8 october 2015 preliminary results from a survey of state dot website infrastructure a.j. million,...

8 October 2015

Preliminary Results from a Survey of State DOT Website InfrastructureA.J. Million, Ph.D. candidate, School of Information Science & Learning Technologies, University of Missouri

A.J. Million, Doctoral CandidateUniversity of Missouri

School of Information Science & Learning Technologies

October 8, 2015

Preliminary Results from a Survey of State DOT Website Infrastructure

Organizations

Traditionally arranged by bureaucratic hierarchy

Government agenciesDebate about centralization and decentralization of service

provisionTied to politics

Organization can influence service quality

Studied in public administrationStudied by socio-technical researchers

E-government services are not typically examined in this way

Cloud Computing

Refers to a formal computational modelFive essential characteristics

Three service models Four deployment models

More than Google docsDescriptive parts also apply to many online

networksUnderlying concept not new

Rooted in networking of computer systems

Definition

Essential Cloud Computing characteristics are

On-demand serviceBroad network access

Resource poolingRapid elasticity

Measured service

Not all apply to every government websiteMost exhibit multiple traits

Definition Cont.

Cloud service models includeSoftware as a ServicePlatform as a Service

Infrastructure as a Service

Similar to contracting out, but granularVery flexible

May not see if something is Cloud-basedDecentralizes service provision from single

provider

Definition Cont.

Deployment models includePrivate

CommunityHybridPublic

Describes non-Cloud systems tooRefers to where systems are physically

deployed

Why It Matters

Cloud-based approaches tied to broadbandHigh-speed Internet 70%+

Likely to become more so in the futureDecouples government staff from online services they provide

Same can happen with other approaches

Research tends to focus on usability or security

Challenges and benefits of using virtual assetsManagement needed

Potential for greatly increased complexity and problems

Socio-technical Research

Overlap between organizations and technical systems

Think of roads like technologyRequires design and standards

Have to plan for users and people or they won’t work wellInvestment

Managers are responsible for implementation

Study the overlap between spheres

Technology

Social Factors

Possible Benefits

For public administratorsIdentify causes of Web infrastructure adoption to make more

purposefulRemove barriers to best practices

See how innovations spreadUse all of these lessons to build better websites

Value for most transportation librariansOften lack a say

Designed mostly for public communicationLeverage to shape infrastructure in way that helps librarians

Methodology

Online survey sent to three groupsAASHTO Subcommittee on Information Systems

SLA Transportation DivisionTRB Information Services Committee

Collected summer of 2015Used definitions from literature to frame

questionsFollow-up interviews nearly done

Summary of Demographics

Generalizable threshold (N=45) for responsesMore individuals (61) than states

Mean (13.9) is over decade of experience

Mid-sized (4,062) agencies mean by FTESmall (4.2) mean for Web staff

Average site built in 2010Mid-sized (6,366,683) state mean by total

populationMean ($107,116) lane mile spending

13

34

14

Job Titles by Classifi-cation

Execuitive ManagementStaff

0k < 1k 2k 3k 4k 5k 6k 7k 8k +0

1

2

3

4

5

6

7

8

9

10

Agency FTE Counts in Thousands

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

1

2

3

4

5

6

7

8

9

Sites Built by Year

0

1

2

3

4

5

6

7

8

State Population Counts in Millions0m <1m2m3m4m5m6m7m8m9m10m11m12m13m +

Summary of Infrastructure

Most (n=28) were built in-house Only two known states host their website in

the CloudOn-demand self-service (n=10) is rare

Vast majority reported having (n=40) broad network accessMany (n=23) pool resources

Most (n=23) infrastructure was not rapidly elasticMajority (n=33) measured

Most DOTs take a centralized approachState IT often involved

Summary of Infrastructure

Most infrastructure deployed (n=32) privatelyEven more administer infrastructure (n=37)

in-houseContent usually (n=42) in-house

Combanation In-House0

5

10

15

20

25

30

Site Built Location

Don't Know No Yes0

5

10

15

20

25

30

35

On-Demand Self-Service

Don't Know No Yes0

5

10

15

20

25

30

35

40

45

Broad Network Access

Don't Know No Yes0

5

10

15

20

25

Pooled Resources

Don't Know No Yes0

5

10

15

20

25

Rapid Elasticity

Don't Know No Yes0

5

10

15

20

25

30

35

Measured Service

0

5

10

15

20

25

30

35

Infrastructure Deployment

Don't Know Community Hybrid Private

Contracted-Out In-House Other0

5

10

15

20

25

30

35

40

Infrastructure Administration

In-House Other0

5

10

15

20

25

30

35

40

45

Content Administration

Statistical TestingRank

Trait X2 N Sig. Notes

1 Content Administration 73.20 45 Yes More in-house. No contracted-out.

2 Deployment 68.35 42 Yes Far more private. No public.

3 Broad Network Access 62.80 45 Yes Far more yes.

4Infrastructure Administration

48.53 45 Yes More in-house.

5 Measured Service 34.68 44 Yes Many more yes.

6 How Built 27.46 43 Yes More in-house. No contracted-out.

7 On-Demand Service 23.33 45 Yes Many more no.

8 Pooled Resources 9.73 45 Yes More yes. Many more don’t know.

9 Rapid Elasticity 7.14 44 Yes More no.

Implications for Librarians

Most DOTs are still managing Web systems in-house

Content management is localResource pooling with other agencies very common

Bureaucratic organization commonLimited staff

Designed to control and limit operationsSubject to authority

Software appears to be compartmentalized

Conclusions

Sites redesigned on average every five years

Uncertainty existsJoin Web committees

Encourage site redesigns to help the libraryMake friends with IT staff

Will follow-up with interviews over next month

Will examine barriers to best practices

A.J. Million, Doctoral CandidateUniversity of Missouri

School of Information Science & Learning Technologies

E-Mail: ajmillion@gmail.com

Thank You!