents in the survey and represented only two of the 124 data points. Null values were also not included in the analysis. The data indicates that those organizations with a matrix management structure had higher mean maturity ranks than traditional or line structures. This may be because those with highest maturity are commercial businesses and have a greater percentage of matrix-structured operations.

An analysis of mean maturity rank and organizational culture also indicates a possible but weak statistical relationship with certain cultures (Table 8). Those respondents that re-ported a rational organizational culture had the highest maturity, followed by consensual, hierarchal, and developmental. The spread between the top three cultures is very small

for the mean maturity rank, and all exhibit significant variances, indicating that if a corre-lation exists, it is a weak one at best. The median and mode of the maturity rank also were higher for rational than the other cultures surveyed.

Maturity Statistic Rational Consensual Hierarchal DevelopmentalMean 56.27 55.20 52.65 48.35Median 56.04 53.45 48.28 49.14Mode 61.21 49.14 40.52 -Standard Deviation 13.81 11.10 18.84 14.72Count 37 31 40 12

Table 8Statistical Results of Mean Maturity Rank and Organizational Culture

Project Management Methods Used by Mature and Immature Organizations

The last question on the survey asked the respondent what project management methods were used at their organizations and to rank these methods as to their usefulness to their organization. Those organizations that ranked in the highest 10 percent in maturity used more project management methods in the aggregate (22 in number) than those that ranked in the lowest 10 percent (12 in number). The actual number of project management methods used by individual organizations also varied. Those organizations that ranked in the top 10 percent of maturity used between 7 and 8 project management methods while those that ranked in the lowest 10 percent of maturity used an between 3 to 4 project management methods.

The five most commonly used project management tools for those organizations that ranked in the top 10 percent of maturity were in descending order:

1. Statement of work,2. Time scheduling,3. Previous projects,4. Work breakdown structure, and5. Project management software.

The five most commonly used project management tools for those organizations that ranked in the lowest 10 percent of maturity in descending order were:

1. Previous projects,2. Work breakdown structure,3. Time scheduling,4. Project management software, and5. Gantt charts.

Two important observations can be made from the data: 1. The organizations with the highest maturity used more project management meth-

ods in the aggregate those at the low end of maturity, and

2. The variety of project management methods was greatest with those that had the highest maturity rank than those that had the lowest maturity rank.

Other Data Collected in the Survey

Other data were collected in the survey to see if there were any other influences on GIS project management. Other data were:

1. If projects meet specifications,2. Who considers projects successful,3. What concerns management the most about in GIS project comple-

tion.Respondents reported that projects met specifications 86.29 percent of the time. Respon-dents were split as to what concerned them the most about project completion. The sur-vey indicated that 55.7 percent of respondents thought that management was most con-cerned with being over budget while 37.9 percent indicated that management was most concerned with projects taking longer than expected. An implication to this data is that the labor component is not as important to management as total project costs.

Data Analysis ObservationsInferences that can be drawn from the data set and this analysis are not definitive, but do indicate that further work can be done to determine if there is a relationship between GIS project success, maturity levels of organizations, and other influences such as manage-ment style or organizational culture. The analysis indicates that there may be relation-ships present, but the data set also shows significant variances. A larger data set may help reduce the variance in the data and be able to lead to stronger conclusions.

One set of data that also was collected described what project management tools were used by the respondent’s organizations. There seems to be a correlation between the num-ber of project management tools employed and the diversity of tools used by more mature organizations. A more in-depth examination of this relationship, if one exists, should be a made in another study. Having a greater number of tools available to project managers may be a function of departmental budgets, personnel, or commitment to the planning process by management. A survey that collects data that can be used to infer these rela-tionships, if they exist, to a greater degree of accuracy would be useful to organizations looking to improve their success rate. Getting organizations to participate in such a study that examines this point in greater detail may be challenging, but with a good response rate, such as was done in this article, would lead one to think that it is possible with suffi-cient resources.

CONCLUSIONS AND RECOMMENDATIONS

This research centered on the idea that a GIS organization’s structure and management methods can have an influence upon the projects it undertakes. Project success can be de-fined many ways but is dependent upon the evaluator’s perspective and definition of suc-cess. Two main measures of a project success generally include how the project meets its financial budget and how long the project takes to complete. Both of these measures con-

tain a certain amount of risk. Time for completion for most organizations is linked to money spent through the labor component of a budgeted project. For those in govern-mental organizations, labor is viewed differently than for commercial organizations. Per-sonal observation indicates that governmental budgets can be cut, but governments rarely reduce their workforce as quickly. However, reductions in governmental workforces can and do occur. The fact that labor is thought of differently by governmental organizations in the U.S. as compared to commercial enterprises creates a difference in how success is viewed between the two types of organizations. Surveyed organizations that received high maturity ranks were successful from either a budgetary standpoint or a timeliness standpoint. The survey was designed to answer and succeeded in determining what dis-tinguishes the top performers from the bottom performers.

In recent years project management maturity has been a topic of great interest to many in management. Every manager wishing for success needs to examine all the available tools to him to improve his group’s performance. Project management maturity models have come along to meet this need for improved performance and quality. The promoters of maturity models claim that those that follow the maturity model path will reap greater success. Case studies do show that the maturity model ideas and concepts do provide benefits. However, do maturity models provide benefits to organizations that have differ-ing structures or styles from those of large multi-national corporations? Dissecting and subdividing organizations by management style, organizational culture, and organization type shed light on the potential weaknesses to maturity models and their claims of project success. It is nearly impossible to perform a total, detailed maturity assessment of the GIS industry, although data can be collected that may indicate a general level of maturity present in a subset of the industry. An attempt of this type was done for this dissertation. Little hard data has been collected for the GIS industry that describes project success, project failure, or the application of maturity model concepts to GIS projects. The data collected from the survey attempts to shed light on these areas.

The first hypothesis stated that organizations with a set of attributes that represent a higher level of project management maturity have greater success in GIS projects than those that do not. The survey data indicated that those respondents that reported meeting their budgets or under their budgets ranked higher in maturity than those that did not meet their budgets. The survey data also indicated that those that reported meeting their deadlines or exceeded their expectations for timeliness ranked higher than those that missed their deadlines. If both survey measures, meeting their budgets and completing their projects on time, are true indicators of project success, then the first hypothesis is proved. Additional support for the hypothesis is given when one considers that both mea-sures of success show the same trend.

However some problems exist with the data. The first data problem that throws doubt on the finding is the sample variance and standard deviation. For both budget success values and project timeliness values, survey results indicated high sample variances and standard deviations. This may be the result of a small sample size. If statistical noise had a strong influence on the results, both survey measures could have different trends or no discern-

able trend at all. Taking this fact into consideration, it seems that at least a weak correla-tion exists between project management maturity and success.

The second hypothesis states that those organizations that have higher project manage-ment maturity use project management techniques and are more efficient or successful than those that do not employ project management techniques or models. This is similar to the first hypothesis, but addresses the tools used by more mature organizations and compares them to the tools or techniques used by those organizations that may be less successful or mature. If one accepts the first hypothesis that relates project management maturity with success, then an evaluation of tools and techniques used by organizations to manage projects may shed additional light on why organizations are successful. Data was collected in the survey about what tools were used and how many tools were used by or-ganizations to manage their projects. The data indicates that those organizations in the top 10 percent of maturity used more management tools than those in the bottom 10 percent of maturity. Also, those in the top 10 percent used a larger variety of tools in the aggre-gate than those in the bottom 10 percent of maturity. How effective organizations are in the use of the tools is not known, but project success may be an indicator of the effective use of these management techniques. An implication that can be made from the survey data is that organizations that use a larger selection of management tools and have high maturity levels will be more efficient or successful than those organizations that use a smaller set of management tools. The management tool survey data supports the second hypothesis.

The third hypothesis states that the rational organizational structure has poorer success rates than those that do have a rational organizational structure. Survey data does not sup-port this hypothesis. If maturity rank is used as a measure of success, one may see data that would indicate that those organizations with a rational organizational culture would have the high maturity ranks as compared to other organizational cultures. While the data shows that the rational organizational culture is slightly higher than the others, it is not sufficiently different than the consensual or hierarchal cultures to prove the hypothesis. Other influences need to be considered. The data infer that there is a relationship be-tween the type of organization and maturity rank. Commercial organizations tend to have higher maturity ranks than do county or city governments. Also, these commercial orga-nizations have a sizable number that report consensual or hierarchal cultures. Although the relationship between maturity rank and organizational culture is extremely weak, other factors such as leadership skills, project knowledge, management tools, or some other intangibles need to be considered when determining why some organizations have higher success rates than others.

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ABOUT THE AUTHORThe author received a B.S. from Purdue University, an M.S. from Colorado School of Mines, and an MSc from Manchester Metropolitan University in GIS and Management. He currently is the GIS Coordinator for Gilpin County, Colorado and a developer of soft-ware for the natural resource industry.