The decentralized nature of information and the widespread use of mobile devices for accessing content make the water sector an excellent platform for using the Internet of Things (IoT) paradigm to streamline procedures, benchmark standards, and create a sustainable environment. Smart devices play an essential role in deploying digital solutions through IoT. They help connect the physical world with the digital universe and are regarded as fundamental entities within a network-integrated through IoT. The study provides a better understanding to assess the critical success factors (CSFs) for smart device implementation in the water sector. The factors are obtained from the literature and shortlisted with the help of experts using the Delphi method. Kappa statistics are used to further validate the experts' consensus. The significant factors identified were leadership, usability, cost of implementation, technology awareness, data privacy, interoperability and community partnership. Mental modeler software has been used to construct a fuzzy logic cognitive mapping of CSFs to represent causal reasoning in diagraphs. Scenario analysis was conducted for each CSF. The study provides recommendations for policymakers to develop precise strategies for integrating IoT in the water industry.

  • The study identified the critical success factors (CSFs) for implementing smart devices in the water industry.

  • The study derives using the Delphi methodology. Kappa statistics were further used to validate the output.

  • The CSFs identified later in the brainstorming session were conducted to develop a fuzzy logic cognitive mapping using mental modeler software.

  • Scenario analysis was conducted by varying the energy of the driver, and an effect was observed.

The term ‘Things’ in the Internet of Things (IoT) refers to objects denoted by various names in literature, such as smart devices, smart things, smart gadgets, or smart objects. Smart devices can be characterized as electronic devices equipped for communicating and computing that span from small sensor nodes to household appliances, along with real-time access through mobile connectivity. These devices form a typical part of the IoT and play a critical role in implementing and developing smart solutions for the IoT network in the water industry.

The regular Internet is a global network that establishes communication between computers and devices using a defined protocol. The Internet helps to exchange information among various devices through data exchanges. On the other hand, the IoT is a network that links physical devices such as appliances, equipment, vehicles, or wearables. It helps to share information between these gadgets and connect the real world with the digital space. The IoT paradigm encapsulates a virtual network of physical objects that enables real-time data collection, data analysis, flexibility, productivity tools, and increased security with improved stakeholder communication.

The IoT has expanded the idea of smart water for the water and wastewater industry to encompass more pertinent and connected services, connecting water with other utilities and water equipment. The water industry encapsulates the sourcing, supply, and treatment of water. The wastewater industry includes sewerage, wastewater treatment, sanitation, recovery, and reuse. The inclusion of IoT technology in the water utility sector would reduce the per capita cost of the water utility in the long run (Dworak, 2007). The Gartner Hype Cycle report in 2015 looked into the commercial evolution of IoT in the water sector. They predicted that smart water would remain an evolutionary technology for some years (Sumic, 2015). The initial adoption of smart devices in the water industry should be in a phased manner, with smart meters, leakage management devices and smart irrigation gadgets as the initial adoption techniques (Sedlack, 2016). Until recently, the world's focus was toward changing customer behavior by encouraging them to consume less water. The industry has now stepped in the direction of optimizing water flow, detecting internal leaks, real-time monitoring, and timing the water usage to promote efficient consumption of water and reduced peak demand (Owen, 2018).

The evolution and diffusion of IoT aims to improve water management's social dimensions, i.e., valuing water and the ecosystems it supports, ensuring equitable use and distribution of water resources, and the possibility of ‘water wars’. The industry has taken a closer look at smart water solutions due to the challenges of water scarcity, ageing infrastructure, environmental standards, stricter regulations, and increased development costs (Cahn, 2014). A smart water grid (an analogy from the smart grid for power) gives agencies the tools they need to serve their customers better while achieving their quality, productivity, and efficiency goals. An integrated system of smart devices, services, solutions, and systems can be considered a ‘smart water network’. This is an essential step toward effectively including smart devices and related technologies in the country's water and wastewater sectors. The IoT strategy in the water and wastewater sector encourages academics and software developers to design creative solutions based on mobile content access to boost efficiency and fluid real-time communication around the entire water value chain and among industry players. Implementing such creative solutions necessitates organized strategies and a competent workforce that could analyze and acquire the skills to successfully implement, operationalize, and maintain smart devices in the water network. As a result, the objective of this paper is to:

  • 1. Identify the critical factors for the successful implementation of smart devices in the water and wastewater sectors.

  • 2. Develop and analyze a fuzzy logic cognitive mapping (FCM) for the critical success factors (CSFs) using scenario analysis.

The following section will discuss smart devices in the water and wastewater industry, followed by the research problem in Section 3. Section 4 discusses the CSFs for the successful implementation of smart devices in the water and wastewater industry. It will be followed by research methodology, including Kappa statistics, FCM, and scenario analysis in Section 5 and conclusion and recommendation in Section 6.

The role of IoT depends on how smart devices are defined in this study for its implementation in water and wastewater networks. IoT smart devices have been defined by various names like ‘smart objects’, ‘smart mobile devices’, ‘mobile devices’, ‘smart metering devices’, and ‘smart gadgets’, depending on their specific applications. The literature outlines and clusters smart devices based on key attributes: context-awareness, autonomy (and self-reaction), user interaction and connectivity. The study considers the theory of Stojkoska & Trivodaliev (2017), which studies smart devices in the context of objects existent in IoT pervasive networks. IoT helps build a water and wastewater network between devices; most of these devices might never directly interact with the stakeholders (Miller, 2015). Hence, the ability of a smart device to direct interaction with the stakeholder is not essential. However, context-awareness is a necessary element of a smart device.

A fundamental factor that would make a device installed on the water or wastewater network smart is its ability to be contextually aware. Context awareness is the capability of smart devices to sense information gathered from their surroundings using sensors (e.g., radio frequency identification (RFID), proximity, global positioning system (GPS), heat, camera, and microphone). The water network data collected by sensors can then be used to make autonomous judgments or respond with real-time guidance to the stakeholder. Some examples of context-awareness are the application of smart devices in human voice recognition (Husnjak et al., 2014), and photography and video recording (Godwin et al., 2013).

Connectivity for a smart device is to connect to a new network or an already established, more extensive network. The central objective might be to share water and wastewater data with multiple devices within the network to only obtain Internet access, or both. Internet access on a smart device could be gaining access to a much more extensive or distant water network. Internet use is widespread on smart devices. It implies that connecting to a network is essential for a smart device (Harwood et al., 2014), and devices with IoT technology should possess network capabilities (Sivaraman et al., 2018).

User interaction is yet another essential feature of a smart device. Various smart device designs, such as the consumer IoT devices, are based on user interaction with smart devices, such as smartphones, smartwatches, and tablets (Harwood et al., 2014). Various consumer-centric gadgets assist stakeholders in making decentralized decisions about usage, cost savings, context-awareness, detecting difficulties in real-time, filing customer complaints, and receiving prompt assistance (Williams et al., 2017; Weyns et al., 2018). Even though there is an array of smart devices designed for human interaction, the IoT paradigm is built on the interaction of devices, which means that any item may be linked and can serve as a hub for gathering or transmitting information to a more extensive network (Miller, 2015). As a result, smart gadgets in water or wastewater networks do not always necessitate communication with humans.

Other technologies that are associated with smart devices in conjunction with IoT are cloud computing (Silverio et al., 2017), augmented reality (Chi et al., 2013), and graphical information systems (Sweeney, 1999). From an operational point of view, a smart device is a context-aware electronic gadget able to perform autonomous processing and data exchange via wired or wireless connections (Silverio-Fernández et al., 2018). This notion implies that any water or wastewater device in its network may be converted into a smart device by including the appropriate technology. Despite an established smart device concept, there is limited research to conceptualize and implement it in the water and wastewater industries.

Due to the ever-increasing demand, water security remains a significant and growing concern for many countries, especially developing countries like India. According to a 2019 NITI Aayog report, India has been experiencing its worst water crisis in history, with nearly 600 million people without clean water. India resides roughly 18% of the global population and has only 4% of the water resources worldwide (WWAP (United Nations World Water Assessment Programme) 2017). Most of the water and wastewater infrastructure has been outmoded. Ageing infrastructure has resulted in water loss, pipeline and pump failures, contamination, ineffective maintenance and distribution, and insufficient treatment. The water and wastewater value chain infrastructure needs a complete overhaul. Water source contamination and water pollution are challenges that pose health risks to the general public and disturb the area's ecology. Some other problems the water and wastewater industry faces are water conservation, operational and maintenance cost, energy efficiency, and financial sustainability.

Although the literature thoroughly explains the challenges in the water and wastewater sector and looks forward to technology interventions such as smart devices to mitigate the challenges, it fails to provide a research foundation to implement smart devices in the water and wastewater network. In this scenario, there is limited understanding concerning the essential factors for the successful adoption of smart devices by decision-makers in the water and wastewater sector. As a result, establishing a meaningful interrelationship among the factors leading to the application of smart devices in the sectors brings novelty to this study.

The following section details the Delphi methodology used to extract the CSFs and outlines the seven CSFs for embedding and implementing smart devices in water and wastewater corporations.

Delphi methodology

The Delphi method aims to minimize the less desirable aspects of group discussions while simultaneously increasing the availability of diverse perspectives on a certain issue. Anonymity, iteration, regulated feedback, and an aggregate of individual responses are the four main elements that characterize the Delphi technique (Grime & Wright, 2016).

Thirty-five experts who fit the study's criteria were selected from a larger pool. Experts were chosen using a convenient sampling method. Eleven experts agreed to participate in the study (Table 1), which is considered an appropriate number for the research (Belton et al., 2019). The expert selection was based on two criteria: (1) a minimum of three years of relevant experience and (2) a conceptual continuum of ‘closeness’ to the topic of interest (Belton et al., 2019). The first two experts (as in Table 1) are from different organizations based in Israel and France, respectively. The following three experts are the executives from government ministries in India, looking after various water and technology-related missions in the country. Experts 6 and 9 are engineers from Sri Lanka and Australia, respectively. Experts 7 and 11 deal with water availability and recovery, primarily dealing with industrial hubs and agricultural lands in India. The remaining experts are involved in domestic water supply and recovery in India. The generalizability of the Delphi results was ensured by selecting the experts from a larger pool and retaining the heterogeneity of the Delphi group (Hasson et al., 2000). Empirical study also indicates that heterogeneity and diversity of thought can enhance the accuracy of the Delphi outcome (Hussler et al., 2011).

Table 1

Details of the experts interviewed.

Expert No.OrganizationDesignationExperience
Expert 1 Smart Water Solutions CEO 20 years 
Expert 2 Smart Water Solutions Water Technology Advisor 14 years 
Expert 3 National Water Mission Director 18 years 
Expert 4 National Water Mission Ass. Section Officer 10 years 
Expert 5 Ministry of Jal Shakti RD Head 12 years 
Expert 6 Municipal Water Executive Engineer 12 years 
Expert 7 Municipal Water Executive Engineer 12 years 
Expert 8 Municipal Water Executive Engineer 10 years 
Expert 9 Municipal Water Assistant Engineer 7 years 
Expert 10 Municipal Water Assistant Engineer 5 years 
Expert 11 Municipal Water Junior Engineer 4 years 
Expert No.OrganizationDesignationExperience
Expert 1 Smart Water Solutions CEO 20 years 
Expert 2 Smart Water Solutions Water Technology Advisor 14 years 
Expert 3 National Water Mission Director 18 years 
Expert 4 National Water Mission Ass. Section Officer 10 years 
Expert 5 Ministry of Jal Shakti RD Head 12 years 
Expert 6 Municipal Water Executive Engineer 12 years 
Expert 7 Municipal Water Executive Engineer 12 years 
Expert 8 Municipal Water Executive Engineer 10 years 
Expert 9 Municipal Water Assistant Engineer 7 years 
Expert 10 Municipal Water Assistant Engineer 5 years 
Expert 11 Municipal Water Junior Engineer 4 years 

A ‘real-time’ Delphi procedure was employed for the study. It is a variant of the method in which the entire procedure is carried out within a single ‘round-less’ time window (Gnatzy et al., 2011) or can proceed through multiple Delphi rounds in one session (Belton et al., 2019). This process is generally conducted via the Internet (Aengenheyster et al., 2017). Three rounds of brainstorming sessions were conducted to finalize the study's CSFs (Belton et al., 2019). The experts were initially provided with a detailed description of the problem and the specific goals of the Delphi exercise. In the first round, 12 factors identified from the literature were presented, with experts encouraged to add any additional factors they deemed relevant. They were then asked to select the CSFs necessary for the effective implementation of smart devices in the water sector, providing a rationale for each selection. In the second round, a summary of the collective responses was shared with the experts, who were invited to review and, if necessary, revise their initial responses. This process was repeated in a third round, leading to a convergence of responses. By the conclusion of the third round, seven CSFs had been finalized with expert input, resulting in a cohesive set of themes that underpin the study's framework. Table 2 presents the response rate of the experts concerning the CSFs finalized for the study. The percentage denotes the number of experts positively agreeing with the CSFs.

Table 2

Response rate of CSFs obtained from experts.

CSFResponse rate
Leadership 100 
Technology awareness 81.81 
Usability 81.81 
Cost of implementation 100 
Interoperability 90.90 
Data privacy 72.72 
Community partnership 90.90 
CSFResponse rate
Leadership 100 
Technology awareness 81.81 
Usability 81.81 
Cost of implementation 100 
Interoperability 90.90 
Data privacy 72.72 
Community partnership 90.90 

Leadership

The administration and governance are critical in devotedly aligning the decision-makers with the corporation's endeavors. Organizations that demonstrate leadership capability receive support from the top management to deploy innovative technological solutions (Burmeister et al., 2015). The implementation success is significantly influenced by attitudes toward change and leadership encouraging transformation. According to Aarons et al. (2014), leaders who exhibit transformative and practical leadership traits improve the favorable environment for implementation and sustainability. Empirical research has also demonstrated the need for leadership for the success of implementation decisions (Aarons et al., 2014; McFadden et al., 2015).

Convincing the organization's decision-makers about the benefits of technology implementation is one of the most critical stages of executing smart devices in the water and wastewater sector. To capture the value of the upgraded system, leaders must invest in new technology, upskill their workforce, increase connectivity and information sharing, and streamline organizational boundaries (Cortellazzo et al., 2019). The bottlenecks of the existing water value chain can be well understood by executives managing daily operations. Therefore, executive-level decisions at each level are also essential for implementing the novel technology (Farahnak et al., 2019).

Technology awareness

The degree to which industry professionals perceive technology status is referred to as their level of technology awareness. Being aware necessitates continuous information-gathering due to technology's ever-evolving nature (Longo et al., 2020). There are different age groups of professionals within an organization who understand and embrace technology and its use at different levels. Some groups are resilient to change. Therefore, it is necessary to continuously educate professionals on innovative and upcoming technologies to avoid resistance to their implementation (Saarikko et al., 2020). The industry would more readily accept the technology if it adopted a common mindset regarding the advantages of smart devices.

Administrative procedures generally obstruct and slow innovation attempts (Burmeister et al., 2015). Greater use of new technologies can result from an entrepreneurial culture emphasizing employee flexibility and accountability (Akpan et al., 2020). The awareness of the organization's workforce is closely related to its culture.

The government has the power to develop regulations that can enable this in the water and wastewater sector. To increase knowledge and utilization of smart device technology, educational institutions and professional organizations could offer programs such as workshops, webinars, seminars, and continual professional development courses (Akpan et al., 2020).

Usability

Usability refers to the effectiveness of a system's interaction and user experience (Lv et al., 2015). The user's sentiments are a part of usability. An essential component of a user's ability for decision-making is emotion (Alzoubi & Aziz, 2021). The smart devices for the entire water value chain should be user-friendly, and communication with professionals should facilitate wider adoption of such solutions.

The applicability of smart devices in the water industry depends on various factors. The criteria for the water sector vary significantly and are extremely specific. Before implementing a technology-based solution, the adoption of smart devices should consider the conditions of the water network and any changes that may be required (Narang et al., 2023). Positive usability must make anything simple to utilize. The execution of an optimal user experience necessitates understanding and anticipating the network situations, such as network infrastructure and installation location, which may impact the use of smart devices.

Cost of implementation

A key aspect to take into account for a successful installation of smart devices is the cost of the suggested solution. The organization's capacity to adopt new technology is influenced by the size of the water and wastewater network. Developed countries can more easily incorporate innovative technology-based solutions since they have more resources than developing countries (Reymond et al., 2018). Densely populated areas have a denser water and wastewater network, which increases the likelihood that smart device deployment may be necessary for efficient coordination (Zeferino et al., 2012). A denser network ultimately increases the overall cost of the network. Eventually, the decision-making process is heavily influenced by the implementation cost.

The diffusion of technology is reliant on a cost–benefit analysis of the suggested solution. The use of smart devices initially entails higher costs for the decision-makers (Narang et al., 2023). By demonstrating the possible advantages to be attained and how these benefits can be deciphered into earnings, the positive cost–benefit analysis could promote the use of smart devices (Dworak, 2007). It is unlikely that the cost–benefit analysis will be put into practice if it does not result in either cost or time savings (Smets, 2008). Due to the scale of the water and wastewater network, communication improvements typically result in cost and time savings. Real-time information sharing within the management staff is the primary driver for integrating smart devices (Owen, 2018). This would help to make the water and wastewater network cost as well as time-efficient.

Interoperability

According to Blanc-Serrier et al. (2018), interoperability is a term used to define the capacity of equipment to integrate and share data. Because information systems are not interoperable, organizations must spend a lot of time as well as funds switching between and within projects. Therefore, it is vital for the systems to be interoperable (Forbes, 2017).

The convenience of integrating with existing technologies is a crucial consideration when adopting new technology that might be connected to smart devices and the IoT (Johnston, 2021). Existing assets may be converted into a smart device or gadget, including sensors and Internet connectivity. Installing new technology can be made smoother through interoperability and easy integration (Elkhodr et al., 2016). The concept behind the IoT is that every component of a water and wastewater network may be linked to a system of devices that collects data from every aspect of the network (Usman & Zhang, 2014). Corporations must consider the compatibility between new and old devices to successfully migrate from a traditional network to a new network paradigm that adopts IoT technology.

Data privacy

However, a discussion regarding citizen data privacy difficulties has become popular in the past few years (Sadowski et al., 2021). Big data has emerged, and its value chain appears to handle large data volumes. As a result of the immense volume of sensitive and personal data that IoT devices may gather, analyze, and communicate, data privacy is a crucial problem in the IoT environment (Narang et al., 2023). To safeguard people's rights and stop information from being misused, it is crucial to secure data privacy as IoT devices continue to expand in many facets of our lives (Ogonji et al., 2020). A smart device-enabled network has benefited from big data because sensors generate enormous amounts of data that call for management and analysis. Benchmarking, quality of service, connection, real-time analytics, and storage are additional crucial requirements to improve IoT water network services using big data analytics (Ahmed et al., 2017). Big data may be both a problem and a research opportunity for identifying and assessing security vulnerabilities (Stankovic, 2014).

Due to the many communication stacks and standards used in an IoT-enabled network, traditional security services in the water and wastewater network would be ineffective. Flexible security solutions must be created to address malware attacks and security concerns in a dynamic IoT ecosystem in order to produce a safe network (Balte et al., 2015; Elkhodr et al., 2016). Public water and wastewater data breaches can be detrimental to society and can be used for mala fide intentions or terror attacks. It is a challenge for professionals to design and monitor access control, authorization, bootstrapping, and authentication to ensure data privacy and security (Ogonji et al., 2020). The increased complexity and data gathered through an integrated strategy will present specific obstacles to handling and maintaining such large amounts of data. In order to give information outputs based on externally assumed assumptions, it would be challenging to manage and protect the data in a way that best reflects actual demands.

Community partnership

The water and wastewater network is an essential utility service for the community. The smart devices installed in the network and consumer IoT designed for the public must be developed keeping in mind the community's sentiments availing the services (Narang et al., 2024). Consumers are reluctant to use smart devices due to data breach issues and the extra money they must bear for their use, maintenance, and repair. Long-term per capita water utility costs would decrease with the use of IoT technology (Dworak, 2007). One of the fundamental difficulties facing resource managers and utilities may be the ability and desire of consumers to pay for water and wastewater services. Full cost recovery is still an anomaly universally. There is a significant difference between the current capital investment levels of the corporations and what may be needed to successfully install smart devices in the water and wastewater network (Global Water Intelligence, 2016). Public awareness and participation would be key to effectively implementing smart devices in the water and wastewater network.

The CSFs are finalized with the help of experts. The Delphi methodology was used to collect the responses from water industry experts to finalize the critical factors for a successful implementation of smart devices. Eleven experts were selected for the study, as indicated in Table 1. Further details about the experts are given in Section 4.1. The necessary questions (Table 3) were asked in the third round of the Delphi method to finalize CSFs for implementing smart devices effectively. It was necessary to finalize the CSFs, as it would result in building blocks and a cohesive set of factors that underpin the study's framework. Moreover, these questions also aided in quantitatively measuring the inter-rater reliability and consistency of the responses through Kappa statistics. Hence, the Delphi responses were validated, and seven CSFs were finalized for smart device implementation. The 11 experts assisted in developing the FCM of the seven CSFs using mental modeler software. Scenario analysis of the FCM was also conducted with the help of the experts.

Table 3

Experts response to finalize the CSFs.

Question: How much do you agree that the following finalized factors are of utmost importance for successfully implementing smart devices in the water sector?
Strongly disagreeMildly disagreeNeutralMildly agreeStrongly agree
Leadership 11 
Technology awareness 
Usability 
Cost of implementation 10 
Interoperability 
Data privacy 
Community partnership 10 
Question: How much do you agree that the following finalized factors are of utmost importance for successfully implementing smart devices in the water sector?
Strongly disagreeMildly disagreeNeutralMildly agreeStrongly agree
Leadership 11 
Technology awareness 
Usability 
Cost of implementation 10 
Interoperability 
Data privacy 
Community partnership 10 

Kappa statistics

The opinion of the experts was further validated using Kappa statistics. The statistics are used to measure the inter-rater reliability and consistency of the responses. Kappa statistics is a widely used method in research to validate agreement among decision-makers (Shardeo et al., 2022).

Kappa statistics were developed by Fleiss (1971) to evaluate the inter-rater reliability and consistency of various decision-makers. The difference between observed theoretical agreement and chance agreement divided by the probability of beyond-chance agreement is what is referred to as the Kappa coefficient (Giri et al., 2024). The strength of the agreement may be measured using the Kappa index calculated from Equation (1) using the Kappa measurement scale (Table 4) provided by Landis & Koch (1977).

Table 4

Kappa statistics scale.

Kappa statistics value< 00.10–0.200.21–0.400.41–0.600.61–0.800.81–1
Corresponding interpretation Insignificant consensus Slight consensus Fair consensus Reasonable consensus Significant consensus Perfect consensus 
Kappa statistics value< 00.10–0.200.21–0.400.41–0.600.61–0.800.81–1
Corresponding interpretation Insignificant consensus Slight consensus Fair consensus Reasonable consensus Significant consensus Perfect consensus 

The values of i, j, and E in this investigation are 7, 5, and 11, respectively. A total of 11 decision-makers have been considered to examine seven CSFs on a 5-point Likert scale. The Kappa index of 0.371 shows fair consensus (Table 4). The steps are as follows:
(1)
where Pe is the chance agreement and Po is the proportion of observed theoretical agreement. Furthermore, Pe and Po can be calculated as:
(2)
(3)
where y is the number of categories and x is the number of variables. Then, the percentage of alignment (Pj) and percentage of involvement (Pi) can be calculated as:
(4)
(5)
where E denotes the total number of decision-makers and eij denotes evaluation of the ith variable with regard to the jth category.

Fuzzy logic cognitive mapping (FCM)

FCM was initially developed by Kosko (1986) as a technique to organize expert knowledge using a ‘fuzzy’ soft systems programming methodology that is claimed to be analogous to how the human mind makes decisions (Kosko, 1986). A parameterized version of concept mapping called FCM allows you to create qualitative static models that are converted into semi-quantitative dynamic models. The computational dynamics of FCM are established by analyzing the construction and use of concept maps utilizing graph theory-based studies of pairwise structural interactions between the ideas included in a model (Barbrook-Johnson & Penn, 2022). Models such as these can be employed to investigate how people perceive a social or environmental issue or to simulate a complicated system when uncertainties are substantial and there is a dearth of empirical evidence.

Building FCM becomes a simple and straightforward task using the Mental Modeler software. After creating models, one may investigate various scenario changes by raising or lowering the model's component count (Gray et al., 2013). Because of its versatility and adaptability, FCM has been applied in various scientific fields, including political science, economics, and ecology.

Consequently, Mental Modeler software allows you to build the above FCMs quickly and intrinsically. It is an online application that enables the creation, uploading, and revision of models, assignment, and assessment of preferred states, viewing of component and model metrics, and execution of scenarios on models. We utilized the Mental Modeler (www.mentalmodeler.com). The activation rule was employed (Stylios & Groumpos, 2004), intrinsic to the modeler, as stated in the following equation:
(6)
where represent the value of the ith CSF in the k and k + 1 iterations, and the value of the jth CSF in the k iteration, respectively. We employed the sigmoid function as the transfer function integrated within the modeler.
The FCM developed for this study is shown in Figure 1. The CSFs' centrality explains the model's most relevant factor. Ordinary factors with lower centrality act like a bridge between the driver and receiver. Table 5 shows the centrality along with the type of CSF.
Table 5

Centrality of CSFs in the mode.

CSFsCentralityType
Leadership 2.64 Driver 
Community partnership 2.68 Receiver 
Interoperability 2.26 Ordinary 
Data privacy 1.62 Ordinary 
Cost of implementation 1.1 Ordinary 
Usability 2.18 Ordinary 
Technology awareness 2.2 Ordinary 
CSFsCentralityType
Leadership 2.64 Driver 
Community partnership 2.68 Receiver 
Interoperability 2.26 Ordinary 
Data privacy 1.62 Ordinary 
Cost of implementation 1.1 Ordinary 
Usability 2.18 Ordinary 
Technology awareness 2.2 Ordinary 
Fig. 1

An FCM-based model for the CSFs.

Fig. 1

An FCM-based model for the CSFs.

Close modal

Scenario analysis

It has been observed from Table 5 that the model has only one driver, i.e., leadership and community partnership is the receiver CSF. The water industry is primarily a public sector focused on efficiently delivering community services.

The other CSFs are ordinary and support the physical service system built with the help of streamlining smart devices and IoT in the network. Leadership plays a crucial role in implementing smart devices in the water industry. It is the primary driver that should support reformation as well as attitudes toward the development of the sector. It has an immense influence on the success of smart device implementation in the water and wastewater network. The climate conducive to implementation and sustainability is improved by leaders who demonstrate specific transformative and practical leadership attributes. These leadership traits have a domino effect on the other CSFs, leading to transformative changes and increasing the efficacy of the entire water and wastewater network.

The variation in the energy of the driver influences the impact strength of other CSFs for smart device implementation. Figure 2 shows the variation observed in other CSFs when the energy of leadership is changed to 1. That is to say that when leadership has a fully positive impact, it exerts a strong positive influence on all the other CSFs (Giri et al., 2024). The cost of implementation is found to be the foremost CSF that would be positively influenced, followed by technology awareness, community partnership, usability, interoperability, and data privacy. The cost of implementation is highly impacted as the decision-making process is heavily influenced by it. Leadership, being technology aware, would more readily accept the smart devices and avoid resistance to their implementation. Therefore, the high influence of the driver CSF positively impacts all the other CSFs, which, in turn, increases the implementation of smart devices in the water sector.
Fig. 2

Scenario analysis of the CSFs.

Fig. 2

Scenario analysis of the CSFs.

Close modal

The proposed research establishes the advantages of using a smart IoT framework in the water sector, including more efficient water distribution and management along with efficient recovery and reuse of wastewater.

The study identified the CSFs for implementing smart devices in the water industry. The study derives the CSFs from literature and in consultation with the experts using the Delphi methodology. Kappa statistics were further used to validate the output. The CSFs identified were leadership, usability, cost of implementation, technology awareness, data privacy, interoperability, and community partnership. A brainstorming session was conducted to develop an FCM using mental modeler software. Scenario analysis was conducted by varying the energy of the driver. Its effect on the other CSFs was observed.

The study serves as a springboard for creating awareness of the significance of integrating smart devices in the water industry. This research adds to the increasing body of knowledge since there is limited empirical literature on using the IoT paradigm in the water industry in general. It highlights the benefits of using smart devices in the water industry and refers to the effective distribution, recovery, reuse and management of a water and wastewater network. Highlighting the advantages of using smart devices can help critical decision-makers in the sector make better decisions.

Different stakeholders in the water sector can have multiple associations with the CSFs identified in the study. In order to generate the appropriate regulations for the industry, the government must establish guidelines and examples of a region that presents a quantifiable result for the effectiveness of the use of the IoT in water projects. Then, it might be desirable to create appropriate regulations that support the appropriate implementation of IoT in the water industry. Government regulations are essential in encouraging or mandating corporations to adopt an improved technology ecosystem with extensive IoT deployment. Government policy can initially inspire both the public and private sectors to adopt smart devices in the water sector. When creating and enforcing an implementation strategy, policymakers should also consider factors such as data privacy, usability, implementation cost, and interoperability because these factors are crucial to the effective adoption of smart devices.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

1

Deceased author.

Aarons, G. A., Ehrhart, M. G. & Farahnak, L. R. (
2014
)
The implementation leadership scale (ILS): Development of a brief measure of unit level implementation leadership
,
Implementation Science: IS
,
9
(
1
),
45
.
https://doi.org10.1186/1748-5908-9-45
.
Aengenheyster
S.
,
Cuhls
K.
,
Gerhold
L.
,
Heiskanen-Schüttler
M.
,
Huck
J.
&
Muszynska
M.
(
2017
)
Real-time Delphi in practice – A comparative analysis of existing software-based tools
,
Technological Forecasting and Social Change
,
118
,
15
27
.
doi:10.1016/j.techfore.2017.01.023
.
Ahmed
E.
,
Yaqoob
I.
,
Abaker
I.
,
Khan
I.
,
Ibrahim
A.
,
Imran
M.
&
Vasilkos
A.
(
2017
)
The role of big data analytics in internet of things
,
Computer Networks
,
129
,
459
471
.
Akpan
I. J.
,
Udoh
E. A. P.
&
Adebisi
B.
(
2020
)
Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic
,
Journal of Small Business & Entrepreneurship
,
34
(
2
),
123
140
.
https://doi.org/10.1080/08276331.2020.1820185
.
Alzoubi
H. M.
&
Aziz
R.
(
2021
)
‘Does emotional intelligence contribute to quality of strategic decisions? The mediating role of open innovation
,
Journal of Open Innovation: Technology, Market, and Complexity
,
7
(
2
),
130
.
https://doi.org/10.3390/joitmc7020130.
Balte
A.
,
Kashid
A.
&
Patil
B.
(
2015
)
Security issues in Internet of Things (IoT): A survey
,
International Journal of Advanced Research in Computer Science and Software Engineering
,
5
(
4
),
450
455
.
Barbrook-Johnson
P.
&
Penn
A. S.
(2022)
Fuzzy
cognitive mapping
. In:
Systems Mapping
,
Cham
:
Palgrave Macmillan
.
https://doi.org/10.1007/978-3-031-01919-7_6
.
Belton
I.
,
MacDonald
A.
,
Wright
G.
&
Hamlin
I.
(
2019
)
Improving the practical application of the Delphi method in group-based judgment: A six-step prescription for a well-founded and defensible process
,
Technological Forecasting and Social Change
,
147
,
72
82
.
https://doi.org/10.1016/j.techfore.2019.07.002
.
Burmeister
C.
,
Lttgens
D.
&
Piller
F. T.
(
2015
)
Business model innovation for industry 4.0: Why the industrial Internet mandates a new perspective on innovation
,
Die Unternehmung
,
70
(
2
),
1
31
.
Cahn
A.
(
2014
)
An overview of smart water networks
,
Journal-American Water Works Association
,
106
(
7
),
68
74
.
Cortellazzo
L.
,
Bruni
E.
&
Zampieri
R.
(
2019
)
The role of leadership in a digitalized world: A review
,
Frontiers in Psychology
,
10
.
https://doi.org/10.3389/fpsyg.2019.01938
.
Dworak
T.
(
2007
)
EU Water Saving Potential
.
Berlin, Germany
:
EU Environmental Protection Directorate
.
Elkhodr
M.
,
Shahrestani
S.
&
Cheung
H.
(
2016
)
The Internet of Things: New interoperability, management and security challenges
,
International Journal of Network Security & Its Applications
,
8
(
2
),
85
102
.
Farahnak
L. R.
,
Ehrhart
M. G.
,
Torres
E. M.
&
Aarons
G. A.
(
2019
)
The influence of transformational leadership and leader attitudes on subordinate attitudes and implementation success
,
Journal of Leadership & Organizational Studies
,
27
(
1
),
98
111
.
https://doi.org/10.1177/1548051818824529
.
Fleiss
J. L.
(
1971
)
Measuring nominal scale agreement among many raters
,
Psychological Bulletin
,
76
(
5
),
378
382
.
https://doi.org/10.1037/h0031619
.
Forbes, K. (2017) State of the Nation 2017: Digital Transformation. Institution of Civil Engineers (ICE). Retrieved August 22, 2023, from https://www.ice.org.uk/news-views-insights/policy-and-advocacy/policy-engagement/state-of-the-nation-2017-digital-transformation.
Giri
V.
,
Madaan
J.
,
Varma
N.
&
Charan
P.
(
2024
)
Flexible decision framework for resilient healthcare supply chain systems focusing pharmaceutical industry
,
Global Journal of Flexible Systems Management
,
25
(
3
),
487
512
.
doi:10.1007/s40171-024-00392-1
.
Global Water Intelligence
. (
2016
)
Water's Digital Future: the Outlook for Monitoring, Control and Data Management Systems
.
Oxford, UK
:
Global Water Intelligence
.
Gnatzy
T.
,
Warth
J.
,
Von Der Gracht
H.
&
Darkow
I. -L.
(
2011
)
Validating an innovative real-time Delphi approach – A methodological comparison between real-time and conventional Delphi studies
,
Technological Forecasting and Social Change
,
78
(
9
),
1681
1694
.
doi:10.1016/j.techfore.2011.04.006
.
Godwin
Z. R.
,
Bockhold
J. C.
,
Webster
L.
,
Falwell
S.
,
Bomze
L.
&
Tran
N. K.
(
2013
)
Development of novel smart device based application for serial wound imaging and management
,
Burns: Journal of the International Society for Burn Injuries
,
39
(
7
),
1395
1402
.
Gray
S.
,
Gray
S.
,
Cox
L.
&
Henly-Shepard
S.
(
2013
) ‘
Mental modeler: A fuzzy-logic cognitive mapping modeling tool for adaptive environmental management
’,
Proceedings of the 46th International Conference on Complex Systems
, pp.
963
973
.
Grime, M. M. & Wright, G. (2016) Delphi method. In: Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, W., Ruggeri, F. & Teugels, J. L., (eds.), Wiley StatsRef: Statistics Reference Online, pp. 1–6. Available at: https://doi.org/10.1002/9781118445112.stat07879.
Harwood
J.
,
Dooley
J. J.
,
Scott
A. J.
&
Joiner
R.
(
2014
)
Constantly connected – The effects of smart-devices on mental health
,
Computers in Human Behavior
,
34
,
267
272
.
Hasson
F.
,
Keeney
S.
&
McKenna
H.
(
2000
)
Research guidelines for the Delphi survey technique
,
Journal of Advanced Nursing
,
32
(
4
),
1008
1015
.
doi:10.1046/j.1365-2648.2000.t01-1-01567.x
.
Hussler
C.
,
Muller
P.
&
Rondé
P.
(
2011
)
Is diversity in Delphi panelist groups useful? Evidence from a French forecasting exercise on the future of nuclear energy
,
Technological Forecasting and Social Change
,
78
(
9
),
1642
1653
.
doi:10.1016/j.techfore.2011.07.008
.
Johnston
D.
(
2021
)
Seven Steps to Digital Transformation
.
WaterWorld
.
Kosko
B.
(
1986
)
Fuzzy cognitive maps
,
International Journal of Man-Machine Studies
,
24
(
1
),
65
75
.
https://doi.org/10.1016/s0020-7373(86)80040-2
.
Landis
J. R.
&
Koch
G. G.
(
1977
)
The measurement of observer agreement for categorical data
,
Biometrics, JSTOR
,
33
(
1
),
159
.
Longo
F.
,
Padovano
A.
&
Umbrello
S.
(
2020
)
Value-oriented and ethical technology engineering in industry 5.0: A human-centric perspective for the design of the factory of the future
,
Applied Sciences
,
10
(
12
),
4182
.
https://doi.org/10.3390/app10124182
.
Lv
Z.
,
Feng
S.
,
Feng
L.
&
Li
H.
(
2015
)
Extending touch-less interaction on vision based wearable device
,
IEEE Virtual Reality
, pp.
231
232
.
McFadden, K. L., Stock, G. N. & Gowen, C. R. (
2015
)
Leadership, safety climate, and continuous quality improvement: Impact on process quality and patient safety
,
Health Care Management Review
,
40
(
1
),
24
34
.
https://doi:10.1097/HMR.0000000000000006
.
Miller
M.
(
2015
)
The Internet of Things: how Smart TVs, Smart Cars, Smart Homes, and Smart Cities are Changing the World
.
Indianapolis: Pearson Education
.
Narang
D.
,
Madaan
J.
,
Chan
F. T. S.
&
Chungcharoen
E.
(
2023
)
Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach
,
Journal of Environmental Management
,
350
,
119609
.
doi:10.1016/j.jenvman.2023.119609
.
Narang
D.
,
Madaan
J.
,
Chan
F. T. S.
&
Charan
P.
(
2024
)
Evaluating prioritization of strategic business model for efficient wastewater resource management system
,
Journal of Cleaner Production
,
449
,
141271
.
doi:10.1016/j.jclepro.2024.141271
.
Ogonji
M. M.
,
Okeyo
G.
&
Wafula
J. M.
(
2020
)
A survey on privacy and security of Internet of Things
,
Computer Science Review
,
38
,
100312
.
https://doi.org/10.1016/j.cosrev.2020.100312
.
Owen
D. A. L.
(
2018
)
Smart Water Technologies and Techniques: Data Capture and Analysis for Sustainable Water Management
, vol.
2018
.
Oxford: John Wiley & Sons, Ltd
.
ISBN: 9781119078647.
Saarikko
T.
,
Westergren
U. H.
&
Blomquist
T.
(
2020
)
Digital transformation: Five recommendations for the digitally conscious firm
,
Business Horizons
,
63
(
6
),
825
839
.
https://doi.org/10.1016/j.bushor.2020.07.005
.
Sedlack
D.
(
2016
) ‘
The limits of the water technology revolution
’,
Presentation. Presentation to the NUS Water Megatrends Workshop
.
NUS
:
Singapore
.
Shardeo
V.
,
Madaan
J.
&
Chan
F. T.
(
2022
)
An empirical analysis of freight mode choice factors amid the COVID-19 outbreak
,
Industrial Management & Data Systems
,
122
(
12
),
2783
2805
.
https://doi.org/10.1108/imds-03-2022-0133
.
Silverio
M.
,
Renukappa
S.
,
Suresh
S.
,
Donastorg
A.
, (
2017
)
Mobile computing in the construction industry: Main challenges and solutions
. In:
Benlamri
R.
&
Sparer
M.
(eds.)
Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy
. New York:
Springer
, pp.
85
99
.
Silverio-Fernández
M.
,
Renukappa
S.
&
Suresh
S.
(
2018
)
What is a smart device?- A conceptualisation within the paradigm of the Internet of Things
,
Visualization in Engineering
,
6
(
1
),
15
29
.
Sivaraman
V.
,
Gharakheili
H. H.
,
Fernandes
C.
,
Clark
N.
&
Karliychuk
T.
(
2018
)
Smart IoT devices in the home: Security and privacy implications
,
IEEE Technology and Society Magazine
,
37
(
2
),
71
79
.
Smets
H.
(
2008
) ‘
Water for domestic uses at an affordable price
’,
Presentation to the International Conference on the Right to Water and Sanitation in Theory and Practice
.
Oslo
, Norway
.
Stankovic
J.
(
2014
)
Research directions for the Internet of Things
,
IEEE Internet of Things Journal
,
1
(
1
),
3
9
.
Stojkoska
B. L. R.
&
Trivodaliev
K. V.
(
2017
)
A review of Internet of Things for smart home: challenges and solutions
,
Journal of Cleaner Production
,
140
,
1454
1464
.
Stylios
C. D.
&
Groumpos
P. P.
(
2004
)
Modeling complex systems using fuzzy cognitive maps
,
IEEE Transactions on Systems Man and Cybernetics – Part A Systems and Humans
,
34
(
1
),
155
162
.
doi:10.1109/tsmca.2003.818878
.
Sumic, Z. (2015) Hype Cycle for Smart Grid Technologies, 2015. Gartner Research. Retrieved June 2, 2022, from https://www.gartner.com/en/documents/3092617?%20%20ref=ddisp.
Sweeney
M. W.
(
1999
)
Geographic information systems
,
Water Environment Research
,
71
(
5
),
551
556
.
Usman
M.
&
Zhang
X.
(
2014
)
A framework for realizing universal standardization for Internet of Things
,
Journal of Industrial and Intelligent Information
,
2
(
2
),
147
153
.
Weyns
D.
,
Ramachandran
G.
&
Singh
R.
(
2018
)
Self-managing Internet of Things
. In:
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Goos, G., ed.). Springer Nature, pp.
67
84
.
10706 LNCS, Krems. https://doi.org/10.1007/978-3-319-73117-9_5
.
Williams
R.
,
McMahon
E.
,
Samtani
S.
,
Patton
M.
&
Chen
H.
(
2017
)
Identifying vulnerabilities of consumer Internet of Things (IoT) devices: A scalable approach
,
2017 IEEE International Conference on Intelligence and Security Informatics (ISI)
.
Beijing, China
, pp. 179–181.
WWAP (United Nations World Water Assessment Programme) (2017) UN World Water Development Report 2017 – Wastewater, the Untapped Resource. Paris, UNESCO. Retrieved July 12, 2022, from https://www.unesco.org/en/wwap/wwdr/2017.
Zeferino
A. J.
,
Cunha
C. M.
&
Antunes
P. A.
(
2012
)
Robust optimisation approach to regional wastewater system planning
,
Journal of Environmental Management
,
109
,
113
122
.
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