Search results
1 – 10 of 147Anamika Saharan, Akash Saharan, Krishan Kumar Pandey and T. Joji Rao
The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst…
Abstract
Purpose
The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.
Design/methodology/approach
A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.
Findings
Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.
Originality/value
The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746
Details
Keywords
Anu Mohta and V Shunmugasundaram
This study aims to examine the association between risk tolerance and risky investment intention with financial literacy as a moderating variable. The proposed relationship was…
Abstract
Purpose
This study aims to examine the association between risk tolerance and risky investment intention with financial literacy as a moderating variable. The proposed relationship was explored specifically for millennials.
Design/methodology/approach
The questionnaire was divided into three segments to assess millennials' financial literacy, risk tolerance and risky investment intention. This study uses survey data from 402 millennial investors residing in Delhi-NCR region. The authors exploited PLS-SEM for the analysis because the model involved higher-order constructs.
Findings
The findings revealed that financial literacy has a negative impact on risky investment intention. Further, risk tolerance had a positive and significant influence on risky investment intention; however, when financial literacy was added as a moderating variable in this relationship, it had a negative impact on risky investment intention.
Originality/value
Every generation has its quirks, and millennials are no exception. Given their age and sheer number, leading to their dominance in the global workforce, millennials will bring about a generational shift. Awareness of Gen Y's financial literacy and risk behavior enhances their ability to make informed financial decisions, thus proving beneficial not only to them, but also to the whole economy. This will also help policymakers and institutions to introduce financial literacy programs and financial products in alignment with their needs and preferences.
Details
Keywords
This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy…
Abstract
Purpose
This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy, single-family homes that meet affordable housing criteria in diverse locations.
Design/methodology/approach
The framework is developed and applied in a case example of a TEA of four designs for achieving net zero-water and energy in an affordable home in Saint Lucie County, Florida.
Findings
Homes built and sold at current market prices, using combinations of well versus rainwater harvesting (RWH) systems and grid-tied versus hybrid solar photovoltaic (PV) systems, can meet affordable housing criteria for moderate-income families, when 30-year fixed-rate mortgages are at 2%–3%. As rates rise to 6%, unless battery costs drop by 40% and 60%, respectively, homes using hybrid solar PV systems combined with well versus RWH systems cease to meet affordable housing criteria. For studied water and electricity usage and 6% interest rates, only well and grid-tied solar PV systems provide water and electricity at costs below current public supply prices.
Originality/value
This article provides a highly adaptable framework for conducting TEAs in diverse locations for designs of individual net-zero water and energy affordable homes and whole subdivisions of such homes. The framework includes a new technique for sizing storage tanks for residential RWH systems and provides a foundation for future research at the intersection of affordable housing development and residential net-zero water and energy systems design.
Details
Keywords
Ramin Rostamkhani and Thurasamy Ramayah
This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management…
Abstract
This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.
Details
Keywords
Nehemia Sugianto, Dian Tjondronegoro and Golam Sorwar
This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video…
Abstract
Purpose
This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.
Design/methodology/approach
This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.
Findings
The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.
Originality/value
This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.
Details
Keywords
Rajdeep Kumar Raut and Santosh Kumar
This paper aims to propose a decision-making framework by investigating the impact of perceived risk and computer self-efficacy on the intention to use online stock trading…
Abstract
Purpose
This paper aims to propose a decision-making framework by investigating the impact of perceived risk and computer self-efficacy on the intention to use online stock trading. Furthermore, it demonstrates the mediation effect of attitude and perceived risk as well as the moderating effect of financial literacy.
Design/methodology/approach
An integration of two popular models, technology acceptance model (TAM) and theory of planned behaviour (TPB), is used to provide a sound theoretical base and enhance the understanding of investors’ behaviour towards online trading platforms. The proposed hypothesised model was examined using structural equation modelling.
Findings
The results obtained from this study indicate that all variables, except subjective norms, had a significant impact on investors’ intention to trade online. Perceived risk was found to be a partial mediator between computer self-efficacy and the intention of investors. Finally, financial literacy was also found as a significant moderator for online trading intention of investors.
Practical implications
This study shows the significance of using the TAM and TPB together to provide a comprehensive understanding of the factors that influence an investor’s behaviour in adopting and using technology for online trading. The hybrid approach of TAM and TPB could be considered for a more nuanced and complete understanding of technology adoption and usage in risky affairs like investment decisions. Again, the significant moderating role of financial literacy provides a lance to look into the scope for improvements in investment decision-makings.
Originality/value
The paper develops an assessment framework for analysing the variables based on the hybrid approach for online trading intention in the context of a developing country.
Details
Keywords
Amar Benkhaled, Amina Benkhedda, Braham Benaouda Zouaoui and Soheyb Ribouh
Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However…
Abstract
Purpose
Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.
Design/methodology/approach
In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.
Findings
The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.
Research limitations/implications
The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.
Originality/value
The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.
Details
Keywords
Atefeh Hemmati, Mani Zarei and Amir Masoud Rahmani
Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of…
Abstract
Purpose
Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.
Design/methodology/approach
This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.
Findings
This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.
Research limitations/implications
This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.
Originality/value
This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.
Details
Keywords
Manpreet Kaur, Amit Kumar and Anil Kumar Mittal
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered…
Abstract
Purpose
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.
Design/methodology/approach
To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.
Findings
The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.
Originality/value
To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.
Details
Keywords
Vishal Kumar Laheri, Weng Marc Lim, Purushottam Kumar Arya and Sanjeev Kumar
The purpose of this paper is to examine the purchase behavior of consumers towards green products by adapting and extending the theory of planned behavior with the inclusion of…
Abstract
Purpose
The purpose of this paper is to examine the purchase behavior of consumers towards green products by adapting and extending the theory of planned behavior with the inclusion of three pertinent environmental factors posited to reflect environmental consciousness in the form of environmental concern, environmental knowledge and environmental values.
Design/methodology/approach
The data was collected from 410 consumers at shopping malls with retail stores selling green and non-green products in a developing country using cluster sampling and analyzed using covariance-based structural equation modeling.
Findings
The findings of this study indicate that environmental factors reflecting environmental consciousness positively influence consumers’ attitude towards purchasing green products, wherein consumers’ environmental values have a stronger influence than their environmental concern and environmental knowledge. The findings also reveal that subjective norm, attitude and perceived behavioral control toward purchasing green products positively shape green purchase intention. The same positive effect is also witnessed between green purchase intention and behavior. However, perceived behavioral control towards purchasing green products had no significant influence on green purchase behavior.
Practical implications
This study suggests that green marketers should promote environmental consciousness among consumers to influence and shape their planned behavior towards green purchases. This could be done by prioritizing efforts and investments in inculcating environmental values, followed by enhancing environmental knowledge and finally inducing environmental concern among consumers. Green marketers can also leverage subjective norm and perceptions of behavioral control toward purchasing green products to reinforce green purchase intention, which, in turn, strengthens green purchase behavior. This green marketing strategy should also be useful to address the intention–behavior gap as seen through the null effect of perceived behavioral control on purchase behavior toward green products when this strategy is present.
Originality/value
This study contributes to theoretical generalizability by reaffirming the continued relevance of the theory of planned behavior in settings concerning the environment (e.g. green purchases), and theoretical extension by augmenting environmental concern, environmental knowledge and environmental values with the theory of planned behavior, resulting in an environmentally conscious theory of planned behavior. The latter is significant and noteworthy, as this study broadens the conceptualization and operationalization of environmental consciousness from a unidimensional to a multidimensional construct.
Details