Barriers to the Development of Smart Cities in Indian Context

Smart city development is gaining considerable recognition in the systematic literature and international policies in the last two decades (Albino et al. 2015; Koo et al. 2017; Mori and Christodoulou 2012). For this work, a smart city can be defined as a technologically advanced and modernised territory with a certain intellectual ability that deals with various social, technical, economic aspects of growth based on smart computing techniques to develop superior infrastructure constituents and services (Bakıcı et al. 2013; Cruz-Jesus et al. 2017; Washburn et al. 2010; Zygiaris 2013; Chatterjee and Kar 2018b).

As per the United Nations Population Fund, a large proportion of the population will shift to city regions by 2050 (UNFPA 2008). In India, urbanisation is growing rapidly and cities are likely to expand to 600 million by 2030. Another study by Mckinsey (2018) reported that in the following 15 years, around 200 million people will transition from rural to urban areas in India. The change will be enormous, nearly equal to existing populations of France, Germany, and the United Kingdom combined. In this sense, the Government of India (GoI) is committed to enhance the quality of life for citizens through its urban development agenda (Bloomberg Philanthropies 2017; Nair 2017). In light of this, GoI has listed 109 of India’s most popular urban centres where the focus shifts from “highways to i-ways”.

The urban population of India is growing at a lesser rate when compared to the global average (31.15% as per the 2011 census of India). The reason for this may be a lack of governmental supportive polices or challenges in managing the urban dynamics. On the other hand, countries such Chile, Mexico, Argentina, Brazil and China have responded by launching various timely initiatives to manage urbanization efficiently (Aijaz 2016). For example, Santiago de Chile has shown advancements on becoming smarter (Fast Company 2013). Similarly, the Chinese city Xinxiang pursued a joint programme with IBM to improve its transportation network and community safety (China Daily 2013). In citizens’ quality of life index, countries such as Denmark, Switzerland and Australia are out performing Asian countries including India. For improving the quality of life, policymakers conducted an initiative of smart city development in India (OCED 2015; The Indian Express 2016). However, cities in developing countries like India are extremely different to design and implement.

Cities generate new kinds of physical problems such as scarcity of resources, air pollution, difficulty in waste management, traffic congestions, and inadequate, deteriorating and aging infrastructures etc. (Chourabi et al. 2012). Another set of challenges arise from the massive levels of digitization and generation of data (Chauhan et al. 2016). In recent years, a sequence of challenges in the cities’ economies and needs has arisen, administering the promotion of the smart city idea. In addition, the literature also lacks a clear understanding of strategic planning for smart city projects (Angelidou 2015). There is a clear literature gap pertaining to the smart city agenda, including its theoretical development and evaluation of related challenges that facilitate implementation in a country context (Yigitcanlar 2015). Therefore, key barriers to the smart cities’ development need to be identified and evaluated.

To help policymakers, in this work, the key barriers to the smart cities’ development are identified from an evaluation of the literature and experts’ feedback. Different experts might have diverse opinions regarding the barriers to the smart cities’ development in India. Therefore, the experts on smart cities with regard to academia, industry and public-sector organisations were included to provide their views on the various barriers that may influence the way in which smart cities develop. Specifically, this research sets the following objectives: [i] Identification of relevant barriers of smart cities development in India, [ii] Prioritisation of barriers to recognise the most important barriers of smart cities development in India. The selection of barriers was made through literature and inputs received from experts. Prioritizing the barriers is a decision problem involving various criteria and sub-criteria. Various difficulties supplement the prioritization of barriers due to human involvement and indistinctness in data (Mangla et al. 2017). To remove the essential imprecision and ambiguity, this work uses the fuzzy set theory (Zadeh 1965). In this work, authors selected the fuzzy AHP (Analytic Hierarchy Approach) due to this technique’s ability to determine the importance of the identified barriers under fuzzy surroundings (Govindan et al. 2015). The fuzzy AHP permits mixing fuzzy set theory with the AHP technique to capture the human bias in judgements when developing pair-wise comparisons between barriers.

The remaining sections of the paper are structured as follows: Section 2 presents the related literature on smart cities and highlights the barriers of smart cities development. Section 3 discusses the solution methodology along with the research framework. Section 4 illustrates the data analysis and results. Section 5 presents the sensitivity analysis to examine the priority rank stability. Section 6 discusses the results and presents the theoretical contributions and implications for practice. Finally, Section 8 provides conclusions, limitations and directions of future research.

This section illustrates the literature linked to smart cities, and identifies the barriers related to smart cities development.

This work used fuzzy AHP as the research method. This approach allows factors/variables/phenomena to be weighted in terms of importance, in this case smart cities and their related barriers as well as the categories of barriers. First introduced in 1980 by Thomas L Saaty, AHP is a decision-making tool, which assists in developing a hierarchical structure of variables (Saaty 1980; Luthra et al. 2013; Luthra et al. 2016b). AHP/Fuzzy AHP is arguably superior to other decision analysis methods such as fuzzy TOPSIS/TOPSIS, fuzzy ANP/ANP, and ELECTRE and due to their limited acceptability and complexity (Harputlugil et al. 2011; Mangla et al. 2017). AHP is accessible to use and produces robust results for managers. AHP highlights the alternative, which best accords to achieve the defined goal and understanding of the problem. A human presence can lead to subjectivity in the analysis, however, the application of AHP limits such biases (Mangla et al. 2015). AHP provides the numerical priorities for each variable to attain the goal (Ordoobadi 2010). However, AHP has its own limitations, described as (Ishizaka and Labib 2009; Mangla et al. 2016):

To deal with above problems, AHP techniques can be extended to modified AHP – Bayesian approach, Fuzzy AHP, and Grey AHP (Govindan et al. 2017; Kar 2015; Sahoo et al. 2016). Amonst these, fuzzy AHP is preferential, due to its simplicity and higher consistency (Junior et al. 2014; Prakash and Barua 2015). The Fuzzy AHP technique also allows (i) analysing the behaviour of complex system in decision-making; (ii) evaluating the human judgment by determining the relative importance of system variables. Therefore, this research proposes to use a fuzzy based AHP approach for prioritizing the barriers in smart city development in India. The flow map for the fuzzy based AHP technique is shown in Fig. 1, and the steps involved are explained as follows:

The fuzzy AHP involved several steps (Chan et al., 2008) as follows: Step 1: Formulating and defining the aim of research work: The aim of work to prioritize the barriers in smart cities development is defined. Step 2: Applying the fuzzy concepts: In a decision-making problem generally involves human assessments consist of qualitative judgments. Thereby, the fuzzy concepts are preferred (Dubois and Prade 1979; Zadeh 1965). The triangular fuzzy number (TFN) is used in this work. Step 3: Constructing a hierarchical structure: In respect to the aim of this work, a hierarchical structural keeping the experts’ view into account is formed. Step 4: Developing a fuzzy pair wise assessment matrix: The pair wise assessment matrix for the barriers are formed. Prior to this, a nine-point scale of relative importance based on TFNs is designed (Table 2). Experts generally provide their feedback in terms of linguistic statements thus fuzzy scores were used to transform their linguistic inputs into numbers.

In order to develop a positive fuzzy comparison matrix (M), the average of the pair wise comparisons from expert panel is computed, which is given as M = [m_uv]_n × m.

Where, m_xy shows the fuzzy entries in the developed fuzzy positive matrix, i.e., (i_uv, j_uv, k_uv). Further, positive fuzzy numbers should also satisfy the properties, given as below:

\( {\mathrm{i}}_{\mathrm{uv}}=\frac{1}{{\mathrm{i}}_{\mathrm{uv}}},{\mathrm{j}}_{\mathrm{uv}}=\frac{1}{{\mathrm{i}}_{\mathrm{uv}}},\kern0.75em {\mathrm{k}}_{\mathrm{uv}}=\frac{1}{{\mathrm{i}}_{\mathrm{uv}}} \), where, u and v = 1, 2 ………………z, i.e., no. of criteria.

Step 5: Devising barriers significance weights: The fuzzy assessment matrix is further evaluated using Chang’s Extent Analysis method (Chang 1996; Luthra et al. 2015; Mangla et al. 2017). This helps in determining the significance weights of barriers. The detail for Chang’s Extent Analysis method is given in Appendix 1.

A conceptual framework for analysing the identified inhibitors relevant to smart city development is proposed (see Fig. 2). The framework is developed by following the guidelines of Platts and Gregory (1990) and given as below:

The conceptual framework depicts a real-life illustration of the issues of smart city development in India perspective as presented in Section 4. However, questionnaire and data collection is demonstrated in the next sub-section.

Fuzzy AHP is utilized to find the dominant barriers to smart city development in Indian context. Data analysis and related results have been provided. The proposed framework is applied to the research problem under study with other details as below:

Generally, there is an immense imprecision and vagueness present in the data collection process. Sensitivity analysis monitors the priority ranking of the recognized barriers to smart cities development. Further, it has a tendency that can determine the smallest change in the ranking with the changes in relative weights of the barrier. In this sense, it is sensible to verify the priority ranks by altering the weights of all the categories of barriers (Mangla et al. 2015).

In this research, ‘Governance (GOV)’ category is the topmost ranked among all (see Table 6). This category would affect the other categories of barriers for smart city development. For that reason, we varied the ‘Governance’ category relative weights from values 0.1 to 0.9 and changes in the weights of other categories were noted correspondingly (see Table 8).

At 0.1 value of ‘Governance’ category, barrier SOC1 obtains the highest rank and barrier L&E5 obtains the lowest rank. Barrier SOC1 retains the highest rank and barrier L&E5 the lowest rank value until the normal value (0.2295) for Governance category is reached. From varying the Governance category weights value (from 0.3 to 0.9), barrier GOV3 holds highest rank, and the ranking of other barriers also vary accordingly. The changes in the weights of specific barriers when Governance category weights change from 0.1 to 0.9 have been presented in Table 9.

Global preference weight of the smart city development barriers based on sensitivity analysis is shown in Fig. 4.

From Fig. 4, insignificant changes can be noticed in the global weights of barriers, and thus, the proposed framework is robust enough to deal with human subjectivity and uncertainty in data under fuzzy conditions.

According to Table 6, the categories of barriers follow the order in priority as - Governance (GOV) - Economic (ECO) - Technology (TECH) - Social (SOC) - Environmental (ENV) - Legal and Ethical (L&E). Governance (GOV) categories of barriers obtain the first rank. The implementation of smart city is highly context dependent (nations, government etc.) (Weisi and Ping 2014). Governance is one of key concerns in developing an efficient smart cities network. Thus, there is a higher need of better governance to manage several cities initiatives effectively (Chourabi et al. 2012). Within this category, ‘Political instability (GOV3)’ obtains the highest priority. Letaifa (2015) suggested that a smart city vision obstructed by political instability. Thus, leaders and practitioners should have a clear vision of the future; and make long-term plans, which could be only possible by political leadership and stability. ‘Lack of cooperation and coordination between city’s operational networks (GOV1)’ is ranked after GOV3. There is a high need to promote cooperation and coordination between local authorities i.e. city’s operational networks. ‘Unclear IT management vision (GOV2)’ comes next in the priority list. Chourabi et al. (2012) suggested that the integration of IT with development projects is crucial in smart city context. Next is ‘Poor private-public participation (GOV5)’ in this category. It means that policymakers should make efforts to promote private-public participations and investments for better governance in developing a smart city (Lee et al. 2014). ‘Lack of trust between governed and government (GOV4)’ comes after GOV5 according to their priority. Various researchers suggested that privacy and security issues are major concerns to develop trust between governed and government in the smart cities context. Khan et al. (2017) suggested in their research that user participation is crucial in managing smart cities data privacy and security related concerns to improve trust between governed and government. Finally, the ‘Lack of developing a common information system model (GOV6)’ stands last in the list. It means that common information system is modelled to collect city data to make meaningful decisions or actions in smart cities context.

Economic (ECO) category acquires second place among other barrier categories. Smart cities will require huge infrastructure, modern technologies, based on massive interconnected networks of sensors, screens, cameras, smart devices, smart grid etc. to analyse data and or information. Guy et al. (2011) concluded that infrastructure’s development depends on government regulations and financial resources availability. This particular category has five specific barriers - ‘Lack of competitiveness (ECO2)’ obtains the utmost importance. This implies that urban areas need to be managed in such a way that leads to higher economic competitiveness, enhanced social security and ecological sustainability (Monzon 2015). However, the government fails to do that. Following this, the next is ‘Global economy volatility (ECO4)’ barrier in the list. Global economy volatility can influence the subsidies provided, and results in higher/lower greenhouse gas emissions. Subsequently, ‘High IT infrastructure and intelligence deficit (ECO1)’ shows that huge infrastructure and intelligent/smart systems are required to develop smart cities. Nevertheless, it requires a lot of funds. The ‘Higher operational and maintenance cost (ECO5)’ barrier is next in terms of priority. Thus, technologists and practitioners must focus improving efficiency of the system for refining its sustainability (Mohanty et al. 2016). Finally, ‘Cost of IT training and skills development (ECO3)’ barrier is the last in the priority sequence i.e. smart city development requires higher IT training and skills, which is usually very costly.

Technology (TECH) acquired the third importance level among all the categories. Smart cities development needs higher research and technological innovations. There are different technological developments related to the IoT and Cloud computing in smart cities (Li et al. 2015; Petrolo et al. 2017; Whitmore et al. 2015). Li et al. (2015) and Whitmore et al. (2015) quoted in their research that IoT technologies will play key role in making cities more efficient and improving the lives of citizens. In this particular category, ‘Lacking technological knowledge among the planners (TECH1)’ barrier holds the highest priority. In respect to developing a smart city, it requires technological knowledge among the planners (Letaifa 2015), as ‘Lack of access to technology (TECH2)’ barrier comes next to TECH1. Monzon (2015) suggested that a majority of the population living in these cities lack the access to technology. Hence, policymakers should make available the necessary technology and arrange training programs to educate the citizens for its accurate usage. Next, ‘Privacy and security issues (TECH3)’ comes in this category of barriers to smart city development. Many researchers highlighted the privacy and security issues in smart cities context (Elmaghraby and Losavio 2014; Belanche-Gracia et al. 2015; van Zoonen 2016; Zhang et al. 2017). ‘System failures issues (TECH4)’ barrier comes next. Colding and Barthel (2017) suggested smart city network is highly vulnerable so as provide ample room for cyber-attacks of different kinds and other forms of incidents such as industrial espionage, terrorists, equipment failures, worm infestations and natural disasters. Next to this is ‘Integration and convergence issues across IT networks (TECH5)’ barrier to smart city development. Smart cities require various heterogeneous components to communicate, but in designing, a flexible interface to integrate these heterogeneous components is challenging. Cyber physical networks need to be integrated and supported for an effective data exchange and analysis in smart cities environment. Finally, ‘Poor data availability and scalability (TECH6)’ is last in the list. Santana et al. (2017) suggested that policy planners should address the issues related to data quality and its scalability in smart city context. Janssen et al. (2017) and Pereira et al. (2017) revealed in their research that big open data initiatives can help in providing real-time weather forecast, pollution and traffic management, creating transparency, better decision and policy-making and crisis management etc., and contribute to enhance the delivery of public value in smart city contexts.

Social (SOC) category of barriers occupies next place in the main priority list. There are several social concerns in developing of smart cities, such as public health and safety, education, and hospital facilities (Solanas et al. 2014). Policymakers need to deal with the social challenges in smart cities development. Colding and Barthel (2017) stipluated that there are multiple socio-economic challenges with massive demographic transition; detrimental environmental impacts may also follow unless adequate measures are taken. This category has four specific barriers to smart city development. ‘Lack of involvement of citizens (SOC1)’ is the top ranked barrier in this category. This could be validated from the research of Yang and Callahan (2007) that citizens are often criticized due to their low interest and participation. In this sense, policymakers should encourage citizens to contribute in decision-making processes for a sustainable city. Afterwards, ‘Low awareness level of community (SOC2)’ barrier comes in this category. It means that community engagement is very important for planning and implementing smart cities initiatives. Next to this is ‘Geographical diversification problems (SOC3)’ barrier to smart cities development. In India, with a high geographical diversity, needs large amount of data to analyse urban issues and other geographical processes (Batty 2012; Liu et al. 2016). Finally, ‘Degree of inequality (SOC4)’ is last in the hierarchy list of barriers to smart cities development. Therefore, inequalities among the citizens must be reduced to plan smart cities initiatives.

Environmental (ENV) category of barriers occupies fifth place in the priority list. Thus, practitioners, policymakers and citizens must focus to observe various ecological parameters like air pollution, temperature, vibrations, and noise and make humans consume less energy and water, and even reduce greenhouse gas emissions etc. (Colding and Barthel 2017). This category has five specific barriers. ‘Growing population problems (ENV2)’ is at the top ranking. In India, the urbanisation is growing rapidly, and cities are likely to expand to 600 million by 2030. Higher population needs more resources to fulfil their requirements (Albino et al. 2015). Subsequently, ‘Carbon emissions effect (ENV4)’ is the next to come in this category. Sadorsky (2014) pointed out that growing urbanization leads to higher carbon emissions and results in lower sustainability. ‘Lacking ecological view in behaviour (ENV1)’ comes next. It means that a holistic approach should be adapted to promote ecological view in behaviour in citizens. Next to this is ‘Degradation of resources (ENV5)’ to smart cities development. Finally, ‘Lack of sustainability considerations (ENV3)’ is at the end in the list. Policy planners are suggested to include sustainability aspects while designing smart city networks for higher ecological benefits (Luthra et al. 2015).

Legal and Ethical (L&E) category of barriers holds the last place in priority list. Kitchin (2015) affirmed that there are several social, ethical and legal issues linked to a smart city initiative. Within this particular category, ‘Lacking standardization (L&E2)’ barrier is ranked first. Clearly, there is a lack of standards and policy directions on efficient applicability and managing of IoT based networks (Weber 2013; Perera et al. 2014; Zanella et al. 2014; Weber and Studer 2016). ‘Issues of openness of data (L&E3)’ comes next to the list. Rathore et al. (2016) identified the issues of openness of data are crucial in the smart city agenda. Enabling openness of real time data will help the government authorities as well as citizens. The next barrier i.e. ‘Lack of transparency and liability (L&E4)’ indicates that higher public involvement and superior transparency in governance is critical in smart cities development (Kandpal et al. 2017). Next barrier in this list is ‘Cultural issues (L&E1)’ to smart cities development. Last in the priority list is ‘Lack of regulatory norms, policies and directions (L&E5)’. Well-defined regulating norms, polices and directions are needed that help in keeping the user-friendliness to the data users and monitoring all the stakeholders and parties being a part of the system.

Further, we identified the global ranking of barriers to smart city development. According to global ranking of barriers, ‘Lack of involvement of citizens (SOC1)’; ‘Lack of competitiveness (ECO2)’; ‘Global economy volatility (ECO4)’; ‘Political instability (GOV3)’ and ‘Low awareness level of community (SOC2)’ have been recognized as top five barriers to smart cities development in Indian context.

Smart city development is gaining considerable recognition in the systematic literature and international policies in the last two decades. The present research seeks to recognise and prioritise barriers linked to smart city development to help policymakers in improving their sustainability in an Indian context. In this work, we used fuzzy AHP to demonstrate the importance of the potential barriers under fuzzy surroundings. A comprehensive review of keywords across various literature surveys disclosed 31 key barriers to smart cities development. These barriers were also confirmed further through a panel of experts. We categorised these barriers into six key categories with experts’ consultation. The findings revealed ‘Governance’ is documented as the most significant category of barriers for smart city development followed by barriers related to ‘Economic; ‘Technology’; ‘Social’; ‘Environmental’ and ‘Legal and Ethical’ categories. The relative and global preference weights of specific barriers are also determined. The sensitivity analysis is performed to verify the stability of the findings obtained in this study. This research is useful to the government and policymakers for eradicating the potential interferences in smart city development initiatives in developing countries like India.