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Currently, electronic election is one of the most popular issues of e-democracy. This has led to the development of applications and several security mechanisms to address such necessity. The problem that arises is that such applications are created either on demand for a specific election process, or experimentally for scientific purposes. The purpose of this study is to present a new e-voting system, called VOTAN. The VOTAN system involves a combination of new features with basic advantages, the implementation as open source software, its modular organization covering the functional requirements of a typical electronic voting system (EVS) and the capability of data analysis of candidates and voters.

VOTAN stands for VOTes Analyzer. It is a secure application for the conduct of electronic elections through the internet based on its own security protocol. It also includes a data analysis component which analyzes the election results and investigates the factors that play a crucial role. The major advantages of the system are that it is an open source and includes a data analysis module that can distinguish important variables from the elections and help make predictions for the outcome based on the selected variables. It is a practical solution to the existing e-voting applications and is ideal for small communities such as organizations, universities and chambers.

Its main advantage, compared to similar e-voting systems, is the integration of the data analysis component. The analysis of the data produced from elections is considered a critical process to fully comprehend the outcome of the elections and its correlation to specific attributes/variables of the election process. The data analysis module is a unique feature of VOTAN. It facilitates the selection of the most important attributes that influence the outcome of elections and creates a mathematical model to predict the outcome of an election based on the selected attributes. The method used in the module is the LDA.

The originality of the paper derives from the data analysis component and its security protocol/schema that fulfils several requirements.

Assembly variations, which will propagate along the assembly process, are inevitable and difficult to analyze in Aeronautical Thin‐Walled Structures (ATWS) assembly. The purpose of this paper is to present a new method for analyzing the variation propagation of ATWS with automated riveting.

The paper addresses the variation propagation model and method by first, forming a novel Stage‐State model to represent the process of automated riveting. Second, the effect of positioning error on assembly variation is defined as propagation variation (PV), and propagation matrix of key characteristic points (KCP) is discussed. Third, the effect between the variations in each stage is defined as expansion variation (EV). According to the analysis of mismatch error and the reference transformation, the expansion matrix is formed.

The model can solve the variation propagation problem of ATWS with automated riveting efficiently, which is shown as an example of this paper.

The variation obtained by the model and method presented in this paper is in conformity with the variation measured in experiments.

The propagation variation and expansion variation is proposed for the first time, and variations are studied according to novel propagation matrix and expansion matrix.

The paper developed a novel gallic acid-based rust conversion emulsion (RCE) that is applied in the treatment of rusted steels. The purpose of this paper is to investigate the methods for the synthesis of RCE and study the mechanism of rust conversion.

Conversion emulsion was prepared using styrene, acrylate and self-developed gallic acid (GA)-based rust converter (GRC) via seed emulsion polymerisation. The polymerisable GRC was synthesised by the ring-opening reaction of glycidyl methacrylate with natural GA. The effects of the GRC dosage and its feeding modes on the RCE synthesis were analysed. The corrosion resistance, surface morphology, composition and mechanism of rust conversion coatings were studied using electrochemical tests, scanning electron microscopy, Raman spectroscopy and X-ray photoelectron spectroscopy (XPS), respectively.

The results showed that conversion coating on rusted steels treated with RCE, with various dosages of GRC (weight per cent), synthesised using seed emulsion polymerisation, exhibited the best adhesion and corrosion resistance. Raman spectroscopy revealed that RCE converted the original multiphase rust into stable crystalline phases of α-Fe₂O₃ and Fe₃O₄. According to XPS and energy dispersive X-ray analysis, the phenolic hydroxyl groups of RCE were proposed to chelate with Fe ions to form macromolecular ferrum compounds.

The pre-rusted steels demonstrated a better corrosion resistance than rust-free steels after treatment with RCE.

The paper developed a novel GA-based RCE with high efficiency and environment-friendly method.

This work is expected to replace the conventional rust conversion paints and cause a significant impact on extending the service life of rusted steels.

The purpose of this paper is to show that numerous studies have advanced social capital research over the past decade. Most studies have accepted the theoretical distinction between bonding and bridging social capital networks. Many, however, tend to agglomerate empirical research under the one catch‐all social capital concept, rather than classifying it according to the bonding/bridging distinction. Furthermore, most studies make little distinction on the basis of methodology, between qualitative and quantitative approaches to investigating social capital. These omissions need to be addressed.

This paper reviews definitions and applications of bridging and bonding social capital, classifies empirical studies according to each network type, and produces a further breakdown according to methodological approach.

The result is a four‐part “grid” of social capital research, encompassing bonding and bridging, and quantitative and qualitative aspects. This paper finds that most qualitative research examines non‐excludable and excludable goods and is relevant to bonding social capital, whilst most quantitative analysis looks at civic networks and norms of trust, and relates to bridging social capital.

Results advance the task of clarifying and measuring social capital.

Further development of the bridging/bonding social capital conceptual pair should allow for a more precise measurement of a community, or region.

No review paper to date captures the above empirical and methodological “grid” clearly.

The study aimed at analyzing operations managers’ perception of the use of electric vehicles (EVs) in business processes and its impact on overall business process cost (BPC) and sustainable development (SD).

The present study adopts the triangulation approach which is a combination of quantitative and qualitative methods. The data was collected using structured and scientifically tested questionnaires from the industrial managers working in the industries in the Mysore region of Karnataka. Descriptive statistics, factor analysis and structural equation models were employed to analyze and interpret the data.

The findings revealed that the usage of EVs in Business Processes significantly impacts the BPC (b = 0.851, t = 8.037, p < 0.01) and it is also the usage of EVs in business processes can significantly impact SD (b = 0.889, t = 7.923, p < 0.01). Thus, the adoption of EVs in the business process offers many benefits to business organizations such as minimized operational costs, an eco-friendly business model, more tax incentives, less BPCs, a low-emission footprint and a contribution towards SD at large.

Many business organizations operating in the present time show interest in employing EVs in their business processes. Hence, before introducing EVs in industries on a large scale, it becomes imperative to obtain the perception of industrial managers who have already experienced its impact. This study may help industrial organizations to understand the impact of EV on various aspects of the business and to design a business model which would help in achieving SD goals.

The use of EVs in the daily life of human beings and business activities is gaining importance because of the various positive impacts. Therefore, it is necessary to understand industrial managers’ opinions regarding the use of EV in business activities.

Due to technological improvement and development of the vehicle-to-home (V2H) concept, electric vehicle (EV) can be considered as an active component of net-zero energy buildings (NZEBs). However, to achieve more dependable results, proper energy analysis is needed to take into consideration the stochastic behavior of renewable energy, energy consumption in the building and vehicle use pattern. This study aims to stochastically model a building integrating photovoltaic panels as a microgeneration technology and EVs to meet NZEB requirements.

First, a multiobjective nondominated sorting genetic algorithm (NSGA-II) was developed to optimize the building energy performance considering panels installed on the façade. Next, a dynamic solution is implemented in MATLAB to stochastically model electricity generation using solar panels as well as building and EV energy consumption. Besides, the Monte Carlo simulation method is used for quantifying the uncertainty of NZEB performance. To investigate the impact of weather on both energy consumption and generation, the model is tested in five different climatic zones in Iran.

The results show that the stochastic simulation provides building designers with a variety of convenient options to select the best design based on level of confidence and desired budget. Furthermore, economic evaluation signifies that investing in all studied cities is profitable.

Considering the uncertainty in building energy demand and PV power generation as well as EV mobility and the charging–discharging power profile for evaluating building energy performance is the main contribution of this study.

Electric mobility as a service (eMaaS) is suggested as a possible solution to ease transportation and lessen environmental issues by providing a collaborative transport sharing infrastructure that is based on electric vehicles (EVs) such as electric cars, electric bicycles and so on. Accordingly, this study aims to propose a multi-tier architecture to support the collection, processing, analytics and usage of mobility data in providing eMaaS within smart cities. The architecture uses application programming interfaces to enable interoperability between different infrastructures required for eMaaS and allow multiple partners to exchange and share data for making decision regarding electric mobility services.

Design science methodology based on a case study by interview was used to collect data from an infrastructure company in Norway to verify the applicability of the proposed multi-tier architecture.

Findings suggest that the architecture offers an approach for collecting, aggregating, processing and provisioning of data originating from sources to improve electric mobility in smart cities. More importantly, findings from this study provide guidance for municipalities and policymakers in improving electric mobility services. Moreover, the author’s findings provide a practical data-driven mobility use case that can be used by transport companies in deploying eMaaS in smart cities.

Data was collected from a single company in Norway, hence, it is required to further verify the architecture with data collected from other companies.

eMaaS operates on heterogeneous data, which are generated from EVs and used by citizens and stakeholders such as city administration, municipality transport providers, charging station providers and so on. Therefore, the proposed architecture enables the sharing and usage of generated data as openly available data to be used in creating value-added services to improve citizen’s quality of life and viability of businesses.

This study proposes the deployment of electric mobility to address increased usage of vehicles, which contributes to pollution of the environment that has a serious effect on citizen’s quality of life.

This study proposes a multi-tier architecture that stores, processes, analyze and provides data and related services to improve electric mobility within smart cities. The multi-tier architecture aims to support and increase eMaaS operation of EVs toward improving transportation services for city transport operators and citizens for sustainable transport and mobility system.

The purpose of this study is to understand the specific reasons why developed countries could easily start implementing innovative alternative fuel vehicles (e.g. electric vehicles or EVs) while the implementation in developing countries looks so far-fetched, with respect to infrastructure and downstream activities, and suggest the steps that can be taken to effectively address these issues.

This research undertakes case study – Tesla (USA), Mahindra and Mahindra (India) and Tata Motors to bring out the problems being faced by manufacturers from developing countries vis-a-vis the developed countries. The consumers’ side has been adequately represented though an in-depth survey. An analysis is also carried out as to how Tesla has accrued competitive leverage by innovating and vertical integration of up as well as downstream systems.

EV infrastructure remains grossly inadequate in developing countries like India. Two key areas that remain significantly unexplored are the installation of charging stations at parking lots and at the housing clusters and lack of competitive leverage in the services, processes and other downstream systems due to limited research and development capabilities. The performance metrics of domestic EVs lag those of conventional vehicles as well as foreign competitors like Tesla. Range anxiety is ranked as number one in the major concerns among the potential mass buyers of electric vehicles in India.

The value of the paper lies in an in-depth analysis of the relationship between horizontal and vertical perspectives as well as the impact of the product eco-system innovation on both the upstream as well as downstream nodes in the supply chain. Whereas the consumer attitudes and perspectives on e-mobility are inferred from a survey, the impact analysis matrix is used for analyzing the competitive leverage of Tesla through several features in the upstream, downstream and servitization.

This study has a principal objective of exploring and analyzing the factors affecting adoption or acceptance of eco-friendly electric vehicles in India.

Primary data were gathered through a predesigned questionnaire from 355 vehicle owners of Delhi using simple random sampling method. Statistical softwares like SPSS and AMOS were used to analyze the primary data collected for the study. Statistical techniques like exploratory factor analysis, confirmatory factor analysis and structural equation modeling were usesd to infer a meaningful conclusion from the investigation.

Through this study, a statistically significant relationship between environmental concern, knowledge of EV, subjective norm and attitude toward electric vehicle were investigated. Similarly attitude is responsible for a significant variation in adoption decision.

The findings of the present study underscore the requirement for marketers to build up a systematic strategy for easier adoption of electric vehicles in India. On account of this, this study developed a model of potential determinants of adoption decision toward EVs in India, based on statistically significant evidences.

This study adds to the body of literature by exploring and testing the factors affecting the adoption of eco-friendly products like electric vehicle that were not emphasized earlier in Indian context and second by growing theoretically consistent operationalization for the marketing of electric vehicles. These upgrades brought about a last model with an unrivaled illustrative power.

The current study aims to investigate the various consumption motives (hedonic, gain and normative) responsible for strengthening consumers' intentions toward purchase behavior for electric vehicle (EV).

A total of 411 valid survey responses were collected using a structured questionnaire. Data were analyzed using confirmatory factor analysis and structural equation modeling to investigate the empirical fit of the hypothesized framework.

The results of structural equation modeling revealed that all three motives were positively correlated with purchase intentions for EV. Hedonic motives were found to have the strongest influence on purchase intentions. In addition, gain and normative motives were also found to be significant predictors of EV buying behavior. Further analysis revealed a positive correlation between gain, normative and hedonic motives. Moreover, personal moral standards seem to have a significant and positive impact on the positive emotions associated with buying EV.

The results of current research can be useful for marketers while designing promotional strategies for all the high-involvement green products. Marketing professionals and policymakers can use these results to build effective marketing strategies for EVs and reduce greenhouse gas emissions resulting from personal vehicle use.

To the best of the authors' knowledge, this is the first study in the South Asian region that explores consumers' motives for EV purchase behavior. Further, this is among a few studies, which have attempted to investigate the impact of hedonic, gain and normative motives on green purchase behavior in the context of high involvement green products.