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1 – 10 of 17Janianton Damanik, Tri Kuntoro Priyambodo, Moh Edi Wibowo, Putu Diah Sastri Pitanatri and Suci Sandi Wachyuni
This study aims to explore the differences in the travel behaviour of Indonesian youth of Generations Y and Z in the pre-, during and post-travel stages and their associated use…
Abstract
Purpose
This study aims to explore the differences in the travel behaviour of Indonesian youth of Generations Y and Z in the pre-, during and post-travel stages and their associated use of information and communication technology.
Design/methodology/approach
Data were gathered through a questionnaire that was distributed via the internet for six weeks; 569 people provided their full responses. Chi-square tests and linear regression were used for data analysis.
Findings
These generations use digital media and word of mouth differently when searching for travel information. The differences are also apparent in the pre-, during and post-travel stages. Generation Z tends to use digital media and share travel experiences through a certain social media platform more frequently than Generation Y.
Research limitations/implications
This study covers the travel history prior to and during the COVID-19 pandemic and equalises the situation in these two periods. The number of samples was relatively small to capture the current population of both generations.
Practical implications
This study promotes a new understanding of the travel behaviours of the two generations based on the stages of the travel examined. The findings suggest that the travel industry can distinguish between promotional media and types of services to serve each of the generational cohorts more effectively.
Originality/value
To the best of the authors’ knowledge, this is the first study to reveal differences in travel behaviour between Generations Y and Z in Indonesia.
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Francisco Jesús Arjonilla García and Yuichi Kobayashi
This study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the…
Abstract
Purpose
This study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the Jacobians in the vicinity of the starting point and deducing a virtual input to effectively navigate the system along the non-holonomic constraint. Second, the authors explore the sensorimotor space in a predetermined pattern and obtain an approximate mapping from sensor space to chained form that facilitates controllability.
Design/methodology/approach
In this paper, the authors tackle the controller acquisition problem of unknown sensorimotor model in non-holonomic driftless systems. This feature is interesting to simplify and speed up the process of setting up industrial mobile robots with feedback controllers.
Findings
The authors validate the approach for the test case of the unicycle by controlling the system with time-state control policy. The authors present simulated and experimental results that show the effectiveness of the proposed method, and a comparison with the proximal policy optimization algorithm.
Originality/value
This research indicates clearly that feedback control of non-holonomic systems with uncertain kinematics and unknown sensor configuration is possible.
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Rana I. Mahmood, Harraa S. Mohammed-Salih, Ata’a Ghazi, Hikmat J. Abdulbaqi and Jameel R. Al-Obaidi
In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their…
Abstract
Purpose
In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.
Design/methodology/approach
Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.
Findings
The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.
Originality/value
This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.
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Muskan Sachdeva and Ritu Lehal
Stock markets are considered as the largest and most important units for the development and growth of the economy. The present study attempts to provide a comprehensive view of…
Abstract
Purpose
Stock markets are considered as the largest and most important units for the development and growth of the economy. The present study attempts to provide a comprehensive view of factors influencing investment decision making process of stock market investors. A multi group analysis of gender is also carried out on the proposed model.
Design/methodology/approach
The data of 402 valid responses are collected through structured questionnaires from individual investors of North India. SPSS 23 is used to do the descriptive analysis and AMOS 22 is used to establish the validity of the constructs and for hypotheses testing. For performing multi group analysis, several invariance tests have also been conducted to check the robustness of the model.
Findings
The results reveal that all the factors such as firm image, accounting information, neutral information, advocate recommendation and personal financial needs significantly influence investment decision making concluding image of the firm being the most influential factor and advocate recommendation being the least influential factor for investment decisions. No significant differences between males and females were found.
Research limitations/implications
The current study suffers from the limitation of restricted geographical area of North India. Moreover, there is also a scope to incorporate more demographic factors for predicting investment decisions.
Originality/value
This study incorporates a range of factors which covers all the aspects of investment decision making. This study also highlights the notion of signaling theory, thus contributing to the limited literature in Indian context.
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Norita Ahmad and Arief M. Zulkifli
This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is…
Abstract
Purpose
This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.
Design/methodology/approach
This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.
Findings
This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.
Research limitations/implications
While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.
Practical implications
Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.
Originality/value
Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.
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Kanza Abid, Zafar Iqbal Shams, Muhammad Suleman Tahir and Arif Zubair
The presence of heavy metals in milk causes many acute and chronic physiological dysfunctions in human organs. The present study aims to investigate the heavy metals in cow's and…
Abstract
Purpose
The presence of heavy metals in milk causes many acute and chronic physiological dysfunctions in human organs. The present study aims to investigate the heavy metals in cow's and buffalo's milk of two major cities, Karachi and Gujranwala, Pakistan to estimate metal intake by humans from this source.
Design/methodology/approach
In total, 48 milk samples from 2 cities were drawn from animals' udder to avoid contamination. Each sample was digested with nitric acid at 105 oC (degree Celsius) on a pre-heated electric hot plate to investigate the metals by atomic absorption spectroscopy (flame type). Air-acetylene technique analyzed chromium, cadmium and lead, and the hydride method analyzed arsenic in the milk samples.
Findings
The results revealed the highest mean lead concentration (19.65 ± 43.86 ppb) in the milk samples, followed by chromium (2.10 ± 2.33 ppb) and arsenic (0.48 ± 0.73 ppb). Cadmium was not detected in any sample, assuming cadmium's occurrence was below the detection level. The concentrations of all the metals in the samples of the two cities do not differ statistically. Lead concentrations in the buffalo's milk were higher than in cow's milk (p < 0.05). However, the concentrations of arsenic and chromium between buffalo's and cow's milk do not differ statistically. The present study reveals a lower level of metals in the milk than those conducted elsewhere. The mean concentrations of all the metals met the World Health Organization's (WHO) safety guidelines (1993).
Research limitations/implications
Although cadmium causes toxicity in the human body, cadmium could not be measured because cadmium's concentration was below the detection level, which is 1 ppb.
Practical implications
This study will help reduce the toxic metals in our environment, and the sources of heavy metals, particularly from the industrial sector could be identified. The feed and water consumed by the milking animals could be carefully used for feeding them.
Social implications
This study will help reduce the diseases and malfunction of human organs and organ systems since these heavy metals cause toxicity and carcinogenicity in humans. Arsenic and chromium cause cancer while lead causes encephalopathy (a brain disease).
Originality/value
The study reports heavy metal concentrations in the two attributes of four independent variables of raw milk samples that were scarcely reported from Pakistan.
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The purpose of this paper is to first develop indicators for how total inequality, measured through the ANalysis Of GIni (ANOGI) framework, is mapped onto each group – i.e…
Abstract
Purpose
The purpose of this paper is to first develop indicators for how total inequality, measured through the ANalysis Of GIni (ANOGI) framework, is mapped onto each group – i.e. indicators for each group's share of total inequality. Second, to develop indicators for the sensitivity of total inequality and its structure to changes in the composition of the population. Specifically, to develop indicators for how the Gini index and its ANOGI components react to (1) changes in the population-share of each group, (2) migration between groups, (3) changes in group incomes and (4) income transfers between groups.
Design/methodology/approach
First, the expressions for these indicators are derived analytically. Following this, the indicators are applied to labour-market data from Brazil, contrasting the results to others available in the literature.
Findings
The indicators described above are presented and their characteristics discussed. Empirically, it is illustrated how labour formalisation in Brazil was an inequality-reducing process between 2002 and 2011, contrary to previous incorrect measurements of the phenomenon based on income-source decompositions for Latin American countries.
Originality/value
Indicators for how total inequality reacts to changes in group sizes and income were unavailable for the ANOGI framework, which this article provides. The empirical illustration shows how this leads to a reassessment of important inequality dynamics, using the example of labour formalisation in Brazil. Contrary to the existing literature, it is shown how this was a progressive development, with key implications for social and labour-market policy. This framework can be used to assess the impact of diverse processes in the ANOGI methodology.
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Saeedeh Rezaee Vessal, Judith Partouche-Sebban and Francesco Schiavone
The COVID-19 outbreak has undoubtedly affected overall mental health. Thus, researching resilience is important, as it has been previously discussed as a means to protect people…
Abstract
Purpose
The COVID-19 outbreak has undoubtedly affected overall mental health. Thus, researching resilience is important, as it has been previously discussed as a means to protect people from mental health problems. This study aims to clarify whether survivors of a traumatic event (i.e. cancer survivors) are more resilient to living through another traumatic experience, such as COVID-19, compared to those who have never had such an experience. The study also examines the role of emotional creativity in this process.
Design/methodology/approach
A quantitative research design was adopted. The data collection was performed through a survey (N = 338), which was conducted among two separate groups of participants. The first group (N = 152) included the survivors of a traumatic event (i.e. cancer survivors), and the second group (N = 186) included those who did not have such an experience.
Findings
The results demonstrate that living through a traumatic experience results in a higher level of resilience during another traumatic experience (i.e. COVID-19), which is the result of higher post-traumatic growth. Moreover, emotional creativity is discussed as an explanatory variable that explains a significantly higher level of post-traumatic growth among survivors of a traumatic event.
Originality/value
This research offers a better understanding of the effect of living through a traumatic event on post-traumatic growth and resilience in living through another traumatic experience. Moreover, post-traumatic growth is explained through emotional creativity improvement, which happens after experiencing a traumatic life event.
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Norma Schönherr, Heike Vogel-Pöschl, Florian Findler and André Martinuzzi
While corporate social responsibility (CSR) standards are amongst the most widely adopted instruments for supporting firms in becoming more accountable, firms who adopt them…
Abstract
Purpose
While corporate social responsibility (CSR) standards are amongst the most widely adopted instruments for supporting firms in becoming more accountable, firms who adopt them frequently fail to comply. In this context, the purpose of this study is to explore to what extent CSR standards are designed for accountability. In the analysis, this paper investigates design characteristics related to accountability across different standard types, namely, principle-based, reporting, certification and process standards.
Design/methodology/approach
This study reviews the design characteristics of 50 CSR standards in a systematic and comparative fashion. This paper combines qualitative deductive coding with exploratory quantitative analyses methods to elucidate structural variance and patterns of accountability-related design characteristics across the sample.
Findings
This study finds that the prevalence of design characteristics aimed at fostering accountability varies significantly between different types of standards. This paper identifies three factors related to the specific purpose of any given standard that explain this structural variation in design characteristics, namely, implementability, comparability and measurability.
Practical implications
Non-compliance limits the effectiveness and legitimacy of CSR standards. The systematic exploration of patterns and structural variation in design characteristics that promote accountability may provide valuable clues for the design of more effective CSR standards in the future.
Social implications
Better understanding the role of design characteristics of CSR standards is critical to ensure they contribute to greater corporate accountability.
Originality/value
This study strives to expand the current understanding of the design characteristics of CSR standards beyond individual cases through a systematic exploration of accountability-related design characteristics across a larger sample.
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This study explores whether a new machine learning method can more accurately predict the movement of stock prices.
Abstract
Purpose
This study explores whether a new machine learning method can more accurately predict the movement of stock prices.
Design/methodology/approach
This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.
Findings
The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.
Originality/value
This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.
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