Indra Bastian
Efa Agus Susanto
Sumiyana Sumiyana- Master of Science and Doctorate Programme, Faculty of Economics and Business, Yogyakarta, Indonesia
This article investigates the lack of certainty regarding the success of smart city development in the Government of Indonesia’s regions. The authors suspect that developing smart cities in each region ran independently and nationally, without a unifying strand of knowledge and information system leadership models. Based on these statuses and circumstances, this research examines these phenomena with two critical perspectives: The cognitive model of double-loop learning of smart city development and a dexterous strategy in terms of political economy. Consequently, this study finds that the regions developing a smart city act according to the statuses: Working with only fiscal budget readiness, cognitive models in the decisions to create future works, excessive caution about accruing potential future benefits, and null regulatory leadership in terms of guidelines. Thus, this study concludes that regions develop smart cities in their self-imagination without profound directional boundaries.
Introduction
Since the Indonesian Government at the central level (GoI) announced the development of smart cities in 2017, the medium-term plan has stated that 50 out of 100 smart cities in the region were starting to be built. However, implementing smart city development varies according to each region’s motivation (Mayangsari and Novani, 2015; Anindra et al., 2018a; Budy, 2018). Furthermore, this study indicates that smart city development is affected by regions’ freedoms of analysis and design, the IST type and platform developed, the management of their implementation, etc., On the other hand, the central GoI does not lead the regions with smart cities’ standardised ICT and IoT nationally and, at the same time, does not instruct them with strategic knowledge to accelerate these smart city developments. In other words, realising the smart cities, the GoI should invest in the knowledge endowment to be imparted to the regions through regional government chiefs and collective communities Ng et al. (2022); Pratama (2021); Yigitcanlar (2015). Thus, this study demonstrates that physical or non-physical development is realised more quickly when capitalised knowledge substantially precedes physical and legal aspects. This lack of clarity implied a void in terms of a genuine spirit to complete the development of smart cities (Bykova and Jardon, 2018; Offenhuber, 2019; Peter and Meyer, 2022). Finally, this study analyses the potential emergencies in the GoI’s smart city development without strategic knowledge to accelerate smart city realisation (Lerro and Schiuma, 2009; Centobelli et al., 2017; Džupka, 2021). The lack of knowledge is the partiality of deceit that becomes agility, which is wrong (Offenhuber, 2019; Pratama, 2021; Kusumastuti et al., 2022), and a lack of understanding to accrue future potential benefits based on a definite political economy (Purwanto, 2018; Appio et al., 2019; Ahmad et al., 2022).
This article demonstrates that many smart city developments are possibly stalled (i.e., not immediately realised) due to the GoI’s clandestine leadership supported by a lack of knowledge informing the transformation process (Israilidis et al., 2021; Pratama, 2021; Okafor et al., 2022). This clandestine leadership is the first uniqueness of this study, with evidence of the various smart cities being developed differently in various regions. Thus, this study suggests variations in managing smart city development projects. Furthermore, it reveals that the GoI did not issue an intelligent city development model as a tool to systemise control of this development (Firmanyah et al., 2017; Anindra et al., 2018b; Herdiyanti et al., 2019). Therefore, this article shows that the immediately unrealised smart city developments were solved when the central GoI shared congruent knowledge with the regions (Tan, Taeihagh, and Sha 2021; Tan and Taeihagh 2020; Yigitcanlar et al., 2019), supporting the regions’ chiefs through their cognitive models. Consequently, the GoI has to dismantle its clandestine leadership for the control systems that always actively diagnose smart city development progress (Shelton et al., 2015; Silva et al., 2018; Yoshida and Thammetar, 2021). Next, when the GoI uses command leadership to replace clandestine ones, the regions’ chiefs can construct their cognitive models (Donaldson and Luo, 2014; Chen et al., 2018; Džupka, 2021), which then get feedback to correct these development projects. Finally, this study concludes that smart city development is immediately incomplete because the regions are not equipped with cognitive models due to GoI’s clandestine leadership, which does not place knowledge supremacy as part of the transformation process.
Furthermore, regarding the regions’ cognitive model, this study shows that each region gains capabilities and competencies to provide counterfactual reasoning in developing smart cities (Tay et al., 2018; Mahesa et al., 2019; Parlina et al., 2019). In other words, the regions act to develop smart cities with their double-loop learning as a consequence of a cognitive model. Then, they acquire intellectual endowments because of the knowledge transformation from central GoI that are used to adapt their smart city development to fit with the environment and the institutional and work culture. On the other hand, the regions’ double-loop learnings allow its chiefs to compare their projects to normative denominators or other smart city developers (Mohamed et al., 2006; Lerro and Schiuma, 2009; Offenhuber, 2019). Finally, the authors believe that double-loop learning ascertains the regional chiefs’ institutional efficacy due to feedback regarding improved decision-making (Kayaga et al., 2013; Kusumastuti et al., 2022; Okafor et al., 2022). Moreover, the quality of smart cities built by the regions would be better due to their chiefs countering the production progress.
The second uniqueness of this study is the analysis of the GoI’s implementation of a dexterous strategy (or not) for smart city development (Susanti et al., 2016; Syalianda and Kusumastuti, 2021; Hasmawaty et al., 2022), which is sequentially related to its political economy. Specifically, this study highlights whether the central GoI implements dexterities in smart city development by involving multiple resource-based views or just a single one (Moreno et al., 2009; Pettit et al., 2018; Offenhuber, 2019). Furthermore, this study argues that the central GoI should utilise multiple resource-based or dexterous strategies to ensure that the investments in smart city development are not a paradox in terms of investments in the future (Shelton et al., 2015; Mouazen and Hernández-Lara, 2021; Pratama, 2021). On the other hand, the authors analysed the central GoI’s political economy to accrue the future potential benefits of smart cities to be capitalised on as an initial investment (Appio et al., 2019; Israilidis et al., 2021; Kusumastuti et al., 2022). The authors also identify that the central GoI enables this smart city development to have a welfare function for society (Shelton et al., 2015; Pratama and Imawan, 2019; Pratama, 2021) or just a societarian investment. Then, this article argues that when the GoI dares to capitalise on the future economic benefits of smart city development, the GoI takes political economy action correctly because it is really for the communities’ prosperity in the future with low processing costs for each living activity.
This research contributes to the awareness of knowledge mastery and its transformation process to work on smart city development projects or others. Therefore, the authors argue that the GoI’s smart city development should be about knowledge supremacy (Bosch-Mauchand et al., 2013; Yigitcanlar et al., 2019; Tan et al., 2021) as a function of constructing the cognitive agents involved. Therefore, this article considers constructivism which proposes that knowledge is the primary driver for successful development. Thus, when the regions’ chiefs and their communities have capitalised on constructive knowledge for smart city development, its process and implementation are efficiently controlled and effectively achieved (Mohamed et al., 2006; Silva et al., 2018; Appio et al., 2019). Moreover, during the development period, the GoI’s agents became highly dynamic because of their higher ability to use counterfactual reasoning to find the best solutions for smart city problems (Yigitcanlar, 2015; Silva et al., 2018; Pratama, 2021). Otherwise, the GoI’s smart city development process will be dogmatic even if what is dominantly transformed is only mechanistically administrative regulations and compliance with budgeting procedures (Mintrom and Luetjens, 2016; Pereira et al., 2018; Pettit et al., 2018). On the other hand, knowledge mastery becomes the capital for the GoI’s agents to succeed in developing smart city projects because they are in cognitive states within a cognitive model that can provide feedback and evaluate deviations from the supposed truth. Likewise, the GoI’s agents get the dynamic learning to adopt various alternative solutions.
The second contribution of this study is to formulate the validity of the smart city development that the GoI is undertaking to succeed in the future. The validity of smart city development is valid when the GoI has a strategy of dexterity, which utilises double- or multi-resource-based approaches to ensure success (Dencker and Gruber, 2015; Wanzenböck and Piribauer, 2016; Tan and Taeihagh, 2020). Likewise, the double- or multi-resource-based approach eliminates the IT investment paradox where smart city development incurs very high costs, and the development work becomes protracted. Furthermore, the validity of smart city development is properly complemented if the GoI accrues future economic benefits (Mohamed et al., 2006; Chen et al., 2018; Pratama, 2021). In other words, the GoI carries out a political economy by utilising smart cities as a function of the community’s prosperity for the future. The authors argue that the investment validity of the smart city development made by the GoI plays a “polity” in which the community gets definite benefits, and the GoI bears the concentrated costs. In brief, this article underlines that the investment motive for developing smart cities of the central and regional GoI is in the validity of public interest (Bykova and Jardon, 2018; Suartika and Cuthbert, 2020; Kwak and Lee, 2022) or something else.
This study’s critical perspective
Cognitive models of double-loop learning for smart city development
Organisational intellectuality, as a collection of individuals’ tacit knowledge, supports the development of smart cities. Then, this article obliques to induce cognitive models into smart city development, showing the knowledge capabilities needed to accommodate decision-making and create problem-solving experiences (Dencker and Gruber, 2015; Pratama, 2021; Mangindaan et al., 2022). Furthermore, as a consequence of the cognitive model, double-loop learning facilitates decision-making by changing the work methodologies to become more optimal. On the other hand, the authors explain that double-loop learning transforms simple and static understanding into a broader and more dynamic one (Moreno et al., 2009; Kim et al., 2017; Chen et al., 2018). Moreover, double-loop learning can respond when receiving feedback information and increase optimisation in decision-making. Thus, this study argues that smart city development should adopt individual and organisational cognitive models and double-loop learning. Thus, the GoI empowers its regions to innovate smart city development and further realises it with the certainty of induced cognitive models and double-loop learnings. Hence, these cognitive models and double-loop learning inducements certainly guarantee the process of realising a smart city development.
This study focuses on the fact that smart city development projects are not immediately realised due to the incomplete knowledge of the cognitive model (Mohamed et al., 2006; Džupka, 2021) and the absence of double-loop learning (Tan et al., 2021; Yoshida and Thammetar, 2021). Furthermore, the GoI did not conduct a knowledge of the transformation process for regions to build smart cities or did not emphasise regions’ knowledge mastery (Retna, 2002; Lerro and Schiuma, 2009; Purwanto, 2018), especially for the formation of mental models which have consequences for double-loop learning capabilities. However, this article highlights that the GoI tends to command the development of regions’ smart cities with mechanical public administration and compliance with budgetary evaluations. Furthermore, this mechanical system has a consequence that creates a void and the absence of feedback information, impacting regions’ low expertise to realise a smart city (Silva et al., 2018; Mouazen and Hernández-Lara, 2021; Pratama, 2021). Thus, this study reveals that smart cities are not immediately realised in various regions because they do not place knowledge supremacy as capitalised cognition but only mechanical administrative and budgeting evaluations. Hence, the regional governments, leakages in the incompleteness of the cognitive model and the void in terms of double-loop learning, cannot achieve dynamic flexibility to realise smart cities.
Dexterous strategy in political economy
This study considers a manual dexterity concept (Bosch-Mauchand et al., 2013; Mintrom and Luetjens, 2016; Sobinov and Bensmaia, 2021), revealing that regions should construct dexterous strategies for developing smart cities programmes. Moreover, in viewing various urban problems, local leaders should be able to activate their neural mechanisms along with fundamental smart cities knowledge, creating the optimum strategies for solving those problems and enhancing society’s prosperity. Furthermore, because regions have primary responsibility compared to the GoI, they should use functionalised all existing resources, producing the most professional approach and deploying the best ICT knowledge through their smart cities programmes. Consequently, they interactively stay ahead of the dynamisation curve of political economy phenomena via their groundbreaking strategies (Purwanto, 2018; Suartika and Cuthbert, 2020; Yoshida and Thammetar, 2021). However, the authors argue that all regions’ poor progress in smart city programmes is caused mainly by the vagueness of the region itself in conceptualising a smart city project as an ongoing IST project rather than a mission to streamline all society activities. Even worse, the GoI has not yet accommodated a clear national roadmap for these 100 smart city projects, leading them to actualise the smart city building project blindly and endlessly (Deng et al., 2021; Džupka, 2021; Rachmawati et al., 2021). Thus, this study demonstrates that a dexterous mindset of all regions’ chiefs is critical to running smart city projects. Hence, the regions have their decentralised autonomous organisation and society’s responsibility, simultaneously executing political economy. Likewise, the GoI has done this too.
Consequently, the GoI could further build rigorous policies, guiding and controlling smart city intercity projects. Furthermore, each region utilises its resources optimally by using those policies as an anchored institutionalisation in developing smart city programmes (Retna, 2002; Caprotti et al., 2017; Mangindaan et al., 2022), reaching the national-smart city’s mission integratively (Yigitcanlar 2015; Pettit et al., 2018; Appio, Lima, and Paroutis 2019). Moreover, the punctilious policy of the GoI resulted from its cognitively dexterous strategies, which determined the parameterised national production and consumption adaptively. In particular, in each GoI-graded relationship, welfare achievement targets could continue to grow, involving all agents at the regional and central levels (Mouazen and Hernández-Lara, 2021; Rachmawati et al., 2021; Okafor et al., 2022). Therefore, this study highlights that the implemented dexterity principle by the GoI led to the establishment of collective knowledge, encouraging all regions to work on smart city development properly. Consequently, when the Central GoI bundles all smart city projects, all regions could run in an aligned political economy (Shelton et al., 2015; Purwanto, 2018; Suartika and Cuthbert, 2020) due to disparity of thoughts. Furthermore, all smart city projects fulfil the essential missions of the smart city’s political economy rather than as mechanistic administrative IST projects. Thus, all actors involved in smart city development concurrently run political economies generated from a high-minded smart city roadmap.
Proposition developments
This article develops propositions that would be investigated in terms of the actual activities of central-regional governments and all agents involved in smart city development. The deep contextual design of this study is the development of smart city projects for the regions concentrating on mechanistic administrative and budget fulfilments. GoI stressed that the development of smart cities does not emphasise the role of substance knowledge, which is when the regions’ chiefs capitalise on this knowledge. Then, this knowledge is helpful for the smart cities’ settlement process. Furthermore, this article indicates that the GoI did not innovate with dexterous strategies, which were then induced to the regions’ and communities’ involvement because of uncontrolled boundaries of this intangible knowledge (Mohamed et al., 2006; Wanzenböck and Piribauer, 2016; Israilidis et al., 2021). Therefore, this study indicates that the GoI implements the knowledge of the transformation process (Centobelli et al., 2017; Deng et al., 2021; Džupka, 2021) and the ability to measure output (Kayaga et al., 2013; Džupka, 2021; Ng et al., 2022) on the smart city development project with the clandestine approach. In addition, the unclear measurement and control of smart city development are due to misplaced philosophical concepts and theoretical knowledge (Caprotti et al., 2017; Appio et al., 2019; Kusumastuti et al., 2022) that should be transformed into the regions’ chiefs. Likewise, the authors highlight that a smart city’s development is equivalent to software application development. Furthermore, this study searches for the underlying inquiries of the realm-validated phenomena. Thus, we develop the propositions below.
P1: Does GoI treat regions? Through chiefs and communities involved, do substance knowledge sharing focus on underlying philosophies for smart city development or just ICT and IoT knowledge?
P2: How does the GoI’s knowledge sharing improve regions’ capacities and competencies with a cognitive model, double-loop learning, dexterity, and political economy to ascertain the succession of smart city development?
P3: How does the GoI measure and control these regions’ smart city developments through an innovative achievement or mechanistically administrative and budget absorption performance?
P4: Why does the GoI not measure and control these regions’ achievement levels with incremental welfare criteria in smart city developments?
Research method
This study collects data from the GoI’s regulations or policies and the websites of regions developing smart cities and confirms them with competent participants related to implementing smart city development projects. Furthermore, this study analyses the content of a set of regulations, information on websites and social media, and inferences from in-depth interviews. On the other hand, the authors randomised 20 smart city development projects from 98 regions that carry out development projects, obtaining an assessment from Aptika Kominfo in 2021. In addition, the authors consider the Ministry of Communication and Information assessing the project completion. Moreover, we select our sampling randomly based on this ministry grading system of A, B, C, and D, presented in Supplementary Appendix Table 1A. Furthermore, this research used the Ministry of Communication and Information grading system for 2020–2021. In addition, we show the grading and trend of these project development, presented in Supplementary Appendix Table 1B. However, we noted that there are no data for 2017–2019 due to the time for planning design.
Furthermore, this study has selected participants from GoI, the Ministry of Communication and Information, regions, ICT consultants, and the GoI’s internal auditor. The authors select research samples randomly from the west, central, and east Indonesian areas. In addition, we consider public administration types of regency and city, presented in Supplementary Appendix A. We collected primary data with triangulated sources that are masterplan, grading and scoring systems, and other related documents. In addition, we also design a triangulation of participant expertise from central GoI, regencies, cities, and smart city consultants. Meanwhile, we analyse the contents using a qualitative method referring to this research’s proposition. Then, in analysing data from various sources, the authors strive to achieve the best mutually confirming data collection to achieve reliability, validity and credibility (Garside, 2014; Kaczynski et al., 2014; Lowe et al., 2018). Finally, we finalise the data collection and then shift to analysis and reporting using the highest critical reasoning when it reaches saturation (Rowlands et al., 2016; Sebele-Mpofu, 2020). Hence, we depict participants as giving valid confirmation of this study’s content analysis and whether it is true or false. Finally, we are concerned about the data credibility through the participants collected, as presented in Table 1 below.
TABLE 1. Data on participants.
The authors collected data from all legislation and regulations, websites from various regions developing smart cities, and in-depth interviews with participants concentrating on the knowledge transformation by the GoI for the regions, especially in cognitive models, double-loop learning, dexterous strategy and political economy. Furthermore, this study breaks down the four primary constructs into subdimensions and detailed subdimensions with detailed questions (Moreno et al., 2009; Kayaga et al., 2013; Kim et al., 2017). The detailed questions for the cognitive model and double-loop learning focus on information feedback from the implementation of smart city development projects that can drive the decision-making process and improve the cognitive model (Džupka, 2021; Israilidis et al., 2021; Ahmad et al., 2022). Furthermore, the details of the questions on the dexterous strategy are related to the attachment of the multiple strategies carried out by the regions to ensure the success of all cities’ development (Shelton et al., 2015; Pettit et al., 2018; Appio et al., 2019). Furthermore, the concept of this strategy relates to the symptoms and consequences of the political economy (Purwanto, 2018; Pratama and Imawan, 2019; Suartika and Cuthbert, 2020) or is it just an IST development project? Finally, this study integrates the answers from all participants on the four constructs to condense them into research analysis and discussions.
Analysis and discussion
Development within single dexterity
This study has identified that smart city development using single dexterity in each region or city efficiently serves society. Moreover, this development is not treated as an ICT investment (Mwaniki, 2017; Rao and Prasad, 2018; Shin et al., 2021) and does not comprehend cognitive behavioural therapy for society (Ning and Liu, 2015; Jiang et al., 2020; Nikki Han and Kim, 2021). Thus, smart city development is solely an ICT product not accompanied by knowledge domains that society should consider (Lin et al., 2011; Ning and Liu, 2015; Rana and Dwivedi, 2015). The authors then present some transcripts below.
We, as human resources for regions and cities, accept the work of developing this smart city project as one activity to engage in it. But, more profoundly, we are not accommodated with the control and measurement of whether this is an ICT investment or cost-burdened. Therefore, the better we should do, the more knowledge Central GoI supports us, such as cost reduction strategy, information failures, programming interconnection, deep learning, etc., (P-05; 13’; P-02; 9’; P-01; 23’).
We are in deep agitation or nervous anxiety that the smart city development project will become an investment paradox. We believe this project works on ICT development without comprehending knowledge sharing, learning orientation, social influences, etc., for regional society’s learning (P-05; 60’; P-02; 54’; P-04; 3’; P-08; 9’).
There are no determinative outputs and outcomes when we would be measured with mission accomplishment in this smart city development (P-07; 63’; P-01; 25’; P-08; 57’).
This research shows that investments in information system development are usually equipped with ambi- or multiple-dexterities that aim to ensure the success and usability of the application (Dencker and Gruber, 2015; Wanzenböck and Piribauer, 2016; Offenhuber, 2019). Likewise, investing in smart cities does not become a futile activity with uncertainty regarding the generation of returns, better known as the investment paradox (Bibri and Krogstie, 2017; Edelenbos et al., 2018; Yigitcanlar et al., 2019). Ensuring the investment paradox does not occur, the management of the information system equipped this ICT product with ambi- or multiple-dexterous strategies (Wanzenböck and Piribauer, 2016; Edelenbos et al., 2018; Mouazen and Hernández-Lara, 2021). For example, the controller conducts dual- or triple-emphasises strategies, cross- or multi-platform, users’ learning fit systems, knowledge repository, reciprocity booster, etc. However, the authors find that comprehensive strategies have not equipped smart city developments in several regions and cities, especially in knowledge-sharing disseminated to communities. Moreover, we believe that regencies or cities should influence local communities to ascertain that this smart city application program could reduce the costs of effort, time, and resources (Lin et al., 2011; Ning and Liu, 2015; Nikki Han and Kim, 2021).
On the other hand, this study reveals that without strategies implemented in managing smart cities, the GoI’s instructions to develop and practise them are not measured and controlled determinatively (Shen et al., 2018; Sharifi, 2019; Bisello, 2020). Then, the authors infer that the GoI orders smart city development without profound outputs, especially for what society and its administrative process should be transformed (Huovila et al., 2019; Sharifi, 2019; Kumar et al., 2020). Therefore, we argue that a smart city is an information system application program oriented to serve society’s needs (Lin et al., 2011; Ning and Liu, 2015; Jiang et al., 2020). From other perspectives, developing smart city applications did not disrupt the former administrative process for local citizens to new defragmented activities promising cost reductions (Appio et al., 2019; Kakderi et al., 2021; Nikki Han and Kim, 2021). Thus, we infer that regencies or cities did not act as disruptors of existing administrative processes, innovating these processes efficiently and social welfare congruently (Serey et al., 2020; Ramirez Lopez and Grijalba Castro, 2021; Okafor et al., 2022). Finally, we reveal that developing a smart city with an unclear mission to be accomplished would not move local citizens to be involved and used as a learning repository (Ma et al., 2018; Tomor et al., 2019; Zhao et al., 2021).
Cognitive models based on regions’ or cities’ fiscal budget capacity
The authors argue that decision-makers rely on rules of decision-making, feedback and feedforward information to determine what they will do (Greene et al., 2001; Cornelissen et al., 2013; Truelove et al., 2014). By contrast, decision-makers could make a decision that disregards the rules and information. Hence, this research collects transcripts of how a smart city development is implemented.
Most regions and cities received instructions to develop smart cities and technical guidance to create master plans. We were chosen not based on data and information in our area for smart city development. However, we did a smart city development project because of the fiscal capacity of our regional budget to finance this project (P-05; 27’; P-02; 14’; P-01; 6’).
Due to Central GoI’s instruction to build, we are doing a smart city project. However, we did not yet collect data and information for analysing, designing and developing it. We, moreover, recognise that this project is not a usual program, as shown by the disguised mission and goal (P-05; 13’; P-02; 83’; P01: 3’).
This study successfully identifies that the regional leaders’ cognitive model for smart city development is determined by fiscal capacity. The realisation of the smart city, in the context of budgeted programs, articulates the regional leadership missions that have been budgeted for in this fiscal capacity. This regional cognitive model implies the absence of comprehensive rules for decision-making, feedback and feedforward information (Chen et al., 2018; Džupka, 2021; Pratama, 2021). In addition, at the same time, regional leaders are more responsive to this smart city development by labelling an existing ICT development of e-government. Meanwhile, the GoI’s mental leadership in carrying out this smart city development program can be classified into instructive characteristics (Cruz and Camps, 2003; Yiing and Ahmad, 2009; O’Donovan et al., 2021) without financing the budgeted costs. Thus, the authors infer that the GoI’s and regional leadership’s cognitive models have an output-based orientation (Ichniowski et al., 1996; Considine, 2002; Ryan and Walsh, 2004). Thus, the authors highlight that they are in adverse accountability and responsibility due to not being process-based (D’Agostini et al., 2017; Pichler, 2012; Tagliabue et al., 2020).
As for the other perspective, the authors reveal that most regions and cities received the smart city development project with no opportunities to diagnose their short- and long-run capacities (Appio et al., 2019; Israilidis et al., 2021; Ahmad et al., 2022). In other words, regions and cities tended to comply only with the GoI’s instructions. Consequently, the authors demonstrate that the smart city development would be no expectation for a significant change due to the absence of synergy between the GoI and the regions or cities. Thus, both exhibit adverse cognitive models.
Excessive caution about accruing potential future benefits
This study indicates that smart city development was an instruction from the GoI that is not based on regional or municipal diagnostics. Meanwhile, agents in regions and cities developing smart cities have no obligation to conduct analytical reviews of prospective social denominators eligible to be transformed and accrued (Smith and Cooper, 1994; Cruz and Camps, 2003; O’Donovan et al., 2021). Finally, the authors collected the following participants’ transcripts.
Some regions’ smart city developments construct the current needs for service to society without capturing foresight information. Thus, smart city investment is treated by regions or cities without considering such capital budgeting, discounting future benefits to calculate financial, social and political returns (P-07; 43’; P-03; 97’; P-01; 14’).
As technocrats, smart city development projects in Indonesia did not build with unclearly staged elevations. So, where are we standing on these smart city developments now? How will we enhance the adaptive capacities of the smart city? From the perspective of analytical review, what have we done in the “signified” and “signifier” nodes in developing a smart city? These question answers will always be unclear (P-05; 15’; P-04; 2’; P-01; 87’).
The authors depict the problems of smart city development that regions and cities carry out based on instructions from the GoI. Meanwhile, the instructions are the regency’s and city’s duty to develop smart cities’ applications of e-government, society, living, environment, economy, or tourism for enhancing social prosperity. On the other side, this study argues that the directed instructions are valid when the content domains are laid out as sequential knowledge used to conduct a perfect transformation process (Lewrick et al., 2010; Kantabutra, 2020; Hai et al., 2021). Furthermore, this process should order regions and cities to conduct an analytical review first to ensure a focused, smart city. In addition, regions’ and cities’ analytical reviews must formulate their adaptive capacity as a driver, trigger, accelerator and disruptor to the ongoing regional administrative processes to be transformed into ICT-defragmented ones (Lin et al., 2011; Rana and Dwivedi, 2015; Kumar et al., 2020). Thus, developing a smart city with an analytical review of a region or city becomes more certain to serve the community’s social needs.
From the financial, social and political budgeting points of view, smart city developments do not consider the process of accruing potential future benefits that are to be obtained (Purwanto, 2018; Israilidis et al., 2021; Pratama, 2021). Thus, the development of smart cities should capture the need for foresight information mapped into designated signifiers that are useful to transform society’s beliefs, attitudes and behaviours. Furthermore, the authors highlight that the development of smart cities in Indonesia does not prioritise knowledge supremacy, which can shift towards a new equilibrium balance. We emphasise that the success of smart city development depends on the knowledge and faithfulness of appropriation to society structuration to become more prosperous in the future (Shelton et al., 2015; Ramirez Lopez and Grijalba Castro, 2021; Okafor et al., 2022). Thus, this smart city development must be in the knowledge’s saddle-point to increase the community’s prosperity, not merely the development of information system applications. Therefore, this study demonstrates that the GoI commanding regions and cities to develop smart cities enriched by perfect knowledge transformation is the proper order. Hence, regions and cities, supported by knowledge supremacy in developing smart cities, could accrue future benefits for communities compared to inferior ones.
Null regulatory leadership to guideline
Regulations containing control and measurement information would lead to the successful development of smart city projects (Vitunskaite et al., 2019; Rochet and Belemlih, 2020; Ismagilova et al., 2022). Meanwhile, smart city project leaders define the ordered activities. These activities usually show path dependencies and construct a series of induced control and measurement tests. The series of activities direct projects agents not to distort procedures. Thus, smart city developments need genuine regulatory leadership, directing and leading fieldwork and outputs. Some participants’ transcripts are below.
Some of us understand that smart city development is in the context of managing information systems. However, Central GoI instructed us to build this project without clarity regarding creating social values, knowledge acquisition, collective cognition of social balancing programs, etc. Moreover, conceptual frameworks benefit the future community’s welfare (P-04; 12’; P-03; 42’; P-08; 17’).
We were ordered to develop a smart city, but the order was not comprehensive regarding information system development standards, such as IEEE, ISO, etc. Likewise, the results of developing this smart city system in the realm of multi- or cross-platforms, what are the uses for making decisions? What is the required data interconnectivity with other ICTs? etc., (P-07; 33’; P-05; 17’; P-01; 46’; P-08; 93’).
We criticise the development of this smart city project because it is carried out centrally from Central GoI and then distributed to all regions and cities. As a result, development with a choice of 50 regions and cities consumes enormous development costs, the future benefits of which cannot be achieved optimally (P-01; 22’; P-04; 36’; P-02; 8’; P-01; 58’; P-08; 49’).
When Central GoI, the Indonesian President, directs to accelerate national digital transformation programs. Most relevant ministries generally respond to these directions, although they are still in a future isolated operating with others or building-blocked silo of knowledge. Consequently, Central GoI faces difficulties innovating regulatory leadership to realise these directions in nationally united-regulatory action (P-04; 22’; P-06; 31’; P-01; 50’).
This study finds that there is currently null regulatory leadership for smart city development through participants. Then, it underlines this null regulatory leadership in managing smart city development and standardising information system specifications (Gupta et al., 2019; Lytras et al., 2021; Sharif and Pokharel, 2022). Consequently, the developments of agents interpret various perspectives in these two disciplines. For example, the regional agent’s development focuses on an ICT application’s short-run smart city capacity that ignores interconnectivity and interoperability between dimensions of smart e-government, living, economy, tourism, environment, and society. Moreover, agents’ developments tend to escape from complex and dynamic designs of information systems to simple ones such as innovating cross- and multi-platforms, mobile and desktop networks, supportively extensive data analysis, etc. On the other hand, the authors noted that massive smart city developments numbering 50 make the regulatory impetuses diverge. Therefore, this study reveals that regions’ and cities’ smart city developments produce unstandardised applications due to null regulatory leadership in the construction process.
From the principal perspective, the GoI did not lead smart city development with fixed regulatory leadership (Gupta et al., 2019; Ben Yahia et al., 2021; Sharif and Pokharel, 2022). In addition, this study highlights how the embryo of smart city development was originally from a digital transformation. Therefore, this study underlines that GoI’s null regulatory leadership for smart city development has emerged from the unprioritised agenda. The authors also highlight that the GoI has never discussed smart city development, implying no regulations issued by law or in lieu of laws. Meanwhile, the authors note that regions’ and cities’ executive heads signalled they would not issue related regional laws following the GoI policy of prioritised programs because of the knowledge they gained on international best practices. Therefore, in the case of smart city development, the GoI will not issue regulations whenever various regencies and cities translate modern ICT with their respective backgrounds.
Concluding remarks and implications
Overall, this study finds four condensations for the development of smart cities: the application of single dexterity, a cognitive model based on fiscal capacity, excessive caution about accruing future potential benefits, and null regulatory leadership in terms of guidelines. These four findings show that smart city development does not innovate based on regions’ or cities’ adaptive structuration and dynamic capability. Therefore, this study indicates that the development of smart cities in Indonesia does not demonstrate the existence of “signified” underlying factors and “signifiers” for driving benefits in the future. Finally, this study notes a void of knowledge for managing smart city development and ICT standards as parameters for making information system applications. Therefore, this research reveals that smart cities developed by regencies and cities are not characterised by the faithfulness of appropriation to administrative needs that transform society to become more advanced and prosperous.
From the dynamic capabilities perspective, smart city development should consider that communities change smoothly over time, and information system applications must capture this change. In other words, smart cities should have dynamics that capture the dynamics of community change in living social orders. Moreover, the community ecosystem changes towards a new shifted balance. These new ecosystems are where the role of smart cities, through information system applications, accelerates the achievement of this shifted balance with additional smart-distinctive characteristics. However, the authors reveal the dwarfing of smart-distinctive characteristics into a mere application tool of information systems. Finally, the findings of this research demonstrate that smart city development is just a utopian idea, as if drawing an equivalence between global issues and sustainability.
This study implies that the GoI needs to carry out administrative transformation processes. First, it acknowledges that the GoI is experiencing the voids of knowledge endowment in smart city development that have become far from the community’s dynamic structuration. Moreover, the GoI has not decided that smart city development is a core budgeted program but is an additive one instead. In addition, the authors have been able to show there is an antagonistic dualism between comprehensive development and partialised fallacies. In other words, this study infers that the ongoing smart city development is only a perfunctory concept rather than accentuating knowledge. Furthermore, due to partialised fallacies and perfunctory concepts, the smart city will still lead to a lively atmosphere of conflict, moving away from a comfortable life, resulting in a tough city. Thus, the developed smart city still becomes traditional information system applications because it cannot prevent residents from not keeping up with the punctual times. Next, most residents cannot use a proper information system application, causing regencies and cities to be unfaithful in appropriation and untransparent to community members. Finally, partialised fallacies and perfunctory concepts will probably promote other criminal behaviours, and bureaucratic corruption will thrive.
Second, this research notes that the central GoI controls the development of the smart city with poor management. Nevertheless, it has still received assurances from cities or regions that it will comply with its directives. On the other hand, the GoI’s control remains full in terms of administrative disciplines. In addition, this administratively focused control directs no enhancements of local people’s welfare but tends tendentiously according to a political agenda. Consequently, this study infers that massive developments of smart cities in 50 regencies and cities would produce ICT pedantry concerning applications, which is impressive but does not increase people’s belief and improve behaviour in terms of more social welfare. Finally, this study implies a need to transform the deficiencies of GoI’s knowledge management into a comprehensive development for society’s economy with genuinely dynamic capabilities.
Limitations
This study has fundamental weaknesses, which are described as follows. Firstly, it does not measure governmental agent readiness for regencies and cities. This weakness was identified after completing this research, which did not simultaneously analyse and identify governmental agent readiness and cognitive models. Consequently, this study opens up a new opportunity to develop future research that adopts governmental agent readiness, general or multidimensional commitments, and readiness for change for human resources in each regency and city. Secondly, this study does not discuss the unfinished completion of Indonesia’s population and land information systems. Likewise, it recognises that smart cities are highly dependent on these two information systems as identification and internalisation functions for the underlying information that realises the accomplished mission of smart city development. Moreover, the identification refers to regencies’ or cities’ needs for smart city development, such as no need, enforced to build and high necessity. Meanwhile, internalisation means that regencies or cities must accrue the need for realising forward-looking orientations. Therefore, future research becomes even more interesting when the central GoI has a mission and goal to solve problems related to a smart city but is handicapped by the absence of these two fundamental information systems.
The authors did not conduct to asses governmental agent maturities in high technology and management systems. Instead, this maturity level emphasises the collective knowledge of governmental agents’ human resources capabilities in machine learning, big data analysis, artificial programming interface, artificial intelligence, deep learning, and blockchain system. Thus, future research would be interesting whether it measures the governmental agents’ capability maturities. Moreover, other future research could focus on the emptiness of the Indonesian Finance and Development Supervisory Agency, assessing technological and managerial capabilities-level assurances for each government agency. Furthermore, the authors underline that this Supervisory Agency did not, so far, innovate this measurement. Finally, future research would be fruitful when it investigates low incentives for the Supervisory Agency and related ministries to make standards of the technological capacities and the managing information systems for each government agency nationally.
Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbuil.2022.1065652/full#supplementary-material
References
Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., and Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Comput. Sci. Rev. 43, 100452. doi:10.1016/j.cosrev.2021.100452
Anindra, F., Supangkat, S. H., and Kosala, R. R. (2018a). “Smart Governance as smart city critical success factor (case in 15 cities in Indonesia),” in 2018 International Conference on ICT for Smart Society (ICISS), Semarang, Indonesia, Oct. 2018 (IEEE).
Anindra, F., Warnars, H. L. H. S., and Min, D. M. (2018b). “Smart city implementation Modelling in Indonesia with integration platform approach,” in 2018 International Conference on Information Management and Technology (ICIMTech), Jakarta, Indonesia, Sept. 2018 (IEEE).
Appio, F. P., Lima, M., and Paroutis, S. (2019). Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technol. Forecast. Soc. Change 142, 1–14. doi:10.1016/j.techfore.2018.12.018
Ben Yahia, N., Eljaoued, W., Bellamine Ben Saoud, N., and Colomo-Palacios, R. (2021). Towards sustainable collaborative networks for smart cities co-governance. Int. J. Inf. Manag. 56, 102037. doi:10.1016/j.ijinfomgt.2019.11.005
Bibri, S. E., and Krogstie, J. (2017). On the social shaping dimensions of smart sustainable cities: A study in science, technology, and society. Sustain. Cities Soc. 29, 219–246. doi:10.1016/j.scs.2016.11.004
Bisello, A. (2020). Assessing multiple benefits of Housing Regeneration and smart city development: The European project SINFONIA. Sustainability 12 (19), 8038. doi:10.3390/su12198038
Bosch-Mauchand, M., Belkadi, F., Bricogne, M., and Eynard, B. (2013). Knowledge-based assessment of manufacturing process performance: Integration of product lifecycle management and value-chain simulation approaches. Int. J. Comput. Integr. Manuf. 26 (5), 453–473. doi:10.1080/0951192X.2012.731611
Budy, P. A. (2018). Smart city narrative in Indonesia: Comparing policy documents in four cities. Public Adm. Issues, Spec. Issue II Electron. Ed. 6, 65–83. doi:10.17323/1999-5431-2018-0-6-65-83
Bykova, A., and Jardon, C. M. (2018). The mediation role of companies’ dynamic capabilities for business performance excellence: Insights from foreign direct investments. The case of transitional partnership. Knowl. Manag. Res. Pract. 16 (1), 144–159. doi:10.1080/14778238.2018.1428070
Caprotti, F., Cowley, R., Datta, A., Broto, V. C., Gao, E., Georgeson, L., et al. (2017). The new urban agenda: Key opportunities and challenges for policy and practice. Urban Res. Pract. 10 (3), 367–378. doi:10.1080/17535069.2016.1275618
Centobelli, P., Cerchione, R., and Esposito, E. (2017). Knowledge management in Startups: Systematic literature review and future research agenda. Sustainability 9 (3), 361. doi:10.3390/su9030361
Chen, J., Yin, X., and Mei, L. (2018). Holistic innovation: An emerging innovation Paradigm. Int. J. Innovation Stud. 2 (1), 1–13. doi:10.1016/j.ijis.2018.02.001
Considine, M. (2002). The end of the line? Accountable Governance in the Age of networks, Partnerships, and Joined-up services. Governance 15 (1), 21–40. doi:10.1111/1468-0491.00178
Cornelissen, G., Bashshur, M. R., Rode, J., and Le Menestrel, M. (2013). Rules or consequences? The role of ethical Mind-sets in moral dynamics. Psychol. Sci. 24 (4), 482–488. doi:10.1177/0956797612457376
Cruz, S., and Camps, J. (2003). Organic vs. Mechanistic Structures: Construction and validation of a scale of measurement. Manag. Res. J. Iberoamerican Acad. Manag. 1 (1), 111–123. doi:10.1108/15365430380000521
D’Agostini, M., Tondolo, V. A. G., Camargo, M. E., Dullius, A. I. d. S., Tondolo, R. d. R. P., and Russo, S. L. (2017). Relationship between sustainable operations practices and performance: A meta-analysis. Int. J. Prod. Perform. Manag. 66 (8), 1020–1042. doi:10.1108/IJPPM-11-2015-0168
Dencker, J. C., and Gruber, M. (2015). The effects of opportunities and founder experience on new firm performance: Opportunities, Founder Experience and New Firm Performance. Strateg. Manag. J. 36 (7), 1035–1052. doi:10.1002/smj.2269
Deng, T., Zhang, K., and Shen, Z.-J. M. (2021). A systematic review of a digital twin city: A new pattern of urban governance toward smart cities. J. Manag. Sci. Eng. 6 (2), 125–134. doi:10.1016/j.jmse.2021.03.003
Donaldson, L., and Luo, B. N. (2014). The Aston programme contribution to organizational research: A literature review: Aston contribution to organizational research. Int. J. Manag. Rev. 16 (1), 84–104. doi:10.1111/ijmr.12010
Džupka, P. (2021). Urban smart-Mobility projects evaluation. Theor. Empir. Res. Urban Manag. Buchar. 16 (4), 23.
Edelenbos, J., Hirzalla, F., van Zoonen, L., van Dalen, J., Bouma, G., Slob, A., et al. (2018). “Governing the Complexity of smart data cities: Setting a research agenda,” in Smart Technologies for smart governments: Transparency, efficiency and organizational issues. Editor M. P. Rodríguez Bolívar (Springer International Publishing), 35–54. doi:10.1007/978-3-319-58577-2_3
Firmanyah, H. S., Supangkat, S. H., Arman, A. A., and Adhitya, R. (2017). “Searching smart city in Indonesia through maturity model analysis: (Case study in 10 cities),” in 2017 International Conference on ICT For Smart Society (ICISS), Tangerang, Indonesia, Sept. 2017 (IEEE).
Garside, R. (2014). Should we appraise the quality of qualitative research reports for systematic reviews, and if so, how? Innovation. Innovation Eur. J. Soc. Sci. Res. 27 (1), 67–79. doi:10.1080/13511610.2013.777270
Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., and Cohen, J. D. (2001). An fMRI investigation of Emotional Engagement in moral Judgment. Science 293 (5537), 2105–2108. doi:10.1126/science.1062872
Gupta, P., Chauhan, S., and Jaiswal, M. P. (2019). Classification of smart city research - a Descriptive literature review and future research agenda. Inf. Syst. Front. 21 (3), 661–685. doi:10.1007/s10796-019-09911-3
Hai, T. N., Van, Q. N., and Thi Tuyet, M. N. (2021). Digital transformation: Opportunities and challenges for leaders in the emerging Countries in Response to covid-19 pandemic. Emerg. Sci. J. 5, 21–36. doi:10.28991/esj-2021-SPER-03
Hasmawaty, , Utami, Y. T., and Antoni, D. (2022). Building Green smart city capabilities in South Sumatra, Indonesia. Sustainability 14 (13), 7695. doi:10.3390/su14137695
Herdiyanti, A., Hapsari, P. S., and Susanto, T. D. (2019). Modelling the smart Governance performance to support smart city program in Indonesia. Procedia Comput. Sci. 161, 367–377. doi:10.1016/j.procs.2019.11.135
Huovila, A., Bosch, P., and Airaksinen, M. (2019). Comparative analysis of standardised indicators for Smart sustainable cities: What indicators and standards to use and when? Cities 89, 141–153. doi:10.1016/j.cities.2019.01.029
Ichniowski, C., Kochan, T. A., Levine, D., Olson, C., and Strauss, G. (1996). What works at work: Overview and assessment. Ind. Relat. (Berkeley). 35 (3), 299–333. doi:10.1111/j.1468-232X.1996.tb00409.x
Ismagilova, E., Hughes, L., Rana, N. P., and Dwivedi, Y. K. (2022). Security, Privacy and Risks within smart cities: Literature review and development of a smart city interaction framework. Inf. Syst. Front. 24 (2), 393–414. doi:10.1007/s10796-020-10044-1
Israilidis, J., Odusanya, K., and Mazhar, M. U. (2021). Exploring knowledge management perspectives in smart city research: A review and future research agenda. Int. J. Inf. Manag. 56, 101989. doi:10.1016/j.ijinfomgt.2019.07.015
Jiang, J. C., Kantarci, B., Oktug, S., and Soyata, T. (2020). Federated learning in smart city Sensing: Challenges and opportunities. Sensors 20 (21), 6230. doi:10.3390/s20216230
Kaczynski, D., Salmona, M., and Smith, T. (2014). Qualitative research in finance. Aust. J. Manag. 39, 127–135. doi:10.1177/0312896212469611
Kakderi, C., Oikonomaki, E., and Papadaki, I. (2021). Smart and Resilient urban futures for sustainability in the Post COVID-19 Era: A review of policy Responses on urban Mobility. Sustainability 13 (11), 6486. doi:10.3390/su13116486
Kantabutra, S. (2020). Toward an organizational theory of sustainability Vision. Sustainability 12 (3), 1125. doi:10.3390/su12031125
Kayaga, S., Mugabi, J., and Kingdom, W. (2013). Evaluating the institutional sustainability of an urban water utility: A conceptual framework and research directions. Util. Policy 27, 15–27. doi:10.1016/j.jup.2013.08.001
Kim, W., Kim, J., Woo, H., Park, J., Jo, J., Park, S.-H., et al. (2017). The relationship between work Engagement and organizational commitment: Proposing research agendas through a review of Empirical literature. Hum. Resour. Dev. Rev. 16 (4), 350–376. doi:10.1177/1534484317725967
Kumar, H., Singh, M. K., Gupta, M. P., and Madaan, J. (2020). Moving towards smart cities: Solutions that lead to the smart city transformation framework. Technol. Forecast. Soc. Change 153, 119281. doi:10.1016/j.techfore.2018.04.024
Kusumastuti, R. D., Nurmala, N., Rouli, J., and Herdiansyah, H. (2022). Analyzing the factors that influence the seeking and sharing of information on the smart city digital platform: Empirical evidence from Indonesia. Technol. Soc. 68, 101876. doi:10.1016/j.techsoc.2022.101876
Kwak, Y. H., and Lee, J. (2022). Toward sustainable smart city: Lessons from 20 Years of Korean programs. IEEE Trans. Eng. Manag., 1–15. doi:10.1109/TEM.2021.3060956
Lerro, A., and Schiuma, G. (2009). Knowledge-based dynamics of regional development: The case of Basilicata region. J. Knowl. Manag. 13 (5), 287–300. doi:10.1108/13673270910988114
Lewrick, M., Omar, M., Raeside, R., and Sailer, K. (2010). Education for entrepreneurship and innovation: “Management capabilities for sustainable growth and success”. W. Jnl. Ent. Man. Sust. Dev. 6 (1/2), 1–18. doi:10.1108/20425961201000001
Lin, F., Fofanah, S. S., and Liang, D. (2011). Assessing citizen adoption of e-Government initiatives in Gambia: A validation of the technology acceptance model in information systems success. Gov. Inf. Q. 28 (2), 271–279. doi:10.1016/j.giq.2010.09.004
Lowe, A., Norris, A. C., Farris, A. J., and Babbage, D. R. (2018). Quantifying Thematic saturation in qualitative data analysis. Field Methods 30 (3), 191–207. doi:10.1177/1525822X17749386
Lytras, M. D., Visvizi, A., Chopdar, P. K., Sarirete, A., and Alhalabi, W. (2021). Information Management in Smart Cities: Turning end users’ views into multi-item scale development, validation, and policy-making recommendations. Int. J. Inf. Manag. 56, 102146. doi:10.1016/j.ijinfomgt.2020.102146
Ma, R., Lam, P. T. I., and Leung, C. K. (2018). Potential pitfalls of smart city development: A study on parking mobile applications (apps) in Hong Kong. Telematics Inf. 35 (6), 1580–1592. doi:10.1016/j.tele.2018.04.004
Mahesa, R., Yudoko, G., and Anggoro, Y. (2019). Dataset on the sustainable smart city development in Indonesia. Data Brief 25, 104098. doi:10.1016/j.dib.2019.104098
Mangindaan, D., Adib, A., Febrianta, H., and Hutabarat, D. J. C. (2022). Systematic literature review and bibliometric study of Waste management in Indonesia in the COVID-19 pandemic Era. Sustainability 14 (5), 2556. doi:10.3390/su14052556
Mayangsari, L., and Novani, S. (2015). Multi-stakeholder co-creation analysis in smart city management: An experience from Bandung, Indonesia. Procedia Manuf. 4, 315–321. doi:10.1016/j.promfg.2015.11.046
Mintrom, M., and Luetjens, J. (2016). Design thinking in Policymaking processes: Opportunities and challenges: Design thinking in Policymaking processes. Aust. J. Public Adm. 75 (3), 391–402. doi:10.1111/1467-8500.12211
Mohamed, M., Stankosky, M., and Murray, A. (2006). Knowledge management and information technology: Can they work in perfect harmony? J. Knowl. Manag. 10 (3), 103–116. doi:10.1108/13673270610670885
Moreno, V. d. A., de Souza Costa Neves Cavazotte, F., and de Oliveira Valente, D. (2009). Strategic Alignment and its Antecedents: A critical analysis of constructs and Relations in the international and Brazilian literature. J. Glob. Inf. Technol. Manag. 12 (2), 33–60. doi:10.1080/1097198X.2009.10856490
Mouazen, A. M., and Hernández-Lara, A. B. (2021). The role of sustainability in the relationship between migration and smart cities: A bibliometric review. Digital Policy, Regul. Gov. 23 (1), 77–94. doi:10.1108/DPRG-04-2020-0051
Mwaniki, D. (2017). “Infrastructure development in Nairobi: Widening the path towards a smart city and smart economic development,” in Smart economy in smart cities: International Collaborative research. Editor T. M. Vinod Kumar (Singapore: Springer), 687–711. Ottawa, St.Louis, Stuttgart, Bologna, Cape Town, Nairobi, Dakar, Lagos, New Delhi, Varanasi, Vijayawada, Kozhikode, Hong Kong. doi:10.1007/978-981-10-1610-3_25
Ng, M. K., Koksal, C., Wong, C., and Tang, Y. (2022). Smart and sustainable development from a Spatial planning perspective: The case of Shenzhen and Greater Manchester. Sustainability 14 (6), 3509. doi:10.3390/su14063509
Nikki Han, M. J., and Kim, M. J. (2021). A critical review of the smart city in relation to citizen adoption towards sustainable smart living. Habitat Int. 108, 102312. doi:10.1016/j.habitatint.2021.102312
Ning, H., and Liu, H. (2015). Cyber-physical-social-thinking space-based science and technology framework for the Internet of Things. Sci. China Inf. Sci. 58 (3), 1–19. doi:10.1007/s11432-014-5209-2
O’Donovan, R., Rogers, L., Khurshid, Z., De Brún, A., Nicholson, E., O’Shea, M., et al. (2021). A systematic review exploring the impact of focal leader behaviours on health care team performance. J. Nurs. Manag. 29 (6), 1420–1443. doi:10.1111/jonm.13403
Offenhuber, D. (2019). The platform and the bricoleur—Improvisation and smart city initiatives in Indonesia. Environ. Plan. B Urban Anal. City Sci. 46 (8), 1565–1580. doi:10.1177/2399808319865749
Okafor, C. C., Aigbavboa, C., and Thwala, W. D. (2022). A bibliometric evaluation and critical review of the smart city concept – making a case for social equity. J. Sci. Technol. Policy Manag, 1–24. doi:10.1108/JSTPM-06-2020-0098
Parlina, A., Murfi, H., and Ramli, K. (2019). “Smart city research in Indonesia: A bibliometric analysis,” in 2019 16th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering, Padang, Indonesia, July 2019 (IEEE).
Pereira, G. V., Parycek, P., Falco, E., and Kleinhans, R. (2018). Smart governance in the context of smart cities: A literature review. Inf. Polity 23 (2), 143–162. doi:10.3233/IP-170067
Peter, C., and Meyer, C. (2022). Organizing for the Smart African City: Leveraging the urban commons for exerting the right to the city. Organ. Stud, 1–22. doi:10.1177/01708406221089609
Pettit, C., Bakelmun, A., Lieske, S. N., Glackin, S., Hargroves, K. C., Thomson, G., et al. (2018). Planning support systems for smart cities. City, Cult. Soc. 12, 13–24. doi:10.1016/j.ccs.2017.10.002
Pichler, S. (2012). The social context of performance appraisal and appraisal reactions: A meta-analysis. Hum. Resour. Manage. 51 (5), 709–732. doi:10.1002/hrm.21499
Pratama, A. B., and Imawan, S. A. (2019). A scale for measuring perceived bureaucratic readiness for smart cities in Indonesia. Public Adm. Policy 22 (1), 25–39. doi:10.1108/PAP-01-2019-0001
Pratama, A. B. (2021). Smart is not equal to technology. Adv. Southeast Asian Stud. Preview Publ. details ; Vienna 15 (1), 125–132.
Purwanto, E. A. (2018). Smart city as an Upshot of bureaucratic Reform in Indonesia. Int. J. Electron. Gov. Res. 14 (3), 32–43. doi:10.4018/IJEGR.2018070103
Rachmawati, R., Sari, A. D., Sukawan, H. A. R., Widhyastana, I. M. A., and Ghiffari, R. A. (2021). The use of ICT-based applications to support the implementation of smart cities during the Covid-19 pandemic in Indonesia. Infrastructures 6 (9), 119. doi:10.3390/infrastructures6090119
Ramirez Lopez, L. J., and Grijalba Castro, A. I. (2021). Sustainability and Resilience in smart city planning: A review. Sustainability 13 (1), 181. doi:10.3390/su13010181
Rana, N. P., and Dwivedi, Y. K. (2015). Citizen’s adoption of an e-government system: Validating extended social cognitive theory (SCT). Gov. Inf. Q. 32 (2), 172–181. doi:10.1016/j.giq.2015.02.002
Rao, S. K., and Prasad, R. (2018). Impact of 5G Technologies on smart city implementation. Wirel. Pers. Commun. 100 (1), 161–176. doi:10.1007/s11277-018-5618-4
Retna, K. S. (2002). The Fifth Discipline in a highly disciplined Singapore: Innovative learning organisations and national culture. Innovation 4 (1-3), 215–226. doi:10.5172/impp.2001.4.1-3.215
Rochet, C., and Belemlih, A. (2020). “Social emergence, Cornerstone of smart city Governance as a complex citizen-Centric system,” in Handbook of smart cities. Editor J. C. Augusto (Springer International Publishing), 1–26. doi:10.1007/978-3-030-15145-4_29-1
Rowlands, T., Waddell, N., and McKenna, B. (2016). Are We there yet? A Technique to determine theoretical saturation. J. Comput. Inf. Syst. 56 (1), 40–47. doi:10.1080/08874417.2015.11645799
Ryan, C., and Walsh, P. (2004). Collaboration of public sector agencies: Reporting and accountability challenges. Int. J. Public Sect. Manag. 17 (7), 621–631. doi:10.1108/09513550410562284
Sebele-Mpofu, F. Y. (2020). Saturation controversy in qualitative research: Complexities and underlying assumptions. A literature review. Cogent Soc. Sci. 6 (1), 1838706. doi:10.1080/23311886.2020.1838706
Serey, J., Quezada, L., Alfaro, M., Fuertes, G., Ternero, R., Gatica, G., et al. (2020). Methodological Proposals for the development of services in a smart city: A literature review. Sustainability 12 (24), 10249. doi:10.3390/su122410249
Sharif, R. A., and Pokharel, S. (2022). Smart city dimensions and Associated Risks: Review of literature. Sustain. Cities Soc. 77, 103542. doi:10.1016/j.scs.2021.103542
Sharifi, A. (2019). A critical review of selected smart city assessment tools and indicator sets. J. Clean. Prod. 233, 1269–1283. doi:10.1016/j.jclepro.2019.06.172
Shelton, T., Zook, M., and Wiig, A. (2015). The ‘actually existing smart city. Camb. J. Regions, Econ. Soc. 8 (1), 13–25. doi:10.1093/cjres/rsu026
Shen, L., Huang, Z., Wong, S. W., Liao, S., and Lou, Y. (2018). A holistic evaluation of smart city performance in the context of China. J. Clean. Prod. 200, 667–679. doi:10.1016/j.jclepro.2018.07.281
Shin, S.-Y., Kim, D., and Chun, S. A. (2021). Digital Divide in advanced smart city Innovations. Sustainability 13 (7), 4076. doi:10.3390/su13074076
Silva, B. N., Khan, M., and Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38, 697–713. doi:10.1016/j.scs.2018.01.053
Smith, M., and Cooper, C. (1994). Leadership and stress. Leadersh. Organ. Dev. J. 15 (2), 3–7. doi:10.1108/01437739410055290
Sobinov, A. R., and Bensmaia, S. J. (2021). The neural mechanisms of manual dexterity. Nat. Rev. Neurosci. 22 (12), 741–757. doi:10.1038/s41583-021-00528-7
Suartika, G. A. M., and Cuthbert, A. (2020). The sustainable Imperative—smart cities, technology and development. Sustainability 12 (21), 8892. doi:10.3390/su12218892
Susanti, R., Soetomo, S., Buchori, I., and Brotosunaryo, P. M. (2016). Smart Growth, smart city and Density: In Search of the appropriate indicator for Residential Density in Indonesia. Procedia - Soc. Behav. Sci. 227, 194–201. doi:10.1016/j.sbspro.2016.06.062
Syalianda, S. I., and Kusumastuti, R. D. (2021). Implementation of smart city concept: A case of Jakarta smart city, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 716 (1), 012128. doi:10.1088/1755-1315/716/1/012128
Tagliabue, M., Sigurjonsdottir, S. S., and Sandaker, I. (2020). The effects of performance feedback on organizational citizenship behaviour: A systematic review and meta-analysis. Eur. J. Work Organ. Psychol. 29 (6), 841–861. doi:10.1080/1359432X.2020.1796647
Tan, S.-Y., Taeihagh, A., and Sha, K. (2021). How Transboundary learning occurs: Case study of the ASEAN smart cities network (ASCN). Sustainability 13 (11), 6502. doi:10.3390/su13116502
Tan, S., and Taeihagh, A. (2020). Smart city Governance in developing Countries: A systematic literature review. Sustainability 12 (3), 899. doi:10.3390/su12030899
Tay, K. C., Supangkat, S. H., Cornelius, G., and Arman, A. A. (2018). “The SMART Initiative and the Garuda smart city framework for the development of smart cities,” in 2018 International Conference on ICT for Smart Society (ICISS), Semarang, Indonesia, Oct. 2018 (IEEE).
Tomor, Z., Meijer, A., Michels, A., and Geertman, S. (2019). Smart Governance for sustainable cities: Findings from a systematic literature review. J. Urban Technol. 26 (4), 3–27. doi:10.1080/10630732.2019.1651178
Truelove, H. B., Carrico, A. R., Weber, E. U., Raimi, K. T., and Vandenbergh, M. P. (2014). Positive and negative spillover of pro-environmental behavior: An integrative review and theoretical framework. Glob. Environ. Change 29, 127–138. doi:10.1016/j.gloenvcha.2014.09.004
Vitunskaite, M., He, Y., Brandstetter, T., and Janicke, H. (2019). Smart cities and cyber security: Are we there yet? A comparative study on the role of standards, third party risk management and security ownership. Comput. Secur. 83, 313–331. doi:10.1016/j.cose.2019.02.009
Wanzenböck, I., and Piribauer, P. (2016). RandD networks and regional knowledge production in Europe: Evidence from a space-time model: RandD networks and regional knowledge production in Europe. Pap. Reg. Sci. 97, S1–S24. doi:10.1111/pirs.12236
Yigitcanlar, T., Kamruzzaman, M., Foth, M., Sabatini-Marques, J., da Costa, E., and Ioppolo, G. (2019). Can cities become smart without being sustainable? A systematic review of the literature. Sustain. Cities Soc. 45, 348–365. doi:10.1016/j.scs.2018.11.033
Yigitcanlar, T. (2015). Smart cities: An effective urban development and management model? Aust. Plan. 52 (1), 27–34. doi:10.1080/07293682.2015.1019752
Yiing, L. H., and Ahmad, K. Z. B. (2009). The moderating effects of organizational culture on the relationships between leadership behaviour and organizational commitment and between organizational commitment and job satisfaction and performance. Leadersh. Organ. Dev. J. 30 (1), 53–86. doi:10.1108/01437730910927106
Yoshida, M., and Thammetar, T. (2021). Education between GovTech and Civic Tech. Int. J. Emerg. Technol. Learn. 16 (04), 52. doi:10.3991/ijet.v16i04.18769
Keywords: mental models, double-loop, smart city, prosperity, innovation, political economy
Citation: Bastian I, Effendi R, Susanto EA, Unggara I and Sumiyana S (2022) The government of Indonesia’s smart city development: Fiscal capacity, cognitive models in decision making, excessive caution about future accrual of benefits, and null regulatory leadership. Front. Built Environ. 8:1065652. doi: 10.3389/fbuil.2022.1065652
Received: 10 October 2022; Accepted: 11 November 2022;
Published: 28 November 2022.
Edited by:
Pablo Martinez, University of Alberta, CanadaReviewed by:
Mykola Pasichnyi, Kyiv National University of Trade and Economics (KNTEU), UkraineAan Jaelani, State Institute for islamic studies (IAIN) Syekh Nurjati, Indonesia
Copyright © 2022 Bastian, Effendi, Susanto, Unggara and Sumiyana. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Sumiyana Sumiyana, sumiyana@ugm.ac.id