The rise of globalization, characterized by the interlinked flow of trade, technology, and information across borders, acted as a powerful facilitator for urbanization. The mitigation of trade barriers and emerging global markets have propelled cities to become pivotal hubs for international businesses, attracting industries, jobs, and investment. Cities, with their diverse talent pools and research institutions, become incubators of innovation, further attracting skilled workers and entrepreneurs, and perpetuating the urbanization cycle. Moreover, globalized economies prioritize efficient transportation and communication networks, often concentrated in urban areas. This enhanced infrastructure attracts businesses and residents, creating a self-reinforcing cycle of investment and growth.

India is widely recognized as the fastest-growing economy globally, with the consequential implications for the pace and character of urbanization. The amalgamation of burgeoning aspirations and expanding middle-class demographics, juxtaposed with insufficient planning for the inevitable surge in urbanization, presents a scenario fraught with social, financial, and environmental unsustainability ( Gore & Gopakumar, 2015). The imperative confronting India’s planners and policymakers lies in significantly enhancing the urban eminence to accommodate future progress while safeguarding improved living ideals for municipal inhabitants. The Government of India (GOI) has actively embraced urban advance through strategic initiatives, including the establishment of above hundred new towns and metropolises to address demographic, financial, and secretarial necessities ( Brown, 1984). Over the ensuing three decades, a considerable number of new urban centers were established, laying the groundwork for essential institutions supporting sustained urban expansion ( Shaw, 2009).

The current phase of urban expansion strategy has witnessed a notable modification towards the privatization of urban strategy in India ( Batra, 2009). A pivotal development during this period was the enactment of the 74th Constitutional Amendment Act, aimed at decentralizing decision-making by establishing elected urban local bodies ( Batra, 2009). This move also entailed the devolution of essential functions related to city planning and service provision ( Nandi & Gamkhar, 2013). Subsequently, new missions concentrating on different aspects of urban expansion have been launched. The notable ones include the Jawaharlal Nehru National Urban Renewal Mission (JNNURM), National Urban Livelihood Mission (NULM), Pradhan Mantri Awas Yojana (PMAY), HRIDAY, and Atal Mission for Rejuvenation and Urban Transformation (AMRUT) mutually covering almost all metropolises across India.

As mentioned earlier, India is experiencing rapid development, accompanied by a significant increase in urbanization rates. The urbanization phenomenon is considered pivotal in the country’s overall development strategy. A primary focus of this urbanization drive is to enhance infrastructure and services, thereby elevating the living standards of the citizens. Launched in 2015 by the Government of India, the Atal Mission for Rejuvenation and Urban Transformation (AMRUT) stands as a comprehensive urban renewal initiative spanning 500 cities across the nation. This initiative incorporates elements akin to smart city development strategies, emphasizing private investments and long-term infrastructure enhancements ( Priyadarshi, 2018). Notably, both AMRUT and smart city initiatives aim to stimulate reforms, focusing on infrastructure development that directly addresses the genuine needs of the populace. AMRUT underscores that infrastructure development should have a tangible impact on addressing the essential requirements of the people, leading to improved service delivery, resource mobilization, and greater transparency in municipal office operations. The initiative also places significant emphasis on cooperative partnerships, positioning states as equal stakeholders in the planning and execution of projects ( Murugaiah et al., 2018).

The urbanization trend in India is escalating, and surpassing historical rates according to census data (2011). Given the centrality of urbanization in the city and national development, AMRUT’s objectives are structured around ensuring universal access to tap water with a guaranteed supply and sewerage connections for every household. Additionally, it aims to augment the aesthetic appeal of cities by fostering green spaces such as parks, mitigating pollution through the promotion of public transport, and facilitating non-motorized transport options like walking and cycling. AMRUT, serves as a mechanism to enforce key developmental areas. These initiatives mandate periodic reviews of approved projects to monitor progress, ensuring alignment with the central government’s plan for systematic implementation and sustained development.

The AMRUT program, inaugurated in 2015 with a visionary mandate to render all cities in India inclusive, sustainable, and citizen-friendly, has significantly influenced the nation’s urban development discourse. This flagship initiative, concentrating on enhancing fundamental infrastructure and living conditions within cities, particularly targeting low-income and marginalized demographics, warrants meticulous scrutiny to assess its accomplishments, impediments, and potential avenues for enhancement. With an allocation exceeding ₹73,000 crores invested in various projects as of January 2023, AMRUT stands distinguished for its commendable outcomes. Notably, the program has positively impacted the lives of over 5 crore individuals by ameliorating water supply and sanitation facilities—crucial components for public health and well-being. While AMRUT confronts inherent challenges, its demonstrable enhancement of the lives of millions of urban denizens, particularly those most susceptible, is evident. The program’s focused attention on core infrastructure, public spaces, and social equity establishes an admirable precedent for forthcoming urban development initiatives. The optimization of AMRUT’s impact hinges on addressing implementation gaps, ensuring an equitable distribution of benefits, and fortifying community engagement. These measures are pivotal in steering India’s urban landscape towards a more inclusive, sustainable, and citizen-friendly future.

The effective management of rapid urban expansion poses a tough challenge for policymakers in developing nations, such as India ( Sanchez and Govindarajulu, 2023). The complexity of this challenge is compounded by the fact that cities in a swiftly growing country like India formulate disparate policies and plans, often crafted by diverse agencies, occasionally with objectives that may be conflicting and contradictory. Therefore, it is critical to assess the progress and success of urban management schemes like AMRUT.

The diverse nature of cities and their inhabitants significantly complicate the scheme evaluation process. The fact that AMRUT aims to achieve diverse and sometimes conflicting goals, makes it very challenging to evaluate its effectiveness as a single composite indicator. However, it is imperative for the policy makers to comprehensively assess urban management schemes like AMRUT with the objective of ensuring best possible resource management and further improve such transformational schemes. Identifying this research gap, this study outlines a mechanism to quantitatively evaluate AMRUT scheme in a composite manner by combining all its parameters. The study proposes Multiple Criteria Decision Making (MCDM) methods as the basis to evaluate the priority of urban development plan AMRUT, which inherently possess a subjective and often unquantifiable nature and arrive at a composite indicator. The study then further suggests a mechanism to compare various cities under AMRUT scheme based on the composite indicator thus developed.

Numerous studies proposed composite factors as potential tools for evaluating such intricate and holistic plans ( Blanke, Chiesa, & Crotti, 2013; Croes & Kubickova, 2013; Zhou, Muambe, Deng & Selin, 2015). However, certain studies have raised significant concerns regarding the utilization of composite factors for urbanization plan evaluations ( Mendiola & Volo, 2017). Notable concerns include disparities in definitions ( Zhou, Muambe, Deng & Selin, 2015), the absence of a consensus on evaluation parameters ( Crouch, 2011), the selection of pertinent and efficacious parameters ( Mazanec et al., 2007), the oversight of geographical, economic, and market-related differences among diverse cities ( Croes, 2011), and a lack of clarity on the procedural establishment of composite factors ( Mazanec & Ring, 2011).

To fill this research gap, this study contributes to the literature in the context of urban management by evaluating AMRUT scheme and its impact on the applied cities. The research proposes that the impact of AMRUT can be studied by selecting a comprehensive evaluation model based on academic literature review, identifying an assessment method to assign criteria to the parameters of the selected evaluation model, and finally proposing a method to apply the weighted model to rank the cities covered under AMRUT scheme. Therefore, the contribution of this research is three-fold – selecting the evaluation model, identifying the assessment method AHP to operationalize the model by assigning criteria weights scientifically, and finally proposing TOPSIS as a method for ranking AMRUT cities by applying the weighted model.

Rapid urbanization imposes significant strain on infrastructure, systems, services, citizens, and the environment, necessitating innovative and sustainable solutions ( Caragliu, Del Bo, and Nijkamp, 2011). Smart Cities, as articulated by the British Standards Institution ( BSI, 2014), require a strategic integration of physical, digital, and human elements to ensure a sustainable and prosperous life for their citizens. Critical to the design and implementation of the smart city concept is the evaluation of suggested solutions and actions against a city’s sustainability ( Angelakoglou et al., 2020; Barsi, B., 2018; Caird et al., 2019; Shen et al., 2011). This evaluation process necessitates a common and shareable assessment model capable of measuring the effectiveness of interventions for smart city development and sustainable performance progress ( Huovila et al., 2019; Antolín et al., 2020). The development of such an evaluation model serves as a valuable tool for decision-makers, aiding in the assessment of smart city interventions and facilitating comparisons of their effectiveness across cities with diverse characteristics. Additionally, a comprehensive model can engage authorities and stakeholders early in the process, crucial for successful smart city solution implementation and raising citizen awareness about proposed changes.

Despite substantial investments and discussions on urbanization, little research has focused on evaluating smart city interventions and measuring outcomes for cities and citizens ( Bis, 2013). The absence of a common definition and a standardized measurement approach for evaluating factors influencing city performance has further hindered progress. Without a robust evaluation model, judging the success of projects like AMRUT becomes challenging.

The first contribution of this study is to identify a robust theoretical model that comprehensively outlines the significance of diverse criteria influencing residents’ decisions in the selection of a city. Through this objective, we construct a conceptual model that provides a methodical understanding of the complex factors pivotal in residents’ city selection processes. This conceptual model is envisioned to serve as a foundational instrument for subsequent phases of the evaluation, ultimately facilitating policymakers and urban planners in the creation of more attractive and livable urban environments.

The existing researches on evaluating a city can broadly be categorized into three segments based on their focus of study. A group of studies have focused on evaluating specific destinations like, Australia ( Dwyer, Mellor & Livaic, 2004), Hong Kong ( Li, Song, Cao & Wu, 2013), South Korea ( Kim & Dwyer 2003), Spain, Turkey ( Kozak, 2003), and Southern Italy.

The second set of set of studies have explored a few particular aspects of cities. Dwyer, Forsyth & Rao (2002) focused on price component of destinations. Hassan (2000) proposed four determinants of evaluating a destination with sustainability being the major factor. The four determinants included comparative advantage, demand, industry and environment based on sustainability approach. Li, Song, Cao & Wu (2013) examined urban centers in terms of economic returns based on demand elasticity analysis.

The third group of research effort has been to develop conceptual models that are not restricted to specific cities or parameters thereby being more comprehensive in nature. A review of literature shows that there are three such major models. Dwyer et al. (2004) proposed a model that integrates national and firm resources as shown in Figure 1. The first element of their model is a set of resources comprising of endowed resources (natural as well as heritage), created resources, and supporting resources. Their second core component is Destination Management including government and private industry. The model shows the interaction between resources as well as Destination Management with situational conditions. The result of the interactions influences the destination competitiveness as an intermediary variable and the socioeconomic prosperity of residents as the final output variable.

The second model by Heath (2002) presents an evaluation model in the form of a house with four components of foundations, cement, building blocks and roof. Foundations, like climate and culture, provide necessary requirements for a city. Cement integrates different aspects and includes communication channels and information management. Building blocks emphasize success drivers like a sustainable development policy and framework. Fourth and final element of the model is roof that comprises ‘people’ part of like community focus and human resource development.

Crouch and Ritchie (1999, 2006) and Ritchie and Crouch (2000, 2003) defined a conceptual model, based on Porter’s model (1990), for evaluating a destination shown in Figure 2, which is one of the most detailed work on the topic ( Ayikoru, 2015). The model by Ritchie & Crouch (2003) is based on both Comparative and Competitive advantage concept. Comparative advantage is related to eight ‘Endowment Resource’ which are available to a destination like culture, physical, human, knowledge, capital, infrastructure, tourism superstructure and historical. Competitive advantage, on the other hand, is related to deployment of these resources. These are inventory, growth, effectiveness, efficiency, and maintenance. The model also recognizes two distinct and interrelated environments under which tourism destinations work–micro and macro environment. The micro environment includes “members of the travel trade (i.e. tour packagers, suppliers, retail travel agents, specialty channel partners, and facilitators), tourism markets, competitive destinations, and a destination’s public or stakeholders (residents of the destination, employees of the tourism and hospitality industry, citizen-action groups, the media, financial and investment institutions, relevant government departments, and immediate neighborhoods)” ( Crouch & Ritchie, 1999). The macro environment comprises of externalities influencing the microenvironment like greater focus on natural environment, economic restructuring, changing demographics and rising complexity of technology.

The exhaustive model asserts that a city needs to be evaluated on five main components. The first of these components is core resources and attractors that comprise the primary appeal for staying in a particular destination like the climate of a city. The second component includes the set of supporting factors and resources that support the core resources, like accessibility to the city and the infrastructure. The third attribute focuses on destination policy, planning and development highlighting the importance of common understanding amongst stakeholders. The fourth dimension describes the role of urban management to enhance the appeal of core resources and attractors (e.g. marketing, availability of capital and human resource development). The final element consists of qualifying and amplifying determinants that have the capability to limit the scale or potential of the city. These factors could be its geographical location, safety and security, and the overall cost of living. The model further comprises of 36 sub-categories grouped under these five criteria ( Ritchie and Crouch, 2000, 2003).

This research proposes that the conceptual model of Ritchie and Crouch (2000, 2003) is an appropriate model to evaluate the effectiveness of AMRUT based on the perception of citizens. The first reason for selecting this model is its comprehensiveness and exhaustiveness accounting for almost all the factors ( Crouch, 2011; Li, Song, Cao & Wu, 2013). The second reason for selecting this model is that it is one of the most extensively cited model in the literature ( Crouch, 2011; Ayikoru, 2015; Botti & Peypoch, 2013). Third, this model has been widely adopted by researchers in the academic literature. Some of the prominent studies that based their study on the model are Cracoli and Nijkamp (2009), Dwyer et al. (2004), Enright and Newton (2004), Botti and Peypoch (2013), and Zhang, Gu, Gu & Zhang (2011). Finally, results of empirical studies like Enright and Newton (2004) have found support for the model. This study proposes to evaluate the effectiveness of AMRUT scheme on the five criteria and thirty-six sub-criteria adopted from the Ritchie and Crouch (2003) model as shown in Figure 3.

The previous section discussed the identification of the conceptual models based on a literature review to list the criteria and sub-criteria on which the AMRUT scheme can be evaluated ( Ritchie & Crouch, 2003). The review highlights that the evaluation process is characterized by an extensive list of multiple criteria that are mostly subjective and unquantifiable. As noted earlier, the evaluation process of choosing a model must be followed by selecting an assessment method to arrive at a composite indicator to operationalize the model. Having chosen Ritchie and Crouch’s (2000, 2003) conceptual model for evaluation, the study now focuses on identifying the assessment method. The assessment method should thus fulfill the objective of combining all the diverse criteria into one Composite Indicator (CI) with appropriate weights for each indicator. This CI would therefore be a mathematical combination of individual indicators that represent different dimensions of the AMRUT evaluation and provide stakeholders with a holistic picture.

Many recent studies have attempted to arrive at a CI to measure multi-criteria concepts ( Blanke, Chiesa, & Crotti, 2013; Croes & Kubickova, 2013; Enright & Newton, 2004; Tseng & Chen, 2013; Cracolici & Nijkamp, 2009). Once the CI is calculated, it can be used to assign different weights to the five criteria and thirty-six sub-criteria and applied for empirically testing the effectiveness of AMRUT schemes in the cities where it is implemented. Since multiple criteria need to be considered while operationalizing the model, the assessment approach should be Multiple Criteria Decision Making (MCDM) methods. MCDM is a general term for a group of quantitative methods to help decision making in problems where multiple criteria and alternatives are involved. As the evaluation of the AMRUT scheme by adopting Ritchie & Crouch’s (2003) model attempts to understand how a citizen perceives different aspects of urban management from a set of possible alternatives based on multiple criteria, the problem of assigning weights to these diverse criteria can be approached as an MCDM problem. In the present study of evaluating the AMRUT scheme’s effectiveness, when extensive criteria have been identified, it is relevant to give weights to these criteria ( Dwyer & Kim, 2003; Crouch, 2011). There are multiple ways in which weights can be assigned to these criteria. For instance, all attributes can be considered equally important De Keyser & Vanhove (1994). Some of the methods that can be applied to assign weights through quantitative research include Importance Performance Analysis ( Enright & Newton, 2004), simple descriptive analysis ( Tseng & Chen, 2013), factor analysis ( Gooroochurn & Sugiyarto, 2005), ranking method based on citizen response ( Garau-Taberner, 2007), statistical model of PLS_PM ( Mazanec & Ring, 2011; Esposito Vinzi, & O’Connor, 2014), and Information Entropy Weights ( Zhang, Gu, Gu & Zhang, 2011). The more recent methods to assign weights include Multi Criteria Decision Analysis (MCDA) method ELECTRE I ( Botti & Peypoch, 2013), Principal Component Analysis (PCA) ( Huang, Li, Zheng, & Li, 2006; Cracolici & Nijkamp, 2009), and cluster analysis ( Spencer & Holecek, 2007).

Analytic Hierarchy Process (AHP), one such MCDM method, first proposed by Saaty (1980), is a mathematical process in which all the factors involved in decision-making are structured in a tree hierarchy and assigned weights. AHP has been used to address decision-making in a number of interdisciplinary contexts where weight assignment to multiple criteria is the goal. However, despite its utility and relevance there seems to be no study that has applied AHP to operationalize multiple criteria in evaluating urban management schemes like AMRUT. Therefore, the present study proposes that AHP an MCDM can be applied for weight assignment of multiple criteria and sub-criteria to evaluate their relative relevance in urban management. This does not seem to have been attempted in urban planning literature until date, thereby filling a literature gap. A detailed description of AHP and how it can be applied to assign weights to the model have been provided in the next section.

The proposed study employs an integrated approach combining the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to evaluate the effectiveness of the Atal Mission for Rejuvenation and Urban Transformation (AMRUT) scheme in selected cities. The methodology consists of several phases:

Phase 1: Development of Hierarchical Structure using AHP

Step 1: Hierarchical Decomposition ○

Problem Definition: Define the main goal of the evaluation, which is to assess the effectiveness of the AMRUT scheme in various cities.

Step 2: Measurement Methodology ○

Pairwise Comparisons: Conduct pairwise comparisons among the elements within each level of the hierarchy. Respondents (citizens in this case) will compare each element against every other element at the same level in terms of their relative importance concerning the element in the upper hierarchy.

Step 3: Prioritization and Consistency Check ○

Priority Calculation: Calculate the priority weights for each element by normalizing the pairwise comparison matrices and solving the eigenvalue problem.

Phase 2: Evaluation Using TOPSIS

Step 4: Data Collection through Survey ○

Questionnaire Design: Develop a structured questionnaire to collect data on the relative importance of criteria and sub-criteria from citizens. The questionnaire will include definitions of each criterion and sub-criterion to ensure clarity.

Step 5: Application of the TOPSIS Method ○

Attribute Normalization: Normalize the collected data to ensure all attribute values are on a comparable scale. This step is crucial as TOPSIS requires commensurable units.

Step 6: Ranking and Analysis ○

City Ranking: Rank the cities based on their relative closeness to the ideal solution. The city with the highest score is considered the most effective in implementing the AMRUT scheme.

Phase 3: Integration and Validation

Step 7: Model Integration ○

Framework Integration: Integrate the AHP and TOPSIS methodologies into a coherent framework. Ensure seamless data flow and consistency between the two methods.

Step 8: Final Application and Reporting ○

Implementation: Apply the validated model to assess the impact of the AMRUT scheme in the selected cities.

After assigning weights to the criteria and sub-criteria of the model, it is proposed that the model can be applied to measure the effectiveness of the AMRUT scheme in the cities where it is implemented. This study proposes to use the weighted attributes for evaluating specific cities covered under the AMRUT scheme through the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method introduced by Hwang and Yoon (1981), offers a ranking approach in practical applications. Its core principle involves identifying alternatives that exhibit the shortest distance to the positive ideal solution and the greatest distance to the negative ideal solution simultaneously. The positive ideal solution aims to maximize benefit criteria while minimizing cost criteria, whereas the negative ideal solution seeks to maximize cost criteria while minimizing benefit criteria. TOPSIS utilizes attribute information to provide a cardinal ranking of alternatives based on the weighted criteria on which the different criteria are being evaluated without the requirement of preferences to be independent. The only requirement for the application of TOPSIS is attribute values are numeric and possess commensurable units which is fulfilled by the current study.

The survey designed for implementing the TOPSIS method is proposed to determine the optimal ranking of selected cities under the AMRUT Scheme, considering predefined criteria obtained from the Ritchie & Crouch (2003) model, and associated weights as calculated from the AHP survey. The TOPSIS calculation requires the respondents to rank each of the 5 criteria and 36 sub-criteria for all the cities where the effectiveness of the AMRUT scheme needs to be evaluated. The ranking shall be done on a Likert scale ranging from 1 to 5, where 1 represents “Not Important” and 5 signifies “Very Important”. The data collection process can be conducted through surveys. The seamless integration of Multiple Criteria Decision Making (MCDM) methods, a robust sampling strategy, and a structured questionnaire, informed by a comprehensive literature review, collectively establish a robust model for exploring residents’ perspectives on cities governed by the AMRUT initiative. The proposed integrated methodology of this study has been summarized and shown in Figure 4.

The speedy urbanization in India, pushed by initiatives such as the AMRUT, assumes a central and strategic role in the nation’s comprehensive development strategy. Launched in 2014, AMRUT is strategically aligned to enhance living standards through extensive urban renewal initiatives across 500 cities. Bearing a resemblance to smart city development, the initiative places a significant emphasis on private investments, long-term infrastructure improvements, and a genuine responsiveness to the needs of the citizens. Cooperative partnerships, treating states as equal stakeholders, are integral to the planning and execution phases.

In evaluating the sustainability and effectiveness of AMRUT from the citizens’ perspectives, this study leverages Ritchie & Crouch’s well-regarded conceptual model, recognized for its comprehensive approach to destination competitiveness. The research further proposes application of the Analytic Hierarchy Process (AHP) in decision-making and scientific weight assignment approach for assessing the appropriateness of AMRUT’s strategic criteria. The study suggests that surveys among residents in AMRUT-governed cities, utilizing a questionnaire structured around AHP’s principles shall help the policy makers assign specific weights to the diverse parameters of AMRUT. The analysis of AHP results shall reveal a robust consensus among decision-makers in prioritizing factors within the dimensions of Core Resources, Supporting Resources, City Enhancement Factors, and City Management Factors and the thirty-six sub criteria. Finally, the application of the weighted model to measure the effectiveness of AMRUT scheme in specific cities shall help the policymakers understand which cities are doing better on which all criteria as per the residents.

Implications for policymakers would include identification of critical factors and addressing service gaps to ranking cities based on project outcomes, redesigning projects, and formulating policies that consider the multifaceted influences on urban development success. On a societal level, the study offers profound insights into societal transformation, aids informed decision-making for individuals, identifies social requirements, and evaluates the sustainability of AMRUT projects from a socio-economic perspective. This research contributes to a comprehensive understanding of the impact of the AMRUT initiative on urban development, providing actionable insights for policymakers, fostering informed decision-making, and promoting sustainable and inclusive societal growth. The study underscores the intricate interplay of various factors in shaping urban development and emphasizes the indispensable role of citizen perspectives in accurately evaluating the success of ambitious initiatives like AMRUT.

Ethical approval and consent were not required.