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E-procurement Implementation in the United States:
Understanding Progress in Local Government
Abstract
E-procurement is a rapidly growing area of e-government implementation. Nevertheless,
scholarly research on e-procurement implementation is limited, especially at the municipal level.
This study presents empirical evidence on city level e-procurement in the United States, based on
a data set of the 190 largest cities. This study focuses on two research questions – 1) Does e-
procurement progress in a staged manner as suggested about e-government by the stages theory?
2) What factors contribute to the progression of e-procurement systems through the stages? We
find not only that e-procurement is more prevalent in the information or cataloging stage than in
the transaction stage but that cities with both IT capacity and a council-manager form of
government are more likely to be in an advanced stage of e-procurement development. By
providing both descriptive statistics along with statistical analysis, this study helps to explain the
current status of e-procurement implementation along with the factors that might lead to further
implementation.
Introduction
Scholarly research on information and communication technology (ICT) in government (often
referred to as e-government) has grown substantially over the past two decades. While research
may not keep pace with technology itself, it is able to describe the current state of e-government
implementation and help the field gain a proper footing as it moves into future technologies. E-
government has the potential to impact many aspects of government which may lead to greater
effectiveness and efficiency. According to Carter and Belanger (2005), the major benefits of
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adopting e-government are increased government accountability, transparency, and access to
information along with a more efficient and cost-effective government. E-government also
enables effective public reporting and communication by government and public organizations
towards the citizenry.
Electronic procurement (e-procurement) is a rapidly growing area of e-government
implementation and it offers many of the benefits highlighted. Today, there remains a lack of
general understanding about e-procurement implementation even though local government
procurement spending is a major component of local budgets. State and local governments spent
about 1.7 trillion dollars on the purchase of goods and services in 2011 (Keating, 2012). E-
procurement offers a mechanism to make this process more efficient than is currently in practice;
yet, little is known about either the level of e-procurement implementation in U.S. cities or which
factors might lead to its development.
Framing the Issue
The adoption of e-government, as indicated by a 2011 international survey of global municipal
websites, is increasing. Among the 100 cities selected for the 2011 survey, 92 had official
websites, compared to 87 in 2009 and 81 in 2005. Based on the findings, the overall average e-
government score for municipalities surveyed increased from 28.49 in 2003 to 33.76 in 2011,
(Holzer & Manoharan, 2011) – indicating that municipalities, globally, are increasingly using
technology to improve effectiveness and efficiency. In the United States, all 50 states, as well as
almost half of all cities with a population over 100,000 had developed official websites by the
spring of 1997 (Stowers, 1999). Almost 16 years later, not only has the use of technology in
government increased substantially, but governments have moved well beyond simple website
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development to more interactive and service oriented technologies. Such growth is particularly
notable in e-procurement.
E-procurement is the term applied to conducting transactions between authorities and suppliers
over the Internet. E-procurement involves several stages such as preliminary identification of a
need, exchanging goods and legal tender and contract management (Corsi et al., 2006). As
Edmiston (2003) points out, “if electronic government has taken hold anywhere, it is in the area
of government procurement, or electronic purchasing” (p. 24). Scholarly research on the
implementation of e-procurement is limited. Thus far, research primarily focuses on state level
data (Coggburn, 2003; Moon, 2005; Reddick, 2004) with limited research on cities (Edmiston,
2003; Reddick & Frank, 2007). Outside the United States, e-procurement research has gained
more traction focusing on the European and Asian experience. While such research adds greatly
to the knowledge base on e-procurement implementation, it does little to explore the nature of e-
procurement in U.S. cities. Bromberg et al. (2012) compare the implementation in U.S. cities vs.
European cities, but their research can be broadened to provide a more comprehensive
understanding of municipal e-procurement. Our research seeks to broaden and update these
authors’ findings by providing additional empirical evidence on the implementation of e-
procurement at the U.S. city level.
Because e-procurement involves several stages—from preliminary identification of a need to
exchange goods—stages theory seems a viable theoretical framework for its analysis. We test
this viability by addressing two research questions: First, we ask if e-procurement progresses in a
staged manner as suggested about e-government by the stages theory. Generally, stages theory is
applied to an e-government as a complete phenomenon. Rarely, is it applied to individual e-
government features such as e-procurement. We examine if e-procurement, scrutinized as an
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individual feature, presents similar stages to the overall e-government phenomenon. If this is the
case then managers might find success in implementing individual features such as e-
procurement systems in a staged manner. Second, we seek to determine the factors that
contribute to the progression of e-procurement systems from basic features to more advanced
features. Using a data set of the 190 largest cities in the United States, collected through website
analyses, this study shows a specific picture of the current state of e-procurement implementation
among the larger cities in the United States. By providing both descriptive statistics along with
statistical analysis, this study helps to explain the current state of e-procurement implementation
along with the factors that might lead to more advanced implementation.
We hope that answering these research questions will not only bolster practice but will also
advance the theoretical debate on e-government stages. While many scholars have proposed a
stage-wise development, some have dismissed these e-government stages (Coursey & Norris,
2008). For example, Coursey and Norris (2008, p.532) argue that such “predicted movement is
not happening, or if it is, the movement is glacial in its speed.” However, Lee (2010) highlights
the importance of this stage development model because of the inability of e-government to be
incorporated and fully implemented in a single step. Instead of aiming for large-scale success,
those responsible for e-government implementation should aim to produce successful results at
each and every stage of development. We hypothesize that individual features of e-government
likewise occur in a staged manner.
This research provides a new perspective to examining individual e-government features in terms
of various stages. While we know that e-procurement implementation takes place a later stage of
e-government implementation, we should not assume that it e-procurement implementation takes
place all at once. E-procurement, in particular, requires large capital investments, indicating that
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stepwise progress might be the best option for cities. By understanding if it takes place in a
stepwise manner and what factors might lead to e-procurement growth mangers can direct
appropriate investment towards pursuing a comprehensive system. . Lastly, e-procurement
research focuses heavily on state level e-procurement implementation. However, the successful
incorporation of e-government into local governments has better potential to beneficially affect
success on the national stage (Sarikas & Weerakkody, 2007), and citizens tend to access local
government websites more than their state websites for information and services. This research
offers a unique perspective on local level implementation, along with introducing practical
differences between local government and state government.
Benefits of E-Procurement
The benefits of e-procurement, which are similar to the general benefits of e-government,
include greater transparency and greater efficiency (World Bank, 2003). Greater transparency in
the bidding process for public projects may reduce the possibility of corruption. Greater
efficiency results from a direct channel between suppliers and customers, thus limiting the need
for middle agents. E-procurement can thus lead to substantial savings in costs and supply
functions in both the public and private sectors. It also has the potential to improve the
relationship between buyers and suppliers by developing informative websites to improve the
channels of communication (Purchase & Dooley, 2010). According to Moon (2005), e-
procurement “decentralizes procurement management, making the procurement organization
flatter and less hierarchical. The system also saves time and reduces total costs by providing
comprehensive views of procurement decisions and multiple procurement choices, (p.62).” The
benefits, as noted by the World Bank, are presented in Table 1 below
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Table 1 Benefits of E-Procurement Government Supplier Public Transparency • Anti-corruption
• Increased number of suppliers
• Better integration and interaction between governments
• Professional procurement monitoring
• Higher quality of procurement decisions and statistics
• Political return from the public
• Increased fairness and competition
• Improved access to the government market
• Open the government market to new suppliers
• Stimulation of SME participation
• Improved access to public procurement information
• Government accountability
• Access to public procurement information
• Monitor public expenditure information
• “Have a say” • Government
accountability
Efficiency Costs
• Lower prices • Lower transaction
costs • Staff reduction • Reduction in fiscal
expenditure
• Lower transaction costs
• Staff reduction • Improved cash flow
• Redistribution of fiscal expenditure
Time • Simplification/ elimination of repetitive tasks
• Communication anywhere/anytime
• Shorter procurement cycle
• Simplification/ elimination of repetitive tasks
• Communication anywhere/anytime
• Shorter procurement cycle
• Communication anywhere/anytime
Source: World Bank Draft Strategy: E-Government Procurement 2003
Drawing on insights from the private sector, likewise, suggests e-procurement improves quality
and efficiencies. Vaidyanathan and Devaraj (2008) find that utilizing technology to work with
vendors or suppliers increases quality and accuracy of procured goods. Primarily, a need to
“exchange quality information” is acknowledged to increase effectiveness which is enhanced
through the use of an e-procurement system. (Vaidyanathan and Devaraj, 2008, p. 420).
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Additional findings suggest that the implementation of e-procurement systems not only increases
performance for the contracting agency but also may increase performance for the vendor (Tai,
Hob and Wu, 2010). This may in turn, “lead to better partnership between buyers and suppliers,”
(Tai et al., 2010, p. 5411). While benefits of e-procurement are well documented it remains
vague as to how many cities are moving towards e-procurement implementation
Growth of E-Procurement
Studies show that a majority of state government procurement offices are utilizing e-procurement
(Moon, 2005; Reddick, 2004). For example, a 2001 survey by the National Association of State
Procurement Officers (NASPO) shows that 43 of 47 state procurement offices had websites and
utilized some aspect of e-procurement (Moon, 2005). His research demonstrated that about 90%
of states post solicitations/bids and contract award information online, and furthermore, use an
automated procurement system. While the findings are primarily from the NASPO survey, Moon
conducted email follow-ups to the states that did not respond to the original survey. His
subsequent findings yielded higher results than the 2003 NASPO survey results, which are
reported by Reddick (2004). While Reddick (2004) finds that the same number of states reported
having a central procurement website, only 82% of states reported posting solicitations on the
web. Moreover, Reddick (2004) reported that only 64% of states post contract award
information on the web. About 30% of states have promulgated procedures or have statutes
governing Internet bidding and about 20% actually conduct bidding over the Internet.
The factors and trends in state e-government may not be similar to those in municipal e-
government. There are large costs associated with any e-government initiative. Many of these
costs can be absorbed at the state level, which cannot be absorbed in city or municipal budgets.
Moreover, the past two decades have seen significant investment by the federal and state
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governments associated with a number of ICT initiatives. While some of these have trickled
down to local government, investment has been much more variable (Dawes, 2008). Much of the
research indicates that initial investment leads to long-term savings, but initial investment also
serves as a roadblock at the local level of government (Bromberg et al., 2012).
It is much more difficult to identify e-procurement practices at the local level within the United
States. While some research offers insight into local level e-procurement, it is not as
comprehensive as state level data. Edmiston (2003) reports results from two surveys – one
conducted by the International City County Managers Association (ICMA) and the other by the
National Association of Counties (NACo). The NACo survey was conducted in 2001 on county
government while the ICMA survey was conducted in 2000 on city governments. According to
the ICMA survey, about 48% of respondents offered procurement online, whereas only 4.2%
responded similarly to the NACo survey. Furthermore, about 25% of the ICMA respondents
offered bids and proposals online, compared to only 6.7% percent of the NACo respondents
answer in a similar manner. The lack of specificity around the terms “procurement” and “bids
and proposals” makes it challenging to assess exactly what is taking place in local and county
governments, but these findings provide a strong indication that a great deal of e-procurement is,
in fact, taking place. Compared to the states, municipalities show lesser adoption rates of e-
procurement, based on the results of the U.S Municipalities E-Governance Survey (Holzer et al.,
2009). Among the 100 largest cities whose official websites were evaluated by the survey, only
13 cities enabled potential bidders to place bids online. However, about half of all cities provide
access to RFPs as well as the ability to downloadable documents.
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Reddick and Frank (2007) examined the impact of e-government on local governments in Texas
and Florida. While their study is broader than e-procurement, and examines perceptions of
managers, it provides some evidence of the remaining challenges. When asked if “Purchasing
over the Internet has broadened our vendor pool, increased quality, and reduced costs,” about
60% of respondents answered positively (either agree or strongly agree). However, when asked
to respond to the statement, “Traditional government procurement practices have not hampered
our ability to implement purchasing over the Internet,” about 39% responded positively, whereas
27% were neutral and 34% disagreed. Many are still finding it a challenge to implement e-
procurement based upon traditional procurement practices and policies.
To date, a study that falls outside of the peer reviewed literature E-Procurement Adoption in
Local Governments of the United States provides the most direct attempt to understand U.S. local
level adoption of e-procurement. The study is limited by a 5% response rate to a survey sent to
2,000 local procurement managers. Nevertheless, a brief overview of their findings is helpful in
gaining a sense of implementation. The authors find that in 2006 about 51% of respondents
implemented some form of e-procurement (Prier & McCue, 2007). Within the last three years,
36.5% of respondents had implemented this system. The managers that had yet to implement an
e-procurement system expressed that they planned to do so within the next three years. Those
that had implemented e-procurement expressed mixed levels of implementation. About 30%
responded that the system was integrated with financial and other IT systems whereas the
remainder expressed that their systems were independent of other systems.
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A Framework for E-Procurement Adoption
Stages Theory
Many scholars have proposed a model of e-government occurring in various stages, beginning
with the development of a simple website progressing to fully integrating and involving all
departments, as well as citizen participation. Based on their study of e-government, Layne and
Lee (2001) proposed a stage model that consists of (1) cataloging, (2) transaction, (3) vertical
integration and (4) horizontal integration. The ultimate goal of staged-development is to attain a
one-stop portal, from which citizens can access any government agency from a single location.
Moon (2002) introduced a five-level model that emphasized on the degree of technical
sophistication and interaction with users. According to Moon, e-government begins with the
posting of information online, followed by two-way communication via email systems and data-
transfer technologies. The next level involves implementing financial transactions along with
services such as license renewal and loan applications through live database links. Next the site
integrates public services vertically (intergovernmental integration) and horizontally (intra-
governmental integration) to attain and efficient, effective and responsive government. Finally,
the fifth level of e-government encourages online political participation through online voting,
online public forums, and online opinion surveys
Similarly, Hiller and Belanger (2001) presented a four-stage model of e-government that
involves (1) information, (2) two-way communication, (3) transaction, and (4) integration.
According to their model, e-government begins with disseminating information online and
ensuring that information is reliable, up-to-date, and accessible on the official Website. This is
then followed by the two-way communication stage involving public communication with the
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government through email exchanges and requests. Such communication then transforms into
complete transactions with “Web-based self-services completely replacing public servants” (p.
15). Finally, all government services are integrated onto a single portal through which citizens
can access any service from any level of government, which will be followed by the stage of
participation.
E-procurement emerges at more advanced stages of e-government implementation. Most
frequently, it occurs at the transaction stage. It is not clear, however, if e-procurement (or other
individual e-government features) progresses in a similar staged manner. That is, does a
government post information requests for proposals online prior to moving towards online
bidding or other more advanced components?
If e-government (broadly speaking) does move in a staged manner then one would assume
individual features similarly move in a staged manner - this research tests that assumption on e-
procurement. Admittedly, two studies (Reddick, 2004; Moon, 2005) have already tested this
assumption, but their models are not specified in a manner that account for staged progress.
Rather, their models identify factors that are associated with increased e-procurement adoption
regardless of whether the adoption has a specific order. Stages theory assumes that there is an
order to progress. Therefore, we look to their models to help identify predictors of adoption and
test those predictors in a manner that accounts for ordered progression. If we can determine if e-
procurement occurs in a staged manner and we can determine what factors lead to progression
through those stages we can suggest actions for public managers to take to increase e-
procurement adoption.
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Predictors of Adoption
E-government adoption is widely discussed in the literature and provides a strong foundation on
which to build a theoretical framework. Many factors affect e-government adoption including:
form of government (Carrizales, 2008; Moon, 2002), IT capacity (Carrizales, 2008; Norris &
Kraemer, 1996; Reddick, 2004; Schwester, 2009; Teo & Tan, 1998), budget size/fiscal
characteristics (Ho & Smith, 2001; Reddick, 2004; Schwester, 2009) and population (Schwester,
2009).
Moon (2002) found that council-manager forms of government are positively associated with e-
government adoption, similar to Carrizales’ (2008) exploration of e-government adoption among
New Jersey municipalities. Carrizales (2008) not only found a positive association between a
council-manager form of government and e-government adoption, but he also found a negative
correlation between e-government and a mayor-council form of government. Norris and Kraemer
(1996) identify this relationship with leading-edge information technologies at the municipal
level. Teo and Tan (1998) find that a separate IT department increases the growth of e-
government. Schwester (2009) finds that the number of full time IT employees is positively
associated with a higher e-government score. As the number of IT employees grows the e-
government score also increases. Therefore, IT capacity leads to more advanced use of
technology.
Ho and Smith (2001) identify budget as a factor in IT planning and implementation in their study
of Y2K readiness. Schwester (2009) also finds that an increase in budget increases a
municipality’s e-government score. Reddick (2004) looks at fiscal stress as a measure of e-
procurement implementation in state governments. He finds that the more “fiscally stressed”
state governments are more likely states are to adopt e-procurement. The last factor, related to
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budget and fiscal stress, is population. Population is consistently shown to increase e-government
adoption in almost all of the studies cited above (Moon, 2002; Schwester, 2009).
The above factors pertain largely to e-government as an across-the-board phenomenon.
However, little research has moved beyond this trend to examine the determinants of specific
aspects of e-government, such as e-procurement, especially at the municipal level. Reddick
(2004) examined state level e-procurement and found that both management capacity and IT
management capacity were correlated with state level e-procurement adoption. He uses scores
from the Government Performance Project, to operationalize his variables which cannot be
replicated on the local level. Similar to broader studies on e-government adoption Moon (2005)
finds that, “managerial innovation-orientation, increasing policy authority of the central
procurement office, and size are positively associated with the extensiveness of procurement
adoption” (p. 65).
In order to determine whether or not the factors discussed above affect municipal e-procurement
implementation, this study evaluates the websites of the 190 largest cities within the U.S. The
analysis takes form in two steps – first, we utilize a Mokken scale analysis to determine if there
is order in e-procurement progression; second, we fit the data in an ordered logit model to
determine what leads to e-procurement progress. We present two primary hypotheses to test
based upon the above theory.
H1. Cities with higher levels of IT capacity are more likely to be at advanced stages of e-
government implementation than cities with lower levels of IT capacity.
H2. Cities with a Council-Manager form of government are more likely to be at advanced stages
of e-government implementation than other forms of government.
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Data Collection and Research Methodology
The research examines the official websites of the 190 largest cities by population in the United
States. The cities were identified from U.S. Census data and among the 200 cities selected for the
survey, 195 cities had official websites. Five other cities were eliminated due to constraints on
data collection. The distribution of the cities among the 50 states is shown in Appendix A. The
200 largest cities offer a window into municipal level adoption. As shown in the policy diffusion
literature when larger cities adopt policies the neighboring communities are more likely to adopt
that policy (Shipan & Voldan, 2008). Hence, these cities are often leaders in implementation and
offer insight into future trends.
Dependent Variable
The research involves analyses of the official city websites using a 7-point e-procurement index,
shown in Appendix B. The data captured in the index are similar to data captured in the NASPO
survey reported by Reddick (2004). The index consists of a number of criterion evaluated based
on a dichotomous response of 0 or 1, based on the absence or presence of each feature
respectively. The criterion utilized account for each stage proposed in the staged model of
progression - (1) information, (2) two-way communication, (3) transaction, and (4) integration
(Hiller & Belanger, 2001;See Table 2)1. The dependent variable is then transformed to a 4 point
scale based upon having the individual features within each stage of e-government. A “1” is
given if the city had at least one criterion within the stage of e-government and a “0” is given if
no features are present.
1 Hiller and Belanger do not discuss e-‐procurement extensively rather they refer to “Government to Business” interactions and the associated staged progress. As our terms for e-‐procurement are more specific than their terms we try to align the models appropriately.
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Table 2 Stage of E-procurement E-government Stage E-procurement Feature Information • Post solicitations online
• Access requests for proposal online
Two-way communication
• Download requests for proposals
Transaction • Conducts online bidding • Place bids online
Integration • Digital signature laws • Procedures or statutes governing online bidding
We utilize Mokken scale analysis (MSA) on our data (Mokken, 1971) to examine if there is any
order to the e-procurement features captured. Developed primarily for item response theory
(IRT), this analysis allows us to account for an underlying order in a number of dichotomous
responses. For example, should one respond in the affirmative, that they can lift 180lbs we
would assume that the same person would respond in the affirmative that they can lift 170lbs.,
160lbs., etc. (van Schuur, 2011). In a similar manner, MSA allows us to analyze our set of e-
procurement features to determine if the theoretically more advanced stages of e-procurement are
more challenging to adopt. Furthermore – we can determine if a city that has adopted certain
advanced features such digital signature laws is more likely to have had adopted a more basic
feature such as posting proposals online. We utilize the Loevinger H Statistic based on Guttman
errors for each item on the scale along with providing a statistic for the entire scale. We discuss
the results of this analysis in the next section.
Independent Variables
IT Capacity and Form of Government are the two primary independent variables we test in this
model. IT capacity is operationalized as a dichotomous variable of 1 if a city has a separate IT
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Department and a Central Procurement Website. A score of “0” is given if no such department
and website exist or if it is embedded within a different department. For example, an IT
department may be part of a Personnel Office. Form of Government is operationalized as a
dichotomous variable as well. A score of “1” was given if the city had a Council-Manager form
of government and a“0” was given if otherwise. Additional control variables consist of socio-
economic variables collected from the U.S. Census Bureau data along with fiscal and
organizational data collected from the cities’ websites. These include population, fiscal stress,
and tax capacity. We utilize population rather than budget because a high correlation existed
between budget and population and we capture fiscal measures in the two other variables. Based
on previous research it is important that the study utilizes both a spending measure and a
population measure. Population and tax capacity are both taken from census data2. Tax capacity
is operationalized as the average price of an owner occupied home in the city3. Lastly, fiscal
stress was operationalized based upon a city’s credit rating by Moody’s rating agency4. To
determine the relationship between the dependent and the independent variables, an ordered logit
model was fitted with the data. Based upon the initial analysis of the dependent variable utilizing
MSA we were able to determine that there was a clear order to the index.
Analysis
The descriptive statistics of the dependent and independent variables tell an interesting story
about e-procurement implementation among the largest cities in the United States. The average
2 Population data is taken from the most recent city level census data available at the time of conducting the study – 2006. 3 According to Whiting (2000), “Tax capacity is a measure of the readily taxable resources (the tax base) in a given locale,”(p.220). While average housing price may not capture this entirely it provides a reasonable proxy for which data is available. A similar measure, equalized assessed value of residential property, is used by Hendrick as a component of tax capacity in her article Assessing and Measuring the Fiscal Heath of Local Governments : Focus on Chicago Suburban Municipalities. Average home price data is taken from the 2010 census. 4 Fiscal stress is measured in a number of different ways. Bond Rating is used by Nelson and Nollenberger, 2011 to measure fiscal health. It is also used by Chaney, Copley and Stone, 2002 as one indicator of fiscal stress.
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population of the cities in the sample is 384,000 people. The cities range in population from
107,000 to over 8 million residents, with 49% having a population less than 200,000 and 3% of
the cities having more than a million residents. About a third of the cities have populations
between 200,000 and 500,000 and 14% of the cities have populations ranging from 500,000 to a
million. Budget expenditures range from 39 million dollars to 9.6 billion dollars. The average
expenditures are about 885 million dollars.
Among the cities selected for the evaluation, 40% the cities were from the West, followed by
South (37%), Midwest (15%) and Northeast (8%). The average e-procurement score of all cities
was 3.46 with the median being 4.0. Some of the prominent large cities that ranked high on the
index were Philadelphia PA, Columbus OH, Seattle WA, Baltimore MD, Kansas City KS,
Arlington TX, Greensboro NC, Columbia SC and Lincoln NE (see Table 3)
Table 3 City E-Procurement Score Columbus, OH 7 Henderson, NV 7 Long Beach, NV 7 New Haven, CT 7 Raleigh, NC 7 Riverside, CA 7 Sterling Heights, MI 7 Arlington, TX 6 Baltimore, MD 6 Bridgeport, CT 6 Cincinnati, OH 6 Columbia, SC 6 Greensboro, NC 6 Kansas City, KS 6 Laredo, TX 6 Lincoln, NE 6 Madison, WI 6 Mesa, AZ 6 Pasadena, CA 6 Philadelphia, PA 6 Savannah, GA 6 Seattle, WA 6
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Tallahassee, FL 6 Los Angeles, CA 6
The percent distribution of e-procurement scores shows that most cities e-procurement score fall
within the range of 0-4 while very few cities reach a score of 5-7 (see Figure 1). This indicates
that most cities have not fully adopted a comprehensive e-procurement system. Rather, they
have adopted initial features of e-procurement.
Mokken Scale Analysis
The MSA helps us to determine the homogeneity, or scalability, of the response items in our
scale along with the scale in its entirety. Similar to other probabilistic tests, MSA compares
observed values to expecting values (based on Guttman errors) to produce both an overall H
statistic for the entire scale along with individual H statistics for each coefficient of
homogeneity. According to Mokken (1971), a test statistic of 0.3 is the lower boundary for
scalability and is considered a weak scale. A test statistic above 0.4 indicates a medium level of
scalability and above 0.6 indicates a strong level of scalability. Our overall scale indicates a
0 10%
1 5%
2 10% 3
20%
4 28% 5
14%
6 9%
7 4%
E-‐Prourement Score
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medium level of scalability and each of our coefficients of homogeneity indicate stronger levels
of scalability.
Essential to MSA, is the idea of Guttman errors. Guttman errors occur when an easier item on a
scale – that is an item in which answering in the affirmative occurs more frequently than others –
contains responses which do not follow normal pattern. For example, our scale consists of four
items – Stage 1 (Information), Stage 2 (Communication), Stage 3 (Transaction), and Stage 4
(Integration). Our first assumption is that Stage 1 is easier for cities to attain and therefore will
have more positive responses. This sequence should follow for the remainder of stages. This is
indicated by the mean score of the item in Table 4. Errors occur, for example, if one city answers
in the affirmative for stage 2, but in the negative for stage 1. This would contradict our
assumption.
The decrease in the mean score of the Mokken Scale suggests that there is an order to the
progression of e-procurement. Our overall H value is .40, suggesting that there is a medium
level of scalability. Each item on the scale has an H value above .4 and with some being closer
to .7, suggesting fewer errors (see Table 4)
Table 4 Mokken Scale Analysis Results Item Mean Score H Value Stage 1 Information .87 .53*** Stage 2 Communication .78 .68*** Stage 3 Transaction .57 .67*** Stage 4 Integration .27 .42*** Note: n = 190 *** Significant at the .001 level
Ordered Logistic Results
The results of the logistic regression indicate that both IT capacity and form of government are
statistically significant in advancing through e-procurement in a staged manner (see Table 5).
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For a unit increase in IT capacity the odds of having more developed e-procurement system
increase by a factor of 2.65.
Table 5 Ordered Logit Results E-Governance Stage Variable Coefficient SE Odds Ratio IT Capacity .975*** .288 2.65 Form of Government
.716** .286 2.05
Tax Capacity -.0000002 - .000002 1 Population -.00000006 .0000002 1 Fiscal Stress -.021 -.073 .98 Note: n = 191 ** Significant at the .05 level *** Significant at the .001; likelihood ratio x2 = 18.63; model is significant at the 0.01 level; McKelvey & Zavoina's R2 = 0.1
Similarly, the results of having a council-manager FOG suggest a positive direction. The odds of
having a more developed e-procurement system increase by a factor of 2.05 in a municipality
with a council-manager FOG. These results demonstrate that the probability of being at a more
advanced stage of e-government increases for those that have IT capacity or a council-manager
FOG. The probability that cities have either one of these components and are at beginning
stages of e-procurement implementation is less than 5% (see Table 6). It is more likely, if a city
has one of these components that they are at stage 3 or stage 4 in their e-procurement initiatives.
Table 6 Predicted Probability for Stage Progress None Stage 1 Stage 2 Stage 3 Stage 4 Affirmative Response
Prob. Prob. Prob. Prob. Prob.
IT Capacity .04 .05 .14 .22 .54 Council Manager FOG
.05 .06 .16 .23 .49
In cities that have both a council-manager FOG and IT capacity the probability that they are at a
more advanced stage of e-government dramatically increases (see Table 7). Cities in which both
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of these factors exist increase their probability of being at Stage 4 integration to .61.
Table 7 Probability of Stage 4 Integration Council-Manager FOG IT Capacity NO YES NO .23 .37 YES .43 .61
Both of these factors are in line with previous research that associates form of government and
IT capacity with adoption of e-government and innovation. Damanpour and Schnedier (2009)
find a significant negative relationship between the mayor variable and innovation, while
according to Krebs and Pelissero (2010), the stronger the mayor, the lesser the possibility of
innovation. Carrizales (2008) finds a negative relationship between e-government and a mayor-
council form of government among municipalities in New Jersey, while the link between
council-manager form of government and e-government adoption was positive. The presence of
an IT department will help local governments adopt and implement e-government successfully
(Norris and Kraemer, 1996). Ho and Smith (2001) find that the presence of an IT department
indicated Y2K readiness and Schwester (2009) finds that an increase in the number of IT
employees increases e-government adoption. Our results add to these previous studies in that
they demonstrate the presence of technology and a council-manager FOG increase the likelihood
of progressing through the development of an e-procurement system.
Along with adopting new technology, cities need to involve their purchasing employees in IT
training and development, to update their skills with current trends. Some of the common
significant features in e-government research, such as fiscal measures and population, are
insignificant in our model. While it is difficult to draw precise conclusions from such results this
might suggest a difference in adoption of e-procurement and progression through the stages of e-
21
procurement.
Further Discussion
We have selected five cities in the top 25 to discuss some of the standard approaches regarding
online procurement policies. These descriptive findings provide more insight into the
development of a comprehensive e-procurement system. The city of Seattle, WA initiated the e-
procurement process that allows potential bidders to email their bids through secure email
addresses. In turn, the city uses a secure mailbox to receive the bids, and those bids are
subsequently opened at a future specific time/date. Using a rolling acceptance approach, the city
still accepts paper bid applications (traditional process), since the goal for the city is to provide
bidders with more flexibility and enhance efficiency. It is Seattle’s hope that this will allow for
better competition and remove possible obstacles in the submission process (“City Purchasing
and Contracting Services,” 2013).
The city of New Haven, CT employed a new and innovative interactive purchasing website. The
goal of this unique site was to provide sufficient information to potential bidders that would
allow for the development of a collaborative relationship between the city and the bidders. On
the procurement web portal, the side menu provides links to the procurement policy, general
information, department contacts, FAQs and additional information. A key component of the site
is that potential bidders are able to register directly on the website, which allows them to be
apprised of current procurement opportunities through email, as well as read the submission
results online. These components are free to the potential bidders. The city website is clear to
acknowledge the contributions of the dedicated team of public and private employees that make
the site possible (“Bureau of Purchases: Online Procurement”, 2013).
22
Similar to New Haven, the city of Columbia, SC also has a team of dedicated professionals
(public and private) that took the archaic procurement process and transformed it into the
contemporary e-procurement program. Columbia’s primary goal is to clarify the procurement
process and create an effortless site, yet at the same time they hope to make access available to
potential bid opportunities. Moreover, the city strives to upgrade the level of competition by
maximizing the involvement of bidders and contractors who aim to supply services to the city
residents (“Columbia’s Purchasing Division”, 2013).
In the case of Savannah, GA, a new e-procurement system was developed to streamline the flow
of information between the municipal government, vendors, and resident users. The new system
facilitates the e-procurement process by primarily enabling bidders to register online, receive bid
notifications, and submit bids and view bid results online. The e-procurement website also offers
an online question/answer session and includes sealed bids with lock box and bid encryption for
safety and privacy concerns. The city places a great deal of emphasis on maintaining privacy and
safety through the procurement process, which they argue enhances the value of their resident’s
tax dollars (“E-Procurement”, 2013).
The city of Columbus, OH has established the Columbus Vendor Services, an e-government
initiative that provides a one-stop online portal for potential bidders and vendors through which
they can register and compete for the city bids. The website provides all relevant information on
the procurement process and helps conduct business in an open environment that will lead to
better public trust. Moreover, the online process results in numerous choices of suppliers, lower
process costs, improved quality and service delivery. The website is user friendly with complete
instructions listed throughout the entire process and enables vendors to be notified of current
23
openings, submit and view bids online, and keep track of the company's information such as
goods, services provided and payment invoices (“About Vendor Services”, 2013).
This narrative suggests that cities are working hard not only to gain efficiencies and save money
for tax payers, but develop partnerships with vendors that establish clear protocols in a user-
friendly manner. Similar to the findings that high performing private sector firms increase
performance for both buyers and suppliers (Vaidyanathan and Devaraj, 2008); it seems high
performing public organizations seek to accomplish a similar goal.
Implications and Limitations
Our study identifies two areas that are essential for the growth of e-procurement: professional
city management (as measured by FOG) and IT capacity. As regards the first, despite evidence
that U.S. municipalities are becoming structurally more similar (Nelson & Svara, 2010), our
research suggests that government structure is still important to innovation, especially in terms of
technological advancement. Also significant, is the presence of city managers, who usually have
a Master’s in Public Administration and tend to be more professionally qualified and receptive to
the use of innovative technologies. Nonetheless, as indicated by our finding that cities with
greater IT capacity are significantly further along in the e-procurement process, IT capacity also
plays an important role in the successful adoption of technology. The finding also implies that
simply adopting an e-procurement system does not guarantee its development to full potential.
Rather, cities need the internal capacity to manage and advance such systems in a stable manner,
a capacity typically provided by IT departments. At the same time, the effective management of
IT capacity is only possible with innovative leadership and support from top management.
Accordingly, as our results indicate, cities that have both IT capacity and a council-manager
FOG are significantly more likely to be well advanced in their e-procurement development.
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Nonetheless, according to the most recent ICMA survey, even though 59% of cities operate
under a council-manager FOG, 33% are governed by a mayor-council FOG. Based on our
findings, these figures suggest that there would be an enormous potential for the growth of e-
procurement if mayors were to take leadership roles in its implementation. Admittedly, mayors,
like many other elected officials, may perceive rapid e-government innovations rather
negatively, considering them part of a bureaucratic tendency to avoid legislative scrutiny by
“technicalizing” (Berman & Wang, 2000; Kettl, 1994). Such concerns must be overcome,
however, if cutting edge technologies are to be introduced into governance.
Our research findings also provide support for the stages theory of e-government. For example,
our MSA analysis demonstrates that cities, in their adoption of e-procurement, are following a
progression through the information, two-way communication, transaction, and integration
stages, scoring highest on the first and then progressively lower on the latter three. Hence, unlike
earlier studies that dismissed stages theory based on overall observation of e-government, our
examination of the individual e-government feature of e-procurement provides clear evidence of
staged development.
The stages theory of e-government also proposes that e-procurement appears at later stages of
development (e.g., the transaction stage in our model). In doing so, however, it assumes that
simply because a government uses e-procurement, it is engaged in transactions with citizens or
vendors. Our research, in contrast, indicates that the e-procurement feature tends to be more
prevalent in the information or cataloging stage than in the transaction stage. For instance,
among the cities evaluated, posting RFPs in HTML format is the most common aspect of e-
procurement adopted, while only 6% of the cities have laws or statutes governing digital
signatures. Moreover, because many e-government features span multiple phases, they are
25
difficult to distinguish and categorize into the various phases of cataloging, transaction,
participation, and so on. Hence, this paper does not dismiss stages theory but rather suggests
scholars need to accurately examine individual features like e-procurement in terms of staged
development (e.g., progression through the information, communication, transaction, and
integration phases).
With all studies, this study has its limitations, not least its reliance on an online analysis that can
only collect the information available on a city’s website. Such analysis, however, has long been
a staple in research on the status and performance of e-government implementation because
government websites are important interfaces between government and citizens (Holzer,
Manoharan, & Van Ryzin, 2010) and e-government functions rely heavily on web-based
provision of government information and services (Bauer & Scharl, 2000; Huang, 2007).
Nonetheless, the fact that such a focus reflects only the supply-side perspective suggests a need
for future research to address the demand-side viewpoint analyzing data (e.g., user surveys) that
capture citizens and businesses’ trust in and satisfaction with the e-government functions
available. Given the adoption by many local governments of social media and Web 2.0
technologies, future research might also move beyond website assessment to incorporate
measures related to government use of mobile technologies and social media for e-procurement.
Overall, this study provides useful new evidence on city-level e-procurement implementation in
the U.S., demonstrating that although a majority of the 200 most highly populated U.S. cities
have incorporated e-procurement into their official websites, many are still in the experimental
stages. In particular, our MSA results indicate that, from the perspective of stage theory, cities
are facing hurdles in moving toward the transaction and integration stages. Therefore, as
municipalities’ dependence on procurement increases, the use of information technology could
26
reduce barriers to both adoption and development while ensuring a more efficient and
transparent procurement process. Our findings also suggest that, although e-procurement offers a
valuable opportunity to strengthen government performance and accountability by streamlining
procurement and increasing transparency and accessibility, the development of an advanced e-
procurement system is gradual (not instantaneous) and heavily reliant on management having the
foresight to recognize its potential benefits.
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S. No State No. of Cities 1 Alabama 4 2 Alaska 1 3 Arizona 9 4 Arkansas 1 5 California 42 6 Colorado 5 7 Connecticut 2 8 District of Columbia 1 9 Florida 12 10 Georgia 5 11 Hawaii 1 12 Idaho 1 13 Illinois 5 14 Indiana 3 15 Iowa 1 16 Kansas 3 17 Kentucky 3 18 Louisiana 4 19 Maryland 1 20 Massachusetts 3 21 Michigan 4 22 Minnesota 2 23 Mississippi 1 24 Missouri 3 25 Nebraska 2 26 Nevada 7 27 New Jersey 3 28 New Mexico 1 29 New York 5 30 North Carolina 7
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31 Ohio 6 32 Oklahoma 2 33 Oregon 3 34 Pennsylvania 2 35 Rhode Island 1 36 South Carolina 1 37 South Dakota 1 38 Tennessee 6 39 Texas 20 40 Utah 2 41 Virginia 8 42 Washington 4 43 Wisconsin 2 Total 200
Appendix B
Website Evaluation Index % of Cities
1 Does the website allow potential bidders to access RFPs (requests for proposals) and status of procurement online in html format? 73
2 Does the website allow potential bidders to download RFPs (.doc or .pdf)? 70 3 Does the website allow potential bidders to place bids online? 22 4 Has the city enacted digital signature laws? 6 5 Does the central procurement office post solicitations on the Web? 82 6 Does the city central procurement office conduct bids via the Internet? 54
7 Does the city central procurement office develop procedures or have statutes governing Internet bidding? 20