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Modelling the Smart Governance Performance to Support Smart City Program in Indonesia
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Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science 00 (2019) 000–000
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ScienceDirect Procedia Computer Science 161 (2019) 367–377
The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019
Modelling the Smart Governance Performance to Support Smart Modelling the Smart Governance Performance to Support Smart City Program in Indonesia City Program in Indonesia Anisah Herdiyanti*, Palupi Sekar Hapsari, Tony Dwi Susanto Anisah Herdiyanti*, Palupi Sekar Hapsari, Tony Dwi Susanto
Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Abstract Abstract Every local government in Indonesia is in a race to become the leading innovator of smart city initiatives. The ambitious urban Every local government in Indonesia is in a City” race towas become the leading of smartof city initiatives. Theand ambitious urban digitization program: “Gerakan 100 Smart initiated in 2017innovator by the Ministry Communication Information, digitization “Gerakan 100Affairs, Smart the City” was initiated in 2017 by the of Ministry of Communication supported byprogram: the Ministry of Public Ministry of Finance, the Ministry Public Works and Housing,and and Information, the Ministry supported the Ministry Planning. of Public Affairs, thethe Ministry of Finance,program the Ministry of Public Works promising, and Housing, the Ministry of NationalbyDevelopment Although urban digitization of smart city sounds theand implementation of Nationalsince Development Planning. Although the urban digitization program of smart citygovernment sounds promising, the implementation is lacking there is neither specific national standard nor guidance to assist local undertaking the smart city is lacking since is to neither specific national dimension standard nor assist local government undertaking smart city initiatives. With there respect the smart governance thatguidance serves astoan important foundation for deliveringthe governmentinitiatives. With there respectis to the smart governance that serves as an important foundation delivering related services, limited indicators and thedimension related formula to measure its performance. Thisforresearch fillsgovernmentthe gaps by related services, there is formula limited in indicators the related formulatotosupport measure performance. ThisThe research fills gaps by modelling indicators and the smartand governance dimension theits smart city program. research wasthe conducted modelling indicators andgovernance formula in the smart governance to support the smart city program. The research was conducted by identifying the smart purposes, mapping thedimension current related governance indicators, and developing a model for smart by identifyingperformance. the smart governance purposes, mapping the current related governance indicators, ande-government developing a model for smart governance The model was then validated by three experts of smart city and evaluation and governance The asmodel then threeresults experts of indicators smart cityinand evaluation and implementedperformance. in Surabaya City a casewas study. Thevalidated proposedbymodel in 29 threee-government different domains and seven implemented in Surabaya as a can caseserve study.asThe proposedfor model in 29 indicators in three different and seven aspects of assessment. TheCity model a reference smartresults governance performance evaluation to domains support smart city aspects of in assessment. initiatives Indonesia. The model can serve as a reference for smart governance performance evaluation to support smart city initiatives in Indonesia. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee The Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Smart City; Smart Governance; Evaluation; Performance Keywords: Smart City; Smart Governance; Evaluation; Performance
* Corresponding author. Tel.: +62-31-599-9944; fax: +62-31-596-4965. address:author. [email protected] * E-mail Corresponding Tel.: +62-31-599-9944; fax: +62-31-596-4965. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.135
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1. Introduction The concept of Smart City has been gaining momentum in Indonesia since the launching of “Gerakan 100 Smart City” or “Movement to 100 Smart City” program by the Ministry of Communication and Information Republic Indonesia in 2017 [1]. The program features a competition and selection process among cities and regions. The selection process invites cities and regions that are ready to undergo an assessment process in Jakarta to measure readiness to pursue each stage of the program. Candidate cities and regions that qualify will receive assistance and mentoring to prepare a Smart City Development Plan on 5 to 10-year horizon. Twenty-five cities/regions were selected in 2017 for the first phase, fifty cities/regions were selected in 2018 for the second phase, and the rest twenty-five cities/regions were selected in 2019 for the last phase [2]. The ongoing urban digitization program aims at establishing Smart City Development Plan of the selected 100 cities/regions in Indonesia. The plan includes three goals: 1) Smart Connectivity (Infrastructure); 2) Smart Solutions (Environment, Governance, Citizen, Security, Education, Transport, Healthcare); and 3) Smart Users (Community). A dedicated smart city strategy is included in the plan explicitly like other 5 (five) countries in Asia as reported by OECD in 2019 [3] – namely China, India, Malaysia, Singapore, and Thailand. This concludes high interest of government in Indonesia in pushing for smart city initiatives that triggers private sector’s take on smart city by conducting surveys [4], seminars [5], or corporate social responsibility programs [6] as well as academic sector’s interest in contributing to the program by growing a community [7], a research group [8], or a knowledge management forum [9]. Although the urban digitization program of smart city sounds promising, the implementation is lacking since there is neither specific national standard nor guidance to assist local government undertaking the smart city initiatives [10,11]. The initiatives shall be integrated within the cities’ or regions’ agenda – namely, Local Government Mediumterm Development Plan which enlists programs/projects to support the cities’ or regions’ objectives within 5 (five) years. Following this, when developing a masterplan of smart city, a roadmap showing how programs/projects related to smart city support the cities’ or regions’ objectives should be highlighted. Furthermore, when dealing with monitoring and evaluating the smart city initiatives, there is lacking performance measurement standard that can serve on a basis of improvement for the initiatives. This study aims at modelling the smart city performance. Among the six dimensions of smart city, the dimension of smart governance is chosen since it subjects to various existing standard of e-government performance measurements in Indonesia, e.g. ‘Sistem Pemerintahan Berbasis Elektronik (SPBE)’ e-government evaluation, ICT Pura evaluation, and ‘Pemeringkatan e-Government Indonesia (PeGI) e-government ranking evaluation. The smart governance dimension is also considered important from previous scholars since it enables smart city efficiencies and engagement [12,13,14]. This study sheds the light in the model of smart governance performance to support the “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. The model will be developed based on the definitions of the smart governance indicators provided in the program, that mapped to the existing standard of egovernment performance evaluation – namely SPBE e-government evaluation, ICT Pura evaluation, and PeGi egovernment ranking. The rest of this paper will discuss about the related works, research methods, results and discussion, and future works. 2. Related works This section will discuss about 1) the smart city concepts, 2) the smart city dimensions, and 3) the related governance measurement standard that has been implemented to assess e-government initiatives in Indonesia. The discussion aims at providing the basic concept, the current practice, and the existing assessment matrices related to egovernment implementation in Indonesia. 2.1. Smart city concepts Smart City is a growing concept ranging from eco-cities [15–17] to various Information and Communication Technology (ICT) attributes in a city [18]. Although different modelling approach to compare smart cities have been
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identified [19], a common definition of what a smart city is, has not yet been stated. However, some key areas from smart cities fundamental theories can be identified: A smart city was viewed with 4 (four) disciplinary perspective: ICT, urban planning and growth, living labs, and creative industry [20]. A smart city was drawn from 8 (eight) critical factors: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment [21]. A smart city was characterized into several key elements: Cohen Smart City Wheel within which 6 (six) dimensions were defined - smart governance, smart living, smart mobility, smart people, smart economy, smart environment [22] Smart City Framework Wheel with People, Place and Planet as core values and 6 (six) dimensions - smart governance, smart living, smart mobility, smart economy, smart environment, smart infrastructure [23] A smart city definition can be classified into 2 (two) parts [24], and therefore the smart city shall be redefined with a resilience approach. One that emphasizing in “hardware” side, i.e. (1) ICT and modern technology; (2) the utilization of advanced technology products to make living in a big city much more convenient; (3) the utilization of ICT and technology also constitutes an effort to improve the quality of life and prevent the degradation of environmental quality One that emphasizing in “software” side, i.e. the role of ICT and technology in actualizing the welfare, effectiveness, and competitiveness of its residents From the various concept of the “Smart City”, it can be concluded that smart city emerges to achieve more efficient and sustainable cities. The smart city notion has evolved from the execution of specific projects to the implementation of global strategies to tackle wider city challenges at different level, i.e. local, national, regional, international (see [25] for environmental smart city project actions and challenges). While considering smart city as a project, its main objectives must be to solve urban problem in an efficient way to improve sustainability of the city and quality life of its inhabitants. This study will take on the smart city concept introduced by Cohen [22] since this concept is adopted by the Ministry of Communication and Information Republic Indonesia when initiated “Gerakan 100 Smart City” or “Movement to 100 Smart City” program [26]. 2.2. Smart city dimensions There has been a growing debate as how to define and structure the smart city dimensions. This section focuses on the discussion of the smart city dimensions adopted by the “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. The program highlights six dimensions adapted from Cohen [22] – they are: smart governance, smart branding, smart economy, smart living, smart society, and smart environment as seen in Fig. 1(a). These dimensions are represented in a Smart City Wheel [27], on which the dimension of ‘smart people’ is adapted to ‘smart society’ as seen in Fig. 1(b). Each of the dimension defines 3 (three) indicators that address its key definitions. This study focuses the discussion on the smart governance dimension considering two factors: 1) its role to enable smart city efficiencies and engagement [13]; and 2) various existing standard on e-government performance in Indonesia. The smart governance dimension focuses on the effectiveness of government in delivering public services [12], and represents an open governance that can enable public participations and be adaptive to technology [28]. According to a guideline for developing a smart city masterplan published by the Ministry of Communication and Information Republic Indonesia [29], smart governance represents good governance of cities/regions that is effective, efficient, communicative, and continuously improved through an innovation and integrated technology adoption. Meanwhile, previous studies on modelling the smart city performance are limited. Lombardi et al. [28] contributes to defining smart city components connecting the cornerstones of the triple helix. Smart governance dimension was conceptualized within the 7 (seven) quantitative indicators: number of universities and research centers in the city, the proportion number of courses entirely downloadable from the internet, e-Government on-line availability, percentage of households with computers, percentage of households with Internet access at home, e-Government usage by individuals, and number of research grants funded by companies, foundations, or institutes. Meanwhile,
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Cohen [27] defines 3 (three) qualitative indicators: enabling supply and demand side policy, transparency and open data, and ICT & e-gov.
(a)
(b)
Fig. 1. (a) Smart City Wheel by Cohen [27]; (b) Smart City Dimensions in “Movement to 100 Smart City” program .
This study takes on the perspective of the Ministry of Communication and Information Republic Indonesia who conceptualized the smart governance dimension within 3 (three) qualitative indicators (See Fig. 1(b)), i.e. ‘(public) services’, ‘bureaucracy’, and ‘(public) policy’. The ‘public services’ indicator is represented by public administration services, basic needs’ facilities, and utility services’ facilities; while the ‘bureaucracy’ indicator is represented by bureaucratic governance that focuses on fairness, accountability, and transparency. The ‘public policy’ indicator is shown by public policy that takes on the perspective of positive impacts to society and therefore accommodates public aspirations. The study will conceptualize model of quantitative indicators based on the qualitative indicators proposed by the Ministry of Communication and Information Republic Indonesia. The model can be used as a reference to measure the performance of smart governance in cities/regions undertaking the “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. 2.3. Existing e-government measurement standard in Indonesia This subsection discuses briefly about the current practice of e-government performance evaluation in Indonesia. The recent evaluation standard was introduced by the Ministry of Administrative and Bureaucratic Reform Republic Indonesia in 2018 [30]. The e-government evaluation namely ‘SPBE Evaluation’ aims at measuring the e-government initiatives progress, provide recommendations for future improvements, and ensure e-government quality in practice [31]. The ‘SPBE Evaluation’ is represented as a maturity level index that covers 3 (three) domains, 7 (seven) aspects, and 35 indicators. The maturity level in the ‘internal policy’ domain, and ‘governance’ domain represents the process capability maturity level; while the maturity level in the ‘service’ domain shows functional capability maturity level. A former evaluation standard introduced by the Ministry of Communication and Information Republic Indonesia is ICT Pura evaluation, and PeGI e-government ranking. The ICT Pura Evaluation is basically a program aims at mapping, assessing, and awarding cities/regions regarding the use of information technology and communication (ICT) [32]. There are 5 (five) dimensions when assessing ICT use in a city/region – namely Needs and Alignment, Suprastructure, Infrastructure, Community and Society, and Outcomes. The assessment results in a readiness index that will be used as a reference to grant the ‘ICT Pura Award’ to cities/regions. Meanwhile, the PeGi e-government ranking aims at assessing the readiness of e-government implementation [33]. The readiness covers 5 (five) dimensions: policy, structure, infrastructure, application, and planning. The assessment produces an index that can underline which dimensions should be improved on.
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3. Related method This study comprises of 3 (three) stages as depicted in Fig. 2. The stages are: a) preparation stage; b) model development stage; and c) validation stage. Preparation Stage
Model Development Stage
Validation Stage
Fig. 2. Three stages of the research.
The Preparation Stage consists of two main activities – they are performing desk review and mapping indicators. This stage begins with conducting desk-review on the dimensions of 4 (four) e-government evaluation standard: 1) the smart governance dimension based on the guideline of “Movement to 100 Smart City” program; 2) SPBE Evaluation dimensions; 3) the ICT Pura dimensions; and 4) PeGi dimensions. The desk-review aims at producing a list of indicators for each of the dimensions in the e-government evaluation standard. Taking the perspective of the smart governance dimension of the “Movement to 100 Smart City” program, the study continues to identify the mapping between its indicators and the indicators from SPBE dimensions, ICT Pura dimensions, and PeGi dimensions. The result of this stage is mapping of indicators. The Model Development Stage is mainly about constructing the smart governance performance model. The model is adapted from the current structure of smart governance in the “Movement to 100 Smart City” program and expanded with list of quantitative indicators. The result of this stage is the proposed model. The Validation Stage shows the model validation. This study uses expert judgement and a case study for model validation. The expert judgements involve 3 (three) experts of smart city and e-government evaluation from academicians to validate the structure and contents of the model. After performing the structural validation, a case study of Surabaya city is chosen for model implementation. A secondary data is collected, and then is assessed. The result of the smart governance performance evaluation is presented at the end of this stage. Limitations and future works are also discussed to identify the possibilities for the model for future improvements. 4. Result and discussions This section presents the results of the study. The discussion will be categorized according to the research method – i.e. preparation stage, model development stage, and validation stage. 4.1. Preparation stage The study found that statistics of the 4 (four) e-government evaluation standard as seen in Table 1. Each of the standard defines domains and sub domains/aspects within which several indicators are defined. Table 1. Indicators of the e-government evaluation standard. Name of Evaluation Standard
Number of domains
Number of indicators
Type of indicators
Smart governance (“Movement to 100 Smart City” program)
3
3
Qualitative
SPBE evaluation
3
35
Quantitative
ICT Pura
3
100
Quantitative
PeGI
5
24
Quantitative
Table 1 also shows types of indicators in each standard. Apart from the smart governance of the “Movement to 100 Smart City” program, all standards introduce a quantitative-type indicator. This is shown by an index used at the end
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of the assessment. The smart governance is not yet presenting the quantitative indicators and therefore, this study aims at filling this gap. After conducting a desk review, we mapped the quantitative-type indicators to the qualitative indicator. A list of indicators was categorized in 3 (three) domains – namely Domain 1 Public Services, Domain 2 Bureaucracy, and Domain 3 Public Policy. The first domain comprises of 3 (three) aspects with total 7 (seven) indicators; while the second domain consists of 1 (one) aspects with total 13 indicators. The third domain includes 2 (two) aspects with total 5 indicators. All indicators in SPBE evaluation were mapped, while only 56 indicators of ICT Pura can be mapped. The rest of ICT Pura indicators that were not mapped (44 indicators), are related to other smart city dimensions, e.g. smart society, smart economy, and smart branding, and ICT infrastructure related indicators. The smart governance dimension does not explicitly state the needs of ICT infrastructure related indicators. Similar case happens to 4 (four) PeGI indicators that relate to infrastructure. Therefore, only 20 indicators of PeGI can be mapped. 4.2. Model development stage An initial model was constructed at this stage. The model comprises of 3 (three) domains, 6 (six) aspects, and 25 indicators. Indicator elements were also added including formula and source of data to fill the formula. Each indicator also was equipped with a scale to quantitively measure smart governance performance. For instance, “Indicator 1 Percentage of online public services (e-government)” is defined by number of online public services divided by the total of online public services – then convert this portion into a percentage. To fill this formula, several data are needed, e.g. list of public services, status of public services (online or manual). 4.3. Validation stage A purposive sampling technique was implemented to validate initial model. Expert judgement method was employed by conducting interviews with 3 (three) experts with two qualifications: a) knowledgeable in e-government evaluation; and b) knowledgeable in smart city trends in Indonesia. A feedback sheet was developed aiming at validating the proposed domain and the related indicators and validating the formula. General feedback regarding the model were also discussed to improve the overall model. During April to May, three experts were interviewed using the feedback sheet – they are a) Mr. Khakim Ghozali (lecturer, ICT Pura surveyor, e-Government Award committee); b) Mr. Lukito Edi Nugroho (lecturer, triple-helix consultant, smart city initiator); c) Mr. Ferry Astika Saputra.(lecturer, SPBE evaluation committee, external evaluator of SPBE). The validation resulted in 5 (five) main points: i) sequence order of indicators; ii) additional 5 new indicators, i.e.: availability of basic needs’ monitoring, percentage of clean water, availability of clean water monitoring service, availability of city operation center, availability of websites that accommodate public aspirations in validating the draft of regulatory district; iii) reducing 1 indicator, i.e. percentage of regulatory product completed within a year; iv) revising and adding new aspects, i.e. Aspect 4 Internal Policy, Aspect 5 Bureaucratic Governance; and v) smartness categorization (lacking, sufficient, good, very good, and satisfactory). The initial model is then updated. There are 3 (three) domains in the proposed model – namely Domain 1 Public Services, Domain 2 Bureaucracy, and Domain 3 Public Policy. The first domain comprises of 3 (three) aspects with total 10 indicators; while the second domain consists of two aspects with total 14 indicators. The third domain includes 2 (two) aspects with total 5 indicators. The total number of indicators is 29 indicators in the proposed model. A complete list of the proposed model along with its formula and source of data, is presented in Annex A. The proposed model was implemented in a case study of Surabaya city using a secondary data of 2019 ICT monitoring and evaluation in the district. The highest score reached by Domain 1 with 78% score (39 out of 50); while the lowest score reached by Domain 2 with 55,71% score (39 out of 70). Domain 3 scored 60% (15 out of 25). The total score for Surabaya City is 64,14% score (93 out of 145) that represents very good smartness category. 5. Conclusion and future works The study proposed a model to measure smart governance measure to support “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. The proposed model introduced 29 indicators within 3 (three) domains and
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6 (six) aspects. The indicators within the model was very dependent to the existing e-government evaluation standards since it was formulated by mapping the standards. This study contributes to defining quantitative indicators for the smart governance. The model can serve as a reference for policy makers in defining city smartness within smart governance domain. Future works shall consider multi-actor perspective of defining indicators taking perspective of government i.e. the Ministry of Communication and Information Republic Indonesia, industries, and academicians from different backgrounds. It shall also aim at developing a performance model for all smart city dimensions. Acknowledgements The work presented in this paper is supported by a research grant from Institute of Research and Public Services, Institut Teknologi Sepuluh Nopember. The authors also thanks Mr. Khakim Ghozali, Mr. Lukito Edi Nugroho, and Mr. Ferry Astika Saputra for the support provided during the validation stage of the research. Appendix A. smart governance performance model Table A.1 shows the domains, aspects, and indicators of the proposed Smart Governance Performance Model. Table A.1 The proposed Smart Governance Performance Model. Domain
Aspects Aspect 1 Public Administration Services
Aspect 2 Basic Needs’ Facilities
Domain 1 Public Services
Aspect 3 Utility Services’ Facilities
Source of Data
References
Indicator 1 Percentage of online public services (egovernment)
Indicators
[(Number of online public services) / (Number of public services)] x 100%
Formula
List of public services; Status of public services (online or manual)
Lombardi – 6 [28]
Indicator 2 Percentage of complete SOP for public services
[(Number of SOP for public services) / (Number of public services)] x 100%
List of public services; List of SOP for public services
Adapted from PeGI – 19 [33]
Indicator 3 Food Security Index
[(Index of food availability per capita + Index of food quality and safety + Index of availability of market food price) / 3] x 100%
KPI of Department of Food Security, Ministry of Agriculture
Department of Food Security, Ministry of Agriculture [34]
Indicator 4 Availability of basic needs’ monitoring
Index scale (0-5)
List of systems that monitor basic needs’
During Validation Stage
Indicator 5 Percentage of clean water
[(Number of households with clean water) / (Number of household)] x 100%
Percentage of clean water
During Validation Stage
Indicator 6 Availability of clean water monitoring
Index scale (0-5)
List of systems that monitor clean water monitoring
During Validation Stage
Indicator 7 Electrification Ratio
[(Number of Household with electricity) / (number of total households) x 100%
KPI of Ministry of Energy and Mineral Resources
ICT Pura – 8 [32]
Indicator 8 Percentage of Telephone Infrastructure Coverage
[(Coverage area) / (district land area)) x 100%
Tower maps in the area; Land area (km)
Adapted from ICT Pura – 9 [32]
Indicator 9 Availability of Internet Service Provider (ISP) in the district
Index scale (0-5)
List of ISP in the district
ICT Pura – 9 [32]
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Domain
Domain 2 Bureaucracy
Aspects
Aspect 4 Internal Policy
Formula
Source of Data
References
Indicator 10 Percentage of Household Connected to Internet
Indicators
[(Number of household connected to internet) / (Number of households)] x 100%
Number of households; number of households connected to internet
Lombardi – 5 [28]
Indicator 11 Availability of smart city goals (vision and mission)
Index scale (0-5)
Document of Smart City Masterplan (Vision and Mission Statement)
Adapted from PeGI – 1 [33]
Indicator 12 Availability of internal policy related to:
[(Number of internal policy category that is available) / 10] x 100%
List of internal policy
SPBE – 8 to 12 [31]
Indicator 13 Availability of e-government masterplan
Index scale (0-5)
Document of Egovernment Masterplan (or ICT Masterplan)
SPBE – 3 [31]
Indicator 14 Availability of smart city masterplan
Index scale (0-5)
Document of Smart City Masterplan
Adapted from SPBE – 3 [31]
Indicator 15 Availability of smart city goals (vision, mission)
Index scale (0-5)
Document of Smart City Masterplan
Adapted from PeGI – 1 [33]
Indicator 16 Availability of smart city action plan
Index scale (0-5)
Document of Smart City Masterplan
Adapted from PeGI – 2 [33]
Indicator 17 Availability of smart city board
Index scale (0-5)
Document of Smart City Masterplan; Mayor Decree
Adapted from PeGI – 7 [33]
Indicator 18 Availability of job descriptions for each unit
Index scale (0-5)
Mayor Decree
PeGI – 8 [33]
Indicator 19 Percentage of ICT budgets
[ Ʃ (ICT Budgets) / (Unit Budgets) x 100%) / (Number of unit)]
List of ICT budgets; List of budgets in each unit
SPBE – 21 [31]
Indicator 20 Percentage of integrated systems (application)
[ (Number of integrated systems (application)) / (total systems (applications)] x 100%
List of systems (application); application architecture
Adapted from SBPE – 23 [31]
a) b) c) d) e) f) g) h) i) j) Aspect 5 Bureaucratic Governance
Document management service Planning and budgeting Financial management service Procurement service Whistle Blowing System Performance management service Staff management service Data center operation System integration (application) General application sharing
SPBE – 6 [31]
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Domain
Aspects
Indicators
SPBE – 24 [31] PeGI – 20 [33] SPBE – 26 to 31 [31]
Indicator 22 Availability of Whistle Blowing System
Index scale (0-5)
List of systems (application); user manual
SPBE – 34 [31]
Indicator 23 Percentage of open government data
[(Number of government data that is opened) / (Number of government data)] x 100%
ESPRESSO systemic standardization approach to empower smart cities and communities
ICT Pura – 12 [32]
Indicator 24 Availability of City Operation Center
Index scale (0-5)
List of City Operation Center
During Validation Stage
Indicator 25 Availability of government website to receive public report service
Index scale (0-5)
List of government websites and their description
Adapted from SPBE – 32 [31]
Indicator 26 Percentage of response to the public report service via reporting systems within a year
[(Number of responded public reports) / (Number of public reports)] x 100%
List of public report
Adapted from PeGI – 17 [33]
Indicator 27 Percentage of website that accommodate public participation toward regional planning
Index scale (0-5)
List of government websites and their description
Adapted from ICT Pura – 18 [32]
Indicator 28 Availability of websites that accommodate public aspirations in validating the draft of regulatory district
Index scale (0-5)
List of government websites and their description
During Validation Stage
Indicator 29 Availability of regulatory repository systems ‘Sistem Jaringan Dokumentasi dan Informasi Hukum – JDIH’
Index scale (0-5)
List of government websites and their description
Adapted from SPBE – 33 [31]
b) c) d) e) f) g)
Aspect 6 Open access government regulatory
References
List of systems (application); user manual
a)
Aspect 5 Public policy that takes on the perspective of positive impacts to society and therefore accommodates public aspirations
Source of Data
[(Number of available functional categorization) / 7] /x 100%
Indicator 21 Availability of general systems (application) with the following functional categorization:
Domain 3 Public Policy
Formula
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Performance management service Internal document management service Planning management service Budgeting management service Financial management service Procurement service Staff management service
376 10
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References [1] Pusparani, I.G. (2017) “Here’s How Indonesia Will Develop 100 Smart Cities in 2 Years | Seasia.co. Good News from Southeast Asia 2017.” Available from: https://seasia.co/2017/06/14/here-s-how-indonesia-will-develop-100-smart-cities-in-2-years. [Accessed 28 th April 2019]. [2] Rizkinaswara, L. (2018) “Gerakan Menuju 100 Smart City [Title In English: ’Movement to 100 Smart City’].” Ditjen Aptika 2018. Available from: https://aptika.kominfo.go.id/2018/11/gerakan-menuju-100-smart-city/. [Accessed 28th April 2019]. [3] OECD. (2019) “Chapter 2 - Overview of Trends for Smart City Strategies in Emerging Asia.” Economic Outlook for Southeast Asia, China and India 2019 – Update Responding to Environmental Hazards in Cities: Responding to Environmental Hazards in Cities, OECD Publishing. p. 62. [4] PwC Consulting Services. (2015) Smart Cities in Southeast Asia: The Opportunity of Telcos, 20. [5] IISMEX. 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[Accessed 28 th April 2019]. [11] Sanjaya, A., SA. Krisna, TB. Mursito, and Supriyadi. (2018) “Research Trends of Smart City in Indonesia: Where Do We Go from Here?”. doi:10.31227/osf.io/ge359. [12] Pan, J-G., Y-F. Lin, S-Y. Chuang, and Y-C. Kao. (2011) “From Governance to Service-Smart City Evaluations”, in International Joint Conference on Service Sciences, Taipei, Taiwan: IEEE. pp. 334–7. doi:10.1109/IJCSS.2011.74. [13] Scytl. (2013) Smart Cities Start with Governance. [14] Annisah. (2018) “Usulan Perencanaan Smart City: Smart Governance Pemerintah Daerah Kabupaten Mukomuko [Title in English: ’Smart City Planning Proposal : Smart Governance for Regional Government of Mukomuko Regency’].” Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi 2018;8:59–80. doi:10.17933/mti.v8i1.103. [15] Chumakova, O. (2017) “Features of “Smart City” Concept in Urban Paradigm of Globalization”, in MATEC Web Conf 2017;106:01030. doi:10.1051/matecconf/201710601030. [16] Eremia, M., L. Toma, and M. Sanduleac. (2017) “The Smart City Concept in the 21st Century.” Procedia Engineering 2017 181: 12–9. doi:10.1016/j.proeng.2017.02.357. [17] Lim, C.J., E. Liu. (2010) Smartcities and Eco-Warriors, 1 edition, Routledge, New York, Abingdon England. [18] Townsend, A.M. (2013) Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, 1 edition, W. W. Norton & Company, New York. [19] Anthopoulos, L.G., M. Janssen, and V. Weerakkody. (2015) “Comparing Smart Cities with Different Modeling Approaches.” Proceedings of the 24th International Conference on World Wide Web - WWW ’15 Companion, ACM Press, Florence, Italy. pp. 525–8. doi:10.1145/2740908.2743920. [20] Anthopoulos, L.G. (2015) “Understanding the Smart City Domain: A Literature Review”, in de Janvry A, Kanbur R, editors. Poverty, Inequality and Development 1, Boston, MA, Springer US. pp. 9–21. doi:10.1007/978-3-319-03167-5_2. [21] Chourabi, H., T. Nam, S. Walker, JR. Gil-Garcia, S. Mellouli, K. Nahon, et al. (2012) “Understanding Smart Cities: An Integrative Framework”, in 45th Hawaii International Conference on System Sciences, Maui, HI, USA: IEEE. pp. 2289–97. doi:10.1109/HICSS.2012.615. [22] Cohen, B. (2014) “The Smartest Cities In The World 2015: Methodology.” Available from: https://www.fastcompany.com/3038818/thesmartest-cities-in-the-world-2015-methodology. [Accessed 30 th April 2019]. [23] Govada, SS., W. Spruijt, and T. Rodgers. (2017). “Smart City Concept and Framework”, in: Vinod Kumar TM, editor, Smart Economy in Smart Cities, Singapore, Springer Singapore, 187–98. doi:10.1007/978-981-10-1610-3_7. [24] Arafah, Y., and H. Winarso. (2017) “Redefining Smart City Concept With Resilience Approach”, in IOP Conf Ser: Earth Environ Sci 70: 012065. doi:10.1088/1755-1315/70/1/012065. [25] Monzon, A. (2015) “Smart Cities Concept and Challenges: Bases for the Assessment of Smart City Projects”, in Helfert M, Krempels K-H, Klein C, Donellan B, Guiskhin O, editors. Smart Cities, Green Technologies, and Intelligent Transport Systems 579: 17–31. doi:10.1007/978-3-319-27753-0_2. [26] Ministry of Communication and Information. (2017) “Guideline Masterplan Smart City: Gerakan Menuju 100 Smart City [Title in English: Masterplan Smart City Guide].” Available from: https://slideplayer.info/slide/11857474/. [Accessed 18th July 2019]. [27] Boyd, C. (2013) “The Smart City Wheel.” Available from: https://www.smart-circle.org/smartcity/blog/boyd-cohen-the-smart-city-wheel/. [Accessed 18th July 2019]. [28] Lombardi, P., S. Giordano, H. Farouh, and W. Yousef. (2012) “Modelling the Smart City Performance.” Innovation: The European Journal of Social Science Research 25: 137–49. doi:10.1080/13511610.2012.660325.
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[29] Ministry of Communication and Information. (2017) “Buku Panduan Penyusunan Masterplan Smart City [Title in English: ’Guideline Book of the Development of Smart City Masterplan’]”. Available from: http://fliphtml5.com/htogp/pinf/basic. [Accessed 18th July 2019]. [30] Ministry of Administrative and Bureaucratic Reform. (2018) “Decree of the Minister of Administrative and Bureaucratic Reform Republic Indonesia Number 5 2018 Concerning Evaluation Guideline for E-Government 2018.” [31] Ministry of Administrative and Bureaucratic Reform. (2018) “Evaluasi Sistem Pemerintahan Berbasis Elektronik [Title in English: ’Egovernment Evaluation’] 2018”. Available from: https://spbe.menpan.go.id/ [Accessed 18th July 2019]. [32] Indrajit, RE. (2011) “Profil dan Panduan Pelaksanaan Program ICT Pura [Title in English: ICT Pura Guideline].” p. 107. [33] Ministry of Communication and Information. (2018). “Pemeringkatan e-Government Indonesia (PeGI) [Title in English: ’E-government Rangking’] 2018.” Available from: https://pegi.layanan.go.id/. [Accessed 18 th July 2019]. [34] Food Security Department of Ministry of Agriculture. (2018) “Indeks Ketahanan Pangan Indonesia 2018 [Title in English: ’Indonesia Food Security Index 2018’].”
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ScienceDirect Procedia Computer Science 161 (2019) 367–377
The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019
Modelling the Smart Governance Performance to Support Smart Modelling the Smart Governance Performance to Support Smart City Program in Indonesia City Program in Indonesia Anisah Herdiyanti*, Palupi Sekar Hapsari, Tony Dwi Susanto Anisah Herdiyanti*, Palupi Sekar Hapsari, Tony Dwi Susanto
Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Abstract Abstract Every local government in Indonesia is in a race to become the leading innovator of smart city initiatives. The ambitious urban Every local government in Indonesia is in a City” race towas become the leading of smartof city initiatives. Theand ambitious urban digitization program: “Gerakan 100 Smart initiated in 2017innovator by the Ministry Communication Information, digitization “Gerakan 100Affairs, Smart the City” was initiated in 2017 by the of Ministry of Communication supported byprogram: the Ministry of Public Ministry of Finance, the Ministry Public Works and Housing,and and Information, the Ministry supported the Ministry Planning. of Public Affairs, thethe Ministry of Finance,program the Ministry of Public Works promising, and Housing, the Ministry of NationalbyDevelopment Although urban digitization of smart city sounds theand implementation of Nationalsince Development Planning. Although the urban digitization program of smart citygovernment sounds promising, the implementation is lacking there is neither specific national standard nor guidance to assist local undertaking the smart city is lacking since is to neither specific national dimension standard nor assist local government undertaking smart city initiatives. With there respect the smart governance thatguidance serves astoan important foundation for deliveringthe governmentinitiatives. With there respectis to the smart governance that serves as an important foundation delivering related services, limited indicators and thedimension related formula to measure its performance. Thisforresearch fillsgovernmentthe gaps by related services, there is formula limited in indicators the related formulatotosupport measure performance. ThisThe research fills gaps by modelling indicators and the smartand governance dimension theits smart city program. research wasthe conducted modelling indicators andgovernance formula in the smart governance to support the smart city program. The research was conducted by identifying the smart purposes, mapping thedimension current related governance indicators, and developing a model for smart by identifyingperformance. the smart governance purposes, mapping the current related governance indicators, ande-government developing a model for smart governance The model was then validated by three experts of smart city and evaluation and governance The asmodel then threeresults experts of indicators smart cityinand evaluation and implementedperformance. in Surabaya City a casewas study. Thevalidated proposedbymodel in 29 threee-government different domains and seven implemented in Surabaya as a can caseserve study.asThe proposedfor model in 29 indicators in three different and seven aspects of assessment. TheCity model a reference smartresults governance performance evaluation to domains support smart city aspects of in assessment. initiatives Indonesia. The model can serve as a reference for smart governance performance evaluation to support smart city initiatives in Indonesia. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee The Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Smart City; Smart Governance; Evaluation; Performance Keywords: Smart City; Smart Governance; Evaluation; Performance
* Corresponding author. Tel.: +62-31-599-9944; fax: +62-31-596-4965. address:author. [email protected] * E-mail Corresponding Tel.: +62-31-599-9944; fax: +62-31-596-4965. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.135
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Anisah Herdiyanti et al. / Procedia Computer Science 161 (2019) 367–377 Author name / Procedia Computer Science 00 (2019) 000–000
1. Introduction The concept of Smart City has been gaining momentum in Indonesia since the launching of “Gerakan 100 Smart City” or “Movement to 100 Smart City” program by the Ministry of Communication and Information Republic Indonesia in 2017 [1]. The program features a competition and selection process among cities and regions. The selection process invites cities and regions that are ready to undergo an assessment process in Jakarta to measure readiness to pursue each stage of the program. Candidate cities and regions that qualify will receive assistance and mentoring to prepare a Smart City Development Plan on 5 to 10-year horizon. Twenty-five cities/regions were selected in 2017 for the first phase, fifty cities/regions were selected in 2018 for the second phase, and the rest twenty-five cities/regions were selected in 2019 for the last phase [2]. The ongoing urban digitization program aims at establishing Smart City Development Plan of the selected 100 cities/regions in Indonesia. The plan includes three goals: 1) Smart Connectivity (Infrastructure); 2) Smart Solutions (Environment, Governance, Citizen, Security, Education, Transport, Healthcare); and 3) Smart Users (Community). A dedicated smart city strategy is included in the plan explicitly like other 5 (five) countries in Asia as reported by OECD in 2019 [3] – namely China, India, Malaysia, Singapore, and Thailand. This concludes high interest of government in Indonesia in pushing for smart city initiatives that triggers private sector’s take on smart city by conducting surveys [4], seminars [5], or corporate social responsibility programs [6] as well as academic sector’s interest in contributing to the program by growing a community [7], a research group [8], or a knowledge management forum [9]. Although the urban digitization program of smart city sounds promising, the implementation is lacking since there is neither specific national standard nor guidance to assist local government undertaking the smart city initiatives [10,11]. The initiatives shall be integrated within the cities’ or regions’ agenda – namely, Local Government Mediumterm Development Plan which enlists programs/projects to support the cities’ or regions’ objectives within 5 (five) years. Following this, when developing a masterplan of smart city, a roadmap showing how programs/projects related to smart city support the cities’ or regions’ objectives should be highlighted. Furthermore, when dealing with monitoring and evaluating the smart city initiatives, there is lacking performance measurement standard that can serve on a basis of improvement for the initiatives. This study aims at modelling the smart city performance. Among the six dimensions of smart city, the dimension of smart governance is chosen since it subjects to various existing standard of e-government performance measurements in Indonesia, e.g. ‘Sistem Pemerintahan Berbasis Elektronik (SPBE)’ e-government evaluation, ICT Pura evaluation, and ‘Pemeringkatan e-Government Indonesia (PeGI) e-government ranking evaluation. The smart governance dimension is also considered important from previous scholars since it enables smart city efficiencies and engagement [12,13,14]. This study sheds the light in the model of smart governance performance to support the “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. The model will be developed based on the definitions of the smart governance indicators provided in the program, that mapped to the existing standard of egovernment performance evaluation – namely SPBE e-government evaluation, ICT Pura evaluation, and PeGi egovernment ranking. The rest of this paper will discuss about the related works, research methods, results and discussion, and future works. 2. Related works This section will discuss about 1) the smart city concepts, 2) the smart city dimensions, and 3) the related governance measurement standard that has been implemented to assess e-government initiatives in Indonesia. The discussion aims at providing the basic concept, the current practice, and the existing assessment matrices related to egovernment implementation in Indonesia. 2.1. Smart city concepts Smart City is a growing concept ranging from eco-cities [15–17] to various Information and Communication Technology (ICT) attributes in a city [18]. Although different modelling approach to compare smart cities have been
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identified [19], a common definition of what a smart city is, has not yet been stated. However, some key areas from smart cities fundamental theories can be identified: A smart city was viewed with 4 (four) disciplinary perspective: ICT, urban planning and growth, living labs, and creative industry [20]. A smart city was drawn from 8 (eight) critical factors: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment [21]. A smart city was characterized into several key elements: Cohen Smart City Wheel within which 6 (six) dimensions were defined - smart governance, smart living, smart mobility, smart people, smart economy, smart environment [22] Smart City Framework Wheel with People, Place and Planet as core values and 6 (six) dimensions - smart governance, smart living, smart mobility, smart economy, smart environment, smart infrastructure [23] A smart city definition can be classified into 2 (two) parts [24], and therefore the smart city shall be redefined with a resilience approach. One that emphasizing in “hardware” side, i.e. (1) ICT and modern technology; (2) the utilization of advanced technology products to make living in a big city much more convenient; (3) the utilization of ICT and technology also constitutes an effort to improve the quality of life and prevent the degradation of environmental quality One that emphasizing in “software” side, i.e. the role of ICT and technology in actualizing the welfare, effectiveness, and competitiveness of its residents From the various concept of the “Smart City”, it can be concluded that smart city emerges to achieve more efficient and sustainable cities. The smart city notion has evolved from the execution of specific projects to the implementation of global strategies to tackle wider city challenges at different level, i.e. local, national, regional, international (see [25] for environmental smart city project actions and challenges). While considering smart city as a project, its main objectives must be to solve urban problem in an efficient way to improve sustainability of the city and quality life of its inhabitants. This study will take on the smart city concept introduced by Cohen [22] since this concept is adopted by the Ministry of Communication and Information Republic Indonesia when initiated “Gerakan 100 Smart City” or “Movement to 100 Smart City” program [26]. 2.2. Smart city dimensions There has been a growing debate as how to define and structure the smart city dimensions. This section focuses on the discussion of the smart city dimensions adopted by the “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. The program highlights six dimensions adapted from Cohen [22] – they are: smart governance, smart branding, smart economy, smart living, smart society, and smart environment as seen in Fig. 1(a). These dimensions are represented in a Smart City Wheel [27], on which the dimension of ‘smart people’ is adapted to ‘smart society’ as seen in Fig. 1(b). Each of the dimension defines 3 (three) indicators that address its key definitions. This study focuses the discussion on the smart governance dimension considering two factors: 1) its role to enable smart city efficiencies and engagement [13]; and 2) various existing standard on e-government performance in Indonesia. The smart governance dimension focuses on the effectiveness of government in delivering public services [12], and represents an open governance that can enable public participations and be adaptive to technology [28]. According to a guideline for developing a smart city masterplan published by the Ministry of Communication and Information Republic Indonesia [29], smart governance represents good governance of cities/regions that is effective, efficient, communicative, and continuously improved through an innovation and integrated technology adoption. Meanwhile, previous studies on modelling the smart city performance are limited. Lombardi et al. [28] contributes to defining smart city components connecting the cornerstones of the triple helix. Smart governance dimension was conceptualized within the 7 (seven) quantitative indicators: number of universities and research centers in the city, the proportion number of courses entirely downloadable from the internet, e-Government on-line availability, percentage of households with computers, percentage of households with Internet access at home, e-Government usage by individuals, and number of research grants funded by companies, foundations, or institutes. Meanwhile,
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Cohen [27] defines 3 (three) qualitative indicators: enabling supply and demand side policy, transparency and open data, and ICT & e-gov.
(a)
(b)
Fig. 1. (a) Smart City Wheel by Cohen [27]; (b) Smart City Dimensions in “Movement to 100 Smart City” program .
This study takes on the perspective of the Ministry of Communication and Information Republic Indonesia who conceptualized the smart governance dimension within 3 (three) qualitative indicators (See Fig. 1(b)), i.e. ‘(public) services’, ‘bureaucracy’, and ‘(public) policy’. The ‘public services’ indicator is represented by public administration services, basic needs’ facilities, and utility services’ facilities; while the ‘bureaucracy’ indicator is represented by bureaucratic governance that focuses on fairness, accountability, and transparency. The ‘public policy’ indicator is shown by public policy that takes on the perspective of positive impacts to society and therefore accommodates public aspirations. The study will conceptualize model of quantitative indicators based on the qualitative indicators proposed by the Ministry of Communication and Information Republic Indonesia. The model can be used as a reference to measure the performance of smart governance in cities/regions undertaking the “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. 2.3. Existing e-government measurement standard in Indonesia This subsection discuses briefly about the current practice of e-government performance evaluation in Indonesia. The recent evaluation standard was introduced by the Ministry of Administrative and Bureaucratic Reform Republic Indonesia in 2018 [30]. The e-government evaluation namely ‘SPBE Evaluation’ aims at measuring the e-government initiatives progress, provide recommendations for future improvements, and ensure e-government quality in practice [31]. The ‘SPBE Evaluation’ is represented as a maturity level index that covers 3 (three) domains, 7 (seven) aspects, and 35 indicators. The maturity level in the ‘internal policy’ domain, and ‘governance’ domain represents the process capability maturity level; while the maturity level in the ‘service’ domain shows functional capability maturity level. A former evaluation standard introduced by the Ministry of Communication and Information Republic Indonesia is ICT Pura evaluation, and PeGI e-government ranking. The ICT Pura Evaluation is basically a program aims at mapping, assessing, and awarding cities/regions regarding the use of information technology and communication (ICT) [32]. There are 5 (five) dimensions when assessing ICT use in a city/region – namely Needs and Alignment, Suprastructure, Infrastructure, Community and Society, and Outcomes. The assessment results in a readiness index that will be used as a reference to grant the ‘ICT Pura Award’ to cities/regions. Meanwhile, the PeGi e-government ranking aims at assessing the readiness of e-government implementation [33]. The readiness covers 5 (five) dimensions: policy, structure, infrastructure, application, and planning. The assessment produces an index that can underline which dimensions should be improved on.
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3. Related method This study comprises of 3 (three) stages as depicted in Fig. 2. The stages are: a) preparation stage; b) model development stage; and c) validation stage. Preparation Stage
Model Development Stage
Validation Stage
Fig. 2. Three stages of the research.
The Preparation Stage consists of two main activities – they are performing desk review and mapping indicators. This stage begins with conducting desk-review on the dimensions of 4 (four) e-government evaluation standard: 1) the smart governance dimension based on the guideline of “Movement to 100 Smart City” program; 2) SPBE Evaluation dimensions; 3) the ICT Pura dimensions; and 4) PeGi dimensions. The desk-review aims at producing a list of indicators for each of the dimensions in the e-government evaluation standard. Taking the perspective of the smart governance dimension of the “Movement to 100 Smart City” program, the study continues to identify the mapping between its indicators and the indicators from SPBE dimensions, ICT Pura dimensions, and PeGi dimensions. The result of this stage is mapping of indicators. The Model Development Stage is mainly about constructing the smart governance performance model. The model is adapted from the current structure of smart governance in the “Movement to 100 Smart City” program and expanded with list of quantitative indicators. The result of this stage is the proposed model. The Validation Stage shows the model validation. This study uses expert judgement and a case study for model validation. The expert judgements involve 3 (three) experts of smart city and e-government evaluation from academicians to validate the structure and contents of the model. After performing the structural validation, a case study of Surabaya city is chosen for model implementation. A secondary data is collected, and then is assessed. The result of the smart governance performance evaluation is presented at the end of this stage. Limitations and future works are also discussed to identify the possibilities for the model for future improvements. 4. Result and discussions This section presents the results of the study. The discussion will be categorized according to the research method – i.e. preparation stage, model development stage, and validation stage. 4.1. Preparation stage The study found that statistics of the 4 (four) e-government evaluation standard as seen in Table 1. Each of the standard defines domains and sub domains/aspects within which several indicators are defined. Table 1. Indicators of the e-government evaluation standard. Name of Evaluation Standard
Number of domains
Number of indicators
Type of indicators
Smart governance (“Movement to 100 Smart City” program)
3
3
Qualitative
SPBE evaluation
3
35
Quantitative
ICT Pura
3
100
Quantitative
PeGI
5
24
Quantitative
Table 1 also shows types of indicators in each standard. Apart from the smart governance of the “Movement to 100 Smart City” program, all standards introduce a quantitative-type indicator. This is shown by an index used at the end
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of the assessment. The smart governance is not yet presenting the quantitative indicators and therefore, this study aims at filling this gap. After conducting a desk review, we mapped the quantitative-type indicators to the qualitative indicator. A list of indicators was categorized in 3 (three) domains – namely Domain 1 Public Services, Domain 2 Bureaucracy, and Domain 3 Public Policy. The first domain comprises of 3 (three) aspects with total 7 (seven) indicators; while the second domain consists of 1 (one) aspects with total 13 indicators. The third domain includes 2 (two) aspects with total 5 indicators. All indicators in SPBE evaluation were mapped, while only 56 indicators of ICT Pura can be mapped. The rest of ICT Pura indicators that were not mapped (44 indicators), are related to other smart city dimensions, e.g. smart society, smart economy, and smart branding, and ICT infrastructure related indicators. The smart governance dimension does not explicitly state the needs of ICT infrastructure related indicators. Similar case happens to 4 (four) PeGI indicators that relate to infrastructure. Therefore, only 20 indicators of PeGI can be mapped. 4.2. Model development stage An initial model was constructed at this stage. The model comprises of 3 (three) domains, 6 (six) aspects, and 25 indicators. Indicator elements were also added including formula and source of data to fill the formula. Each indicator also was equipped with a scale to quantitively measure smart governance performance. For instance, “Indicator 1 Percentage of online public services (e-government)” is defined by number of online public services divided by the total of online public services – then convert this portion into a percentage. To fill this formula, several data are needed, e.g. list of public services, status of public services (online or manual). 4.3. Validation stage A purposive sampling technique was implemented to validate initial model. Expert judgement method was employed by conducting interviews with 3 (three) experts with two qualifications: a) knowledgeable in e-government evaluation; and b) knowledgeable in smart city trends in Indonesia. A feedback sheet was developed aiming at validating the proposed domain and the related indicators and validating the formula. General feedback regarding the model were also discussed to improve the overall model. During April to May, three experts were interviewed using the feedback sheet – they are a) Mr. Khakim Ghozali (lecturer, ICT Pura surveyor, e-Government Award committee); b) Mr. Lukito Edi Nugroho (lecturer, triple-helix consultant, smart city initiator); c) Mr. Ferry Astika Saputra.(lecturer, SPBE evaluation committee, external evaluator of SPBE). The validation resulted in 5 (five) main points: i) sequence order of indicators; ii) additional 5 new indicators, i.e.: availability of basic needs’ monitoring, percentage of clean water, availability of clean water monitoring service, availability of city operation center, availability of websites that accommodate public aspirations in validating the draft of regulatory district; iii) reducing 1 indicator, i.e. percentage of regulatory product completed within a year; iv) revising and adding new aspects, i.e. Aspect 4 Internal Policy, Aspect 5 Bureaucratic Governance; and v) smartness categorization (lacking, sufficient, good, very good, and satisfactory). The initial model is then updated. There are 3 (three) domains in the proposed model – namely Domain 1 Public Services, Domain 2 Bureaucracy, and Domain 3 Public Policy. The first domain comprises of 3 (three) aspects with total 10 indicators; while the second domain consists of two aspects with total 14 indicators. The third domain includes 2 (two) aspects with total 5 indicators. The total number of indicators is 29 indicators in the proposed model. A complete list of the proposed model along with its formula and source of data, is presented in Annex A. The proposed model was implemented in a case study of Surabaya city using a secondary data of 2019 ICT monitoring and evaluation in the district. The highest score reached by Domain 1 with 78% score (39 out of 50); while the lowest score reached by Domain 2 with 55,71% score (39 out of 70). Domain 3 scored 60% (15 out of 25). The total score for Surabaya City is 64,14% score (93 out of 145) that represents very good smartness category. 5. Conclusion and future works The study proposed a model to measure smart governance measure to support “Gerakan 100 Smart City” or “Movement to 100 Smart City” program. The proposed model introduced 29 indicators within 3 (three) domains and
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6 (six) aspects. The indicators within the model was very dependent to the existing e-government evaluation standards since it was formulated by mapping the standards. This study contributes to defining quantitative indicators for the smart governance. The model can serve as a reference for policy makers in defining city smartness within smart governance domain. Future works shall consider multi-actor perspective of defining indicators taking perspective of government i.e. the Ministry of Communication and Information Republic Indonesia, industries, and academicians from different backgrounds. It shall also aim at developing a performance model for all smart city dimensions. Acknowledgements The work presented in this paper is supported by a research grant from Institute of Research and Public Services, Institut Teknologi Sepuluh Nopember. The authors also thanks Mr. Khakim Ghozali, Mr. Lukito Edi Nugroho, and Mr. Ferry Astika Saputra for the support provided during the validation stage of the research. Appendix A. smart governance performance model Table A.1 shows the domains, aspects, and indicators of the proposed Smart Governance Performance Model. Table A.1 The proposed Smart Governance Performance Model. Domain
Aspects Aspect 1 Public Administration Services
Aspect 2 Basic Needs’ Facilities
Domain 1 Public Services
Aspect 3 Utility Services’ Facilities
Source of Data
References
Indicator 1 Percentage of online public services (egovernment)
Indicators
[(Number of online public services) / (Number of public services)] x 100%
Formula
List of public services; Status of public services (online or manual)
Lombardi – 6 [28]
Indicator 2 Percentage of complete SOP for public services
[(Number of SOP for public services) / (Number of public services)] x 100%
List of public services; List of SOP for public services
Adapted from PeGI – 19 [33]
Indicator 3 Food Security Index
[(Index of food availability per capita + Index of food quality and safety + Index of availability of market food price) / 3] x 100%
KPI of Department of Food Security, Ministry of Agriculture
Department of Food Security, Ministry of Agriculture [34]
Indicator 4 Availability of basic needs’ monitoring
Index scale (0-5)
List of systems that monitor basic needs’
During Validation Stage
Indicator 5 Percentage of clean water
[(Number of households with clean water) / (Number of household)] x 100%
Percentage of clean water
During Validation Stage
Indicator 6 Availability of clean water monitoring
Index scale (0-5)
List of systems that monitor clean water monitoring
During Validation Stage
Indicator 7 Electrification Ratio
[(Number of Household with electricity) / (number of total households) x 100%
KPI of Ministry of Energy and Mineral Resources
ICT Pura – 8 [32]
Indicator 8 Percentage of Telephone Infrastructure Coverage
[(Coverage area) / (district land area)) x 100%
Tower maps in the area; Land area (km)
Adapted from ICT Pura – 9 [32]
Indicator 9 Availability of Internet Service Provider (ISP) in the district
Index scale (0-5)
List of ISP in the district
ICT Pura – 9 [32]
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374 8
Domain
Domain 2 Bureaucracy
Aspects
Aspect 4 Internal Policy
Formula
Source of Data
References
Indicator 10 Percentage of Household Connected to Internet
Indicators
[(Number of household connected to internet) / (Number of households)] x 100%
Number of households; number of households connected to internet
Lombardi – 5 [28]
Indicator 11 Availability of smart city goals (vision and mission)
Index scale (0-5)
Document of Smart City Masterplan (Vision and Mission Statement)
Adapted from PeGI – 1 [33]
Indicator 12 Availability of internal policy related to:
[(Number of internal policy category that is available) / 10] x 100%
List of internal policy
SPBE – 8 to 12 [31]
Indicator 13 Availability of e-government masterplan
Index scale (0-5)
Document of Egovernment Masterplan (or ICT Masterplan)
SPBE – 3 [31]
Indicator 14 Availability of smart city masterplan
Index scale (0-5)
Document of Smart City Masterplan
Adapted from SPBE – 3 [31]
Indicator 15 Availability of smart city goals (vision, mission)
Index scale (0-5)
Document of Smart City Masterplan
Adapted from PeGI – 1 [33]
Indicator 16 Availability of smart city action plan
Index scale (0-5)
Document of Smart City Masterplan
Adapted from PeGI – 2 [33]
Indicator 17 Availability of smart city board
Index scale (0-5)
Document of Smart City Masterplan; Mayor Decree
Adapted from PeGI – 7 [33]
Indicator 18 Availability of job descriptions for each unit
Index scale (0-5)
Mayor Decree
PeGI – 8 [33]
Indicator 19 Percentage of ICT budgets
[ Ʃ (ICT Budgets) / (Unit Budgets) x 100%) / (Number of unit)]
List of ICT budgets; List of budgets in each unit
SPBE – 21 [31]
Indicator 20 Percentage of integrated systems (application)
[ (Number of integrated systems (application)) / (total systems (applications)] x 100%
List of systems (application); application architecture
Adapted from SBPE – 23 [31]
a) b) c) d) e) f) g) h) i) j) Aspect 5 Bureaucratic Governance
Document management service Planning and budgeting Financial management service Procurement service Whistle Blowing System Performance management service Staff management service Data center operation System integration (application) General application sharing
SPBE – 6 [31]
Anisah Herdiyanti et al. / Procedia Computer Science 161 (2019) 367–377 Author name / Procedia Computer Science 00 (2019) 000–000
Domain
Aspects
Indicators
SPBE – 24 [31] PeGI – 20 [33] SPBE – 26 to 31 [31]
Indicator 22 Availability of Whistle Blowing System
Index scale (0-5)
List of systems (application); user manual
SPBE – 34 [31]
Indicator 23 Percentage of open government data
[(Number of government data that is opened) / (Number of government data)] x 100%
ESPRESSO systemic standardization approach to empower smart cities and communities
ICT Pura – 12 [32]
Indicator 24 Availability of City Operation Center
Index scale (0-5)
List of City Operation Center
During Validation Stage
Indicator 25 Availability of government website to receive public report service
Index scale (0-5)
List of government websites and their description
Adapted from SPBE – 32 [31]
Indicator 26 Percentage of response to the public report service via reporting systems within a year
[(Number of responded public reports) / (Number of public reports)] x 100%
List of public report
Adapted from PeGI – 17 [33]
Indicator 27 Percentage of website that accommodate public participation toward regional planning
Index scale (0-5)
List of government websites and their description
Adapted from ICT Pura – 18 [32]
Indicator 28 Availability of websites that accommodate public aspirations in validating the draft of regulatory district
Index scale (0-5)
List of government websites and their description
During Validation Stage
Indicator 29 Availability of regulatory repository systems ‘Sistem Jaringan Dokumentasi dan Informasi Hukum – JDIH’
Index scale (0-5)
List of government websites and their description
Adapted from SPBE – 33 [31]
b) c) d) e) f) g)
Aspect 6 Open access government regulatory
References
List of systems (application); user manual
a)
Aspect 5 Public policy that takes on the perspective of positive impacts to society and therefore accommodates public aspirations
Source of Data
[(Number of available functional categorization) / 7] /x 100%
Indicator 21 Availability of general systems (application) with the following functional categorization:
Domain 3 Public Policy
Formula
375 9
Performance management service Internal document management service Planning management service Budgeting management service Financial management service Procurement service Staff management service
376 10
Anisah Herdiyanti et al. / Procedia Computer Science 161 (2019) 367–377 Author name / Procedia Computer Science 00 (2019) 000–000
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