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.      

16    

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

20    

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).

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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

30    

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