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The purpose of this paper is to narrow the research gap by examining the relationship between new ecological paradigm (NEP), consideration of future consequences (CFC), the intention to buy and the intention to pay a premium in the context of electric car (EC) purchase in India.

This study used a structured questionnaire to measure the variables of the research. The study successfully obtained useable data from a sample of 491 consumers residing in India. The analysis of the variables and their relationships was done using structural equation modelling using SMARTPLS4 software.

The relationship between the values of NEP and CFC was observed in the context of electric cars that has a significant impact on the intention to buy and pay a premium. It also highlights the role of CFC future and CFC immediate on the intention to buy and between NEP and the intention to pay a premium.

The study only covers electric cars, and therefore further testing of these relationships is required in the context of other forms of environmentally friendly transportation. The results are generalizable across the potential consumers of EC but are even more pertinent to higher-income millennial consumers.

Potential buyers of electric cars, having a positive orientation towards the environment and also consideration for future consequence, were observed to have a stronger intention to buy EC. The study finds a way in increasing the intention to buy an EC by catalyzing environmental concern of consumers through CFC future.

This is the first study that has examined the NEP-CFC relationship, and provides evidence that the intention to buy an electric car is not only NEP (environmental concern)-dependent but also considers CFC's future orientation. This study adds the CFC aspect as another important variable regarding the purchase of EC, and proves that environmental concern is not the only moderating factor to buy an EC.

Although many health and safety (H&S) studies have widely examined safety risk perception in the construction industry, few studies have explored how this perception influences site workers' risk-taking behaviours during construction. This study aims to examine how construction site workers perceive and judge safety risks in risk-taking behaviours of site workers for intervention safety policy framework that may encourage safe work.

The study employed Pictorial-based Q-Methodology, which documented 63 picture scenarios of risk-taking behaviours from building sites and submitted them for validation from H&S inspectors. In total, 33 pictures emerged as having great potential to cause harm. After using these 33 pictures to elicit data from randomised site workers, the study used Frequency Tabulation, Relative Importance Index (RII) and Kruskal–Wallis Test to analyse the collected data. To fully explain the analysed data for deeper understanding, the study conducted Focus Group Discussions (FGDs) with these site workers to share the thoughts of site workers on these pictures.

Two distinctive pictures emerged from these analyses: one showing risk-taking behaviour likely to contract internal and skin disease and the other likely to fall from height. One of the implications is that construction site workers are unfamiliar with the dangerous contaminants in the materials the site workers use to work, which can potentially harm the site workers' skin and internal organs. Hence, site workers continue engaging in risk-taking behaviours. The other is that site workers are aware of and can mention catastrophic physical injuries attached to site workers' jobs. However, site workers continue engaging in risk-taking behaviours because of site workers' safety plights and rely on the favour and mercies of a supreme being as coping strategies to escape from these physical injuries.

This study is original in that the study uses picture scenarios of risk-taking behaviours to amass an empirical-based understanding of how site workers perceive and respond to H&S risks during construction. This piece of evidence is missing in the numerous research studies in this area. Again, the findings contribute to the state-of-the-art literature regarding risk-taking behaviours on construction sites.

This paper aims to examine and compare the associations between financial capability and financial anxiety (FA) before and during the coronavirus disease 2019 (COVID-19) pandemic. Specifically, financial capability is measured by three indicators: financial knowledge, financial behavior and financial confidence. This study also examines and compares the association among different income groups before and during the pandemic.

Data are from 2018 to 2021 National Financial Capability Study (NFCS). Structural equation modeling (SEM) is employed to examine the direct and indirect associations between financial capability factors and FA. Furthermore, this paper also conducts multi-group SEM for three income groups to examine the heterogeneous effects of household income.

Both before and during the pandemic, financial knowledge is directly positively and financial behavior is directly negatively associated with FA. In addition, both financial knowledge and financial behavior are positively associated with financial confidence, which in turn is negatively associated with FA. However, when taking the indirect effects into consideration, the total effects of financial capability factors on FA are all negative. Furthermore, the pandemic has intensified the negative association between financial behavior and FA rather than financial knowledge or financial confidence. Multi-group SEM shows that the positive direct effects of financial knowledge are only significant in the low-income group, while the negative direct effects of financial behavior are only significant in the low- and middle-income groups before the pandemic. However, direct effects of financial knowledge and financial behavior are significant in all income groups during the pandemic.

First, this study specifies a construct, financial confidence, to proxy perceived financial capability. Second, it examines the mediating role of financial confidence in the association between the other two financial capability factors (financial knowledge and financial behaviors) and FA. Third, it also compares the associations between financial capability factors and FA before and during the COVID-19 pandemic.

The description of “blockchain banking”, the determination of “the sub-processes” of “blockchain banking” as a “business process”, and the assessment of “maturity level” in Parsian Bank.

Theoretical sources on “blockchain banking” were initially investigated. Then the “sub-processes” of “blockchain banking” as a “business process” were extracted by Parsian Bank's experts through the “Delphi method”. Next, the “sequence” of the “sub-processes” was determined by means of the “AHP”. Eventually, Parsian Bank's maturity levels for all the sub-processes as well as the overall maturity level were specified on the basis of the “CMMI” V1.3 in order for Business Process Management (BPM).

Blockchain banking’ combines traditional banking with cryptocurrencies, which can be provided by merging “hybrid e-wallet” with “bank account” and “bank card” – all together as “crypto bank account”. Plus, “hybrid e-wallet” is a form of mobile e-wallet on blockchain that supports both cryptocurrencies and traditional currencies in the same platform by which the purchase and sale of cryptocurrencies are possible. Besides, “Blockchain banking service” can also be offered within the framework of “open banking” aligned with “open innovation” through a FinTech (or a beta bank) in collaboration with a licensed bank via “open API”, which is called “blockchain banking based on FinTech”. At last, the eight sub-processes of “blockchain banking” were determined and Parsian Bank's “maturity level” was specified.

This is the very first practical guide to “blockchain banking service”.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

Blockchain-based traceability technology (BTT) is an emerging digital technology that claims to have the potential to fulfil the demand for traceability to safeguard fruit safety. Drawing on the technological, organisational and environmental (TOE) framework, this study aims to investigate the perceived facilitators and barriers that influence the behavioural intentions of multiple stakeholders in the Vietnamese fruit supply chain (i.e., farmers, trading enterprises and consumers) to adopt BTT.

This study utilised a qualitative approach of semi-structured in-depth interviews with 60 stakeholders in the Vietnamese fruit supply chain to achieve the research objectives. NVivo 12 was employed to analyse the collected data using content and thematic analysis.

The findings identify several perceived facilitators that motivate BTT adoption. These include trust, transparency, business performance, the formation of alliances, consumer awareness of food safety and ethical agricultural practices, fruit branding and the pivotal role of farmers' cooperatives. Meanwhile, the perceived barriers to BTT adoption include a lack of digital literacy amongst the stakeholders, poor organisational culture, the high cost of traceability-enabled products and data privacy and security governance.

This study suggests that technology awareness and perceived facilitators and barriers should be incorporated into the design and deployment of blockchain-based traceability technology in the agri-food supply chain in developing countries.

To the best of the authors' knowledge, this study is the first qualitative paper that attempts to fill the research gap of understanding the perceived facilitators and barriers that influence the intentions of multiple stakeholders in the fruit supply chain to adopt BTT in the context of a developing economy.

This study aims to provide insight into consumer behavior regarding the use of food delivery apps when making purchases. To investigate the primary elements affecting users' intentions to use meal delivery applications, this study suggests an extension to the technology acceptance model through some contextual variable such as “various food choices (VFC),” “trust (TRR),” “perception of COVID-19-related risks (PCR)” and “convenience (CONV)” during the pandemic.

A cross-sectional data of 407 was collected in the Indian context. This research adopts the covariance-based structural modeling approach to test the hypotheses along with hierarchical regression to predict the efficiency of constructs.

Considering the outcomes, “perceived usefulness (PU)” was positively influenced by “perceived ease of use (PEOU),” “VFC” and “CONV.” In addition, the attitude (ATT) was positively impacted by “PU,” “TRR” and “PEOU.” Nevertheless, “PCR” negatively influenced ATT. In additional, this research illustrates the positive impact of ATT and PU on behavioral intention to use.

By confirming the technology acceptance model's capacity for explanation in relation to food delivery apps, this study adds to the body of knowledge. The primary focus of this study is on determining the direct impact of the identified determinants on the adoption of food delivery applications within the context of a pandemic situation in developing countries.

The present study aims to inform the requirements for developing a sustainable rating tool for small-scale infrastructure projects (SSIPs) through research findings.

A review-based comparative study of existing infrastructure sustainability (IS) rating tools for assessment of SSIPs is presented. Key stakeholder participants of the existing IS rating tools, are interviewed to identify existing barriers and requirements for sustainability rating. The study further presents possible rating tool options to optimise the sustainable performance evaluation of SSIPs.

Findings of this study indicated that prevalent IS rating tools are majorly applied to large-scale infrastructure projects and sustainability of SSIPs are seldom assessed. Based on a literature review and series of interviews, it was found that user friendliness, efficient structure, training and technical support, cost effectiveness and stakeholder recognition are the five key requirements of a sustainability rating tool for SSIPs. Additionally, six sustainability assessment options were proposed for SSIPs which range from pathways for existing tools through to new, customisable tools. Upon comparison, a new modified tool with verification process and revised tool with defined grouping of sustainable criteria was more effective for evaluation of SSIPs.

Use of case specific information for validation and framework development may lack generalisation. However, methodology can be used for future decision-making by making necessary adjustments to suit different local regional requirements.

Despite lack of generalisation, the findings can lead to future general studies on sustainability of SSIPs. Findings of the study provide foundation knowledge and awareness for sustainability evaluation of SSIPs.

This article explores whether six broad categories of activities undertaken by Canadian business scholars’ academics: publications record, citations record, teaching load, administrative load, consulting activities, and knowledge spillovers transfer, are complementary, substitute, or independent, as well as the conditions under which complementarities, substitution and independence among these activities are likely to occur.

A multivariate probit model is estimated to take into account that business scholars have to consider simultaneously whether or not to undertake many different academic activities. Metrics from Google Scholar of scholars from 35 Canadian business schools, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to explain the heterogeneities between the determinants of these activities.

Overall, the results reveal that there are complementarities between publications and citations, publications and knowledge spillovers transfer, citations and consulting, and between consulting and knowledge spillovers transfer. The results also suggest that there are substitution effects between publications and teaching, publications and administrative load, citations and teaching load, and teaching load and administrative load. Moreover, results show that public and private funding, business schools’ reputation, scholar’s relational resources, and business school size are among the most influential variables on the scholar’s portfolio of activities.

This study considers simultaneously the scholar’s whole portfolio of activities. Moreover, the determinants considered in this study to explain scholars’ engagement in different activities reconcile two conflicting perspectives: (1) the traditional self-managed approach of academics, and (2) the outcomes-focused approach of university management.

Scholars have affirmed that a conceptualization of value co-creation in business relationships should reflect the nature and characteristics of interactional processes that occur in use. The advent of sales and marketing technologies, however, is changing the nature and dynamics of interactions. New trends in digitalization have played a significant role in emphasizing and facilitating the occurrence of business-to- business (B2B) collaborative or sharing economy. The B2B sharing economy and value co-creation are closely intertwined, as businesses harness the power of shared resources and collaboration to generate value in diverse ways. This study highlights the importance of going beyond value co-creation in studying B2B collaborative economy, unpacking the interconnected value processes that influence value co-creation. It also aims at showing the activities that characterize multiple joint value spheres among actors.

The study consists of 49 qualitative interviews with managers operating in different industries.

The paper shows that when considering digital B2B contexts, five joint value spheres in business relationships should be considered: a value co-creation, a value appropriation, a value communication, a value measurement and a value representation sphere. Each one is characterized by specific activities that are relevant from a managerial point of view.

This study highlights that value co-creation has often been over stressed when discussing business interactions, also with the advent of new technologies. Rather, this study offers a more comprehensive view of value co-creation that includes different value processes occurring in joint value spheres. These further processes are relevant because failure and success in business relationships within the B2B sharing economy are often dependent from activities outside the value co-creation process, which strongly affect it. Such knowledge will also open up new research venues and opportunities to better contribute to the practice of value management in business relationships.