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The concept of “Smart Cities” is gaining prominence across the world as a solution to effectively address the issues or impediments faced by cities due to rapid urbanization. The purpose of this paper is to identify the key factors which form the primary basis for the implementation of “Smart Cities”. Particularly, this paper aims to analyse the contextual relationship and driving/dependence power of these key factors and model these using the total interpretive structural modelling (“TISM”) framework.

The key factors which form the basis for the implementation of Smart Cities were identified through an evaluation of the literature on “Smart Cities” and expert opinions. Thereon, the contextual relationship between these key factors was examined with the help of experts. Thereafter, these key factors were modelled using the total interpretive structured modelling (“TISM”) framework. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was further applied to classify the factors. It is pertinent to note that the driving power and dependence of these key factors were also reviewed.

This paper establishes a TISM of the key factors for the implementation of “Smart Cities” which will aid in examining the interrelationship among the factors and will also identify the hierarchy among these factors. On extensive examination of the literature and expert opinions on “Smart Cities”, it can be asserted through TISM that quality of life (F1), e-services adoption (F5) and economic growth (F8) are the leading factors in establishing “Smart Cities”. Furthermore, it must be noted that the MICMAC analysis and driving-dependence graph helps in classifying the key factors as autonomous factors, drivers, linkages and outcomes, which assists in comprehending which factors possess driver power and which are exhibiting dependency.

The contribution lies in the authentic manner in which this paper attempts to use the TISM approach combined with MICMAC analysis to model key factors for the implementation of “Smart Cities”; which would aid and assist policymakers and practitioners to construct a structural framework for the implementation of “Smart Cities” through identification of drivers, linkages and outcomes.

The purpose of this paper is to explore whether and how the intellectual capital (IC) approach and concepts could be fruitfully adapted to study the smart city phenomenon from a managerial point of view.

This study is based on a long-term, in-depth ethnographic exploration of the vast global community, which is created around the smart city movement.

The analysis suggests that, in order to effectively analyse a smart city context through the IC lens, the traditional IC framework needs to be extended for: expected outcomes, which should also include sustainability, resilience and quality of life; categories of key resources, which should also include institutional capital and environmental capital; units of analysis, which should also include territorial systems, such as transportation or waste; and key managerial challenges implied. As a final result, a smart city intellectual capital (SC-IC) framework is proposed.

Most of the cases analysed in this study are European; further studies are advisable to better investigate non-European smart city contexts.

The paper suggests that the knowledge management, project portfolio management and network management approaches are crucial to better support managerial practices in smart city organizations.

The SC-IC framework allows for a clear definition of the smart city organization, as a new knowledge-based, project-oriented, network-shaped type of organization. Therefore, the SC-IC framework provides smart city research with a consistent rooting in management studies. Further, this paper contributes to the fourth stage of IC research.

To devise a biometric‐based mechanism for enhancing security of private keys used in cryptographic applications.

To enhance security of a private key, we propose a scheme that regenerates a user's private key by taking a genuine user's password, fingerprint and a valid smart card. Our scheme uses features extracted from fingerprint along with public key cryptography, cryptographic hash functions and Shamir secret sharing scheme in a novel way to achieve our desired objectives.

Despite changes in the fingerprint pattern each time it is presented, our scheme is sufficiently robust to regenerate a constant private key. As compared to conventional methods of storing a private key merely by password‐based encryption, our scheme offers more security as it requires a genuine user's password, fingerprint and a valid smart card. Key lengths up to 1024‐bit or even higher can be regenerated making the scheme compatible with the current security requirements of public key cryptosystems.

Minutia points used for image alignment can be incorporated in the key regeneration algorithm for stronger user authentication. In this case, some alternative technique will be required for image alignment.

The robustness of our scheme depicts its use in practical systems where there are variations in fingerprint patterns because of sensor noise and alignment issues.

In this paper, we have demonstrated a novel idea of regenerating the private key of a user by using fingerprint, password and a smart card. The basic aim is to provide more security to key storage as compared to traditional methods that uses password‐based encryption for secure storage of private keys.

The purpose of this study is to verify the effect of smart pedagogy to facilitate the high order thinking skills of students and to provide the design suggestion of curriculum and intelligent tutoring systems in smart education.

A smart pedagogy framework was designed. The quasi-experiment was conducted in a junior high school. The experimental class used the smart pedagogy and smart learning environment. The control class adopted conventional teaching strategies. The math test scores of these two classes were compared to verify the effectiveness of smart pedagogy.

The smart pedagogy framework contains three sections including the situated learning (S), mastery learning (M), adaptive learning (A), reflective learning (R) and thinking tools (T) (SMART) key elements model, the curriculum design method and detailed teaching strategy. The SMART key elements model integrates the situated learning, mastery learning, adaptive learning, reflective learning and thinking tools to facilitate the high order thinking. The curriculum design method of smart pedagogy combines the first five principles of instruction and the SMART key elements model to design the curriculum. The detailed teaching strategies of smart pedagogy contain kinds of innovative learning methods. The results of the quasi-experiment proved that the learning outcome was significantly promoted by using smart pedagogy.

This research investigates a general framework that can be used to cultivate the high order thinking skills in different subjects and grades was one of the first to introduce high order thinking skills into smart education. The framework of smart pedagogy was innovative and effect in practice.

This study examines the factorial structure of salient user beliefs associated with smart locks. We also examine the predictive value of the identified constructs on the smart lock adoption intention and we evaluate gender differences in the predictive value of the identified constructs.

The study assumes pragmatic epistemological stance and it leverages mixed-methods research design. The research progresses through three stages: belief elicitation, exploratory factor analysis and confirmatory factor analysis within a nomological network. New groups of participants were recruited for each stage of the study.

We find that while potential adopters express a broad range of perceived benefits and concerns associated with smart locks, only the perceived relative advantage of smart locks vis-à-vis conventional locks in providing safety and security is significantly correlated with adoption intention for both genders. We also find that perceived novel benefits are a significant predictor of the smart lock adoption intention for women, but not for men.

Our results indicate that perceived relative advantage can be the singular critical consideration in the adoption of smart home technologies that replace incumbent solutions. The results also demonstrate that gender-specific models can better capture gender effects that influence technology adoption and use.

Smart home technology vendors would need to convince prospective users that new technology is better than the incumbent solutions on the core affordances of the incumbent technology. Men and women differ in the consideration of novel benefits afforded by novel technologies.

This is among the first studies to examine salient beliefs that affect smart home technology adoption. The findings suggest that the traditional models (TAM, UTAUT) do not capture the key salient beliefs that can influence innovative smart home technology adoption. The study also suggests that gendered models are needed to understand technology adoption in contexts where technology adoption intersects with gender roles.

There are 29 million homes in the UK, accounting for 14% of the UK's energy consumption. This is given that UK has one of the highest water and energy demands in Europe which needs to be addressed according to the Committee on Climate Change (CCC). Smart homes technology holds a current perception that it is principally used by “tech-savvy” users with larger budgets. However, smart home technology can be used to control water, heat and energy in the entire house. This paper investigates how smart home technology could be effectively utilised to aid the UK government in meeting climate change targets and to mitigate the environmental impact of a home in use towards reducing carbon emissions.

Both primary and secondary data were sought to gain insight into the research problem. An epistemological approach to this research is to use interpretivism to analyse data gathered via a semi-structured survey. Two groups of participants were approached: (1) professionals who are deemed knowledgeable about smart home development and implementation and (2) users of smart home technology. A variety of open-ended questions were formulated, allowing participants to elaborate by exploring issues and providing detailed qualitative responses based on their experience in this area which were interpreted quantitatively for clearer analysis.

With fossil fuel reserves depleting, there is an urgency for renewable, low carbon energy sources to reduce the 5 tonnes annual carbon emissions from a UK household. This requires a multi-faceted and a multimethod approach, relying on the involvement of both the general public and the government in order to be effective. By advancing energy grids to make them more efficient and reliable, concomitant necessitates a drastic change in the way of life and philosophy of homeowners when contemplating a reduction of carbon emissions. If both parties are able to do so, the UK is more likely to reach its 2050 net-zero carbon goal. The presence of a smart meter within the household is equally pivotal. It has a positive effect of reducing the amount of carbon emissions and hence more need to be installed.

Further research is needed using a larger study sample to achieve more accurate and acceptable generalisations about any future course of action. Further investigation on the specifics of smart technology within the UK household is also needed to reduce the energy consumption in order to meet net-zero carbon 2050 targets due to failures of legislation.

For smart homes manufacturers and suppliers, more emphasis should be placed to enhance compatibility and interoperability of appliances and devices using different platform and creating more user's friendly manuals supported by step-by-step visual to support homeowners in the light of the wealth of knowledge base generated over the past few years. For homeowners, more emphasis should be placed on creating online knowledge management platform easily accessible which provide virtual support and technical advice to home owners to deal with any operational and technical issues or IT glitches. Developing technical design online platform for built environment professionals on incorporating smart sensors and environmentally beneficial technology during early design and construction stages towards achieving low to zero carbon homes.

This paper bridges a significant gap in the body of knowledge in term of its scope, theoretical validity and practical applicability, highlighting the impact of using smart home technology on the environment. It provides an insight into how the UK government could utilise smart home technology in order to reduce its carbon emission by identifying the potential link between using smart home technology and environmental sustainability in tackling and mitigating climate change. The findings can be applied to other building types and has the potential to employ aspects of smart home technology in order to manage energy and water usage including but not limited to healthcare, commercial and industrial buildings.

The purpose of this paper is to investigate how multinational enterprises (MNEs) manage human resources (HR) in explorative and exploitative alliances in smart city projects (SCPs).

In this paper, the authors adopt an explorative and qualitative approach based on multiple case studies thanks to the interviews with 21 smart city managers of MNEs who are deeply involved in SCPs.

The authors found that MNEs use many different partnerships and “temporal separation” in many cities all around the world in order to maximize the benefits of both exploration and exploitation. According to the aim of the project, MNEs implemented different HR practices intentionally targeted toward managing social relations among internal and external employees involved in SCPs.

The authors highlighted that MNEs tend to develop different ties among employees and external partners and to use different HR practices according to the nature and to the aim of the alliances. Thus, the development of human resource management systems becomes crucial in supporting organizational ambidexterity through alliances.

This paper gives useful insights in improving the effectiveness of MNEs in SCPs. Due to the business opportunities arising from the application of ICT and technological innovation to urban services, MNEs are becoming an important player in smart cities. Increasing the effectiveness of the SCPs leads faster to more economically, socially and environmentally sustainable cities.

The development of alliances has a key role in strengthening and complementing firms’ exploration and exploitation agendas in SCPs. Thus, this paper provides guidelines to MNEs in order to adapt HR practices and to rethink the role of HR within and across corporate boundaries in an emergent context of analysis.

Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.

The purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.

We have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.

We apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.

This research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.

Findings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.

The findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.