This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to…
Abstract
Purpose
This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to Cap-Weighted (CW) indices. As investors’ strategies differ with market movements, this study analyses how single-factor and multi-factor SB indices perform during different market phases, in relation to CW indices. It also attempts to determine which SB factors are more suitable for investors in these phases.
Design/methodology/approach
Using various return and risk indicators, this study analyses how SB indices perform vis-à-vis CW indices during bullish, bearish and stagnant phases. The authors also evaluate the upside and downside participation advantage of SB indices and assess their ability to capture upside returns and limit downside risk. The authors attempt to determine the cyclical or defensive nature of SB indices using Average Participation values.
Findings
This study found that SB indices outperform CW indices during the bearish and stagnant phases. Multi-factor SB indices have lower risk levels in all market phases, providing downside protection to risk-averse investors. Dividend, Low Volatility, Quality and multi-factor SB indices are defensive portfolios offering better payoffs during the down market phases, while Alpha, Beta, Equal Weight and Value SB indices provide higher payoffs during the up-market phases.
Originality/value
To the best of the authors’ knowledge, this is the first study that examines the performance of single-factor and multi-factor Indian SB indices in different market phases. It determines the suitability of various factors to passive investors during these phases and also identifies whether SB indices are cyclical or defensive.
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A pandemic causes abrupt and unanticipated disruptions in many facets of society. A lot of authorities have quickly turned to online teaching methods. The best methods for online…
Abstract
A pandemic causes abrupt and unanticipated disruptions in many facets of society. A lot of authorities have quickly turned to online teaching methods. The best methods for online teaching have become a hot topic of discussion due to this urgent fast transmission. It was difficult to teach physiology to medical and paramedical students online because of concerns about how to give the students an effective interactive online teaching practice and how to guarantee successful outcomes. Therefore, three approaches have been individually applied to medical and nursing students for the first time in the physiology department of the Faculty of Medicine in Rabigh, King Abdulaziz University. Through online lectures and assignments, the strategies attempted to capture the students’ interest and interaction. The second-year nursing students were given a mind map project to complete after the lecture. The third-year medical students used a crossword puzzle game to test the students’ understanding. The third-year medical students were presented with short stories to better comprehend the physiological processes covered in the lectures. Overall, the three instructional strategies received positive feedback from the students. Incorporating such cutting-edge and imaginative educational approaches, in conclusion, could significantly aid in managing the pressures that arise during pandemics.
Details
Keywords
- Online physiology education
- cross-puzzles
- mind maps
- stories in teaching physiology
- COVID-19 pandemic
- creativity and flexibility
- AAAS : The American Association for the Advancement of Science
- BUCS : Blackboard Ultra Collaborate System
- MS PPT : Microsoft PowerPoint
- NLEs : Narrative-centered learning environments
- KAU : King Abdulaziz University
Munawar Abbas, Farhan Saeed, Muhammad Umair Arshad, Muhammad Tahir Nadeem, Huma Bader-Ul-Ain, Zohaib Hassan and Hafiz Ansar Rasul Suleria
This paper aims to evaluate the therapeutic potential of apple extracts against thrombocytopenia, i.e. decrease in platelet count.
Abstract
Purpose
This paper aims to evaluate the therapeutic potential of apple extracts against thrombocytopenia, i.e. decrease in platelet count.
Design/methodology/approach
Male Sprague Dawley rats were used to evaluate therapeutic potential of apple extracts. Diets enriched with apple fruit and seed’s ethanolic extract were provided to normal and KBrO3-induced thrombocytopenic rats for 60 days. KBrO3 was administered at level of 85 mg/Kg of body weight of rats to intentionally decrease the platelet count. Platelet count and other hematological parameters were monitored at monthly intervals to evaluate therapeutic effect of apple extracts against thrombocytopenia.
Findings
The results of current research portrayed that both apple seed and fruit extracts enriched diets increased the platelet count significantly (p < 0.05) in thrombocytopenic rats. It was observed that apple fruit extract-enriched diet (AFE) raised the platelet count to 14.72 and 33.07 per cent in normal and thrombocytopenic rats, respectively. Apple seed extract-enriched diet (ASE) raised the platelet count to 11.08 and 32.90 per cent in normal and thrombocytopenic rats, respectively. Other hematological parameters including white blood cells, red blood cells and hemoglobin were also significantly increased.
Originality/value
Thrombocytopenia is uprising problem in developing world including India and Pakistan accompanied by different diseases especially dengue and related complications. Because of questioning on therapies against thrombocytopenia, research on diet-based therapies, as a substitute to synthetic one, is increasing. Diet rich in antioxidant compounds including apple fruit and seeds are the limelight of manuscript.
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Guiwen Liu, Juma Hamisi Nzige and Kaijian Li
The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose…
Abstract
Purpose
The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose the future outline.
Design/methodology/approach
The paper adopted literature reviews methodology involving 1,057 relevant documents published in 2008-2017 from 15 journals. The selected documents were empirically analyzed through a topic-modeling technique. A latent Dirichlet allocation model was applied to each document to infer 50 key topics. A machine learning for language toolkit was used to get topic posterior word distribution and word composition.
Findings
This is an exploratory study, which identifies the distribution of topics and themes; the trend of topics and themes; journal distribution trends; and comparative topic, themes and journal distribution trend. The distribution and trends show an increase in researcher’s interest and the journal’s priority on OSC research. Nevertheless, OSC existing literature is faced with; under-researched topics such as building information modeling, smart construction and marketing. The under-researched themes include organizational management, supply chain and context. The authors also found an overload of similar information in prefabrication and concrete topics. Furthermore, the innovative methods and constraints themes were found to be overloaded with similar information.
Research limitations/implications
The naming of the themes was based on our own interpretation; hence, the research results may lack generalizability. Therefore, a comparative study using different data processing is proposed. The study also provides future research outline as follows: studying OSC topics from dynamic evolution perspective and identifying the new emerging topics; searching for effective strategies to enhance OSC research; identifying the contribution of countries, affiliation and funding agency; and studying the impact of these themes to the adoption of OSC.
Practical implications
This study is of values to the scholars, as it could stimulate research to under-researched areas.
Originality/value
This paper justifies a need to have a broad understanding of the nature and structure of existing OSC literature.
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Since there are now 4600 students reading for CNAA degrees, the Council has decided to establish its own two‐tiered higher degree system. The degrees of MA and MSc will be awarded…
Abstract
Since there are now 4600 students reading for CNAA degrees, the Council has decided to establish its own two‐tiered higher degree system. The degrees of MA and MSc will be awarded for successful completion of 1–3 year specialized courses, which may be full‐time, part‐time, sandwich, or block release. It is hoped that such courses will be planned with the help of industry and commerce to meet their needs, producing postgraduates qualified for senior industrial posts or for further research. The MPhil and PhD degrees will be awarded for pure or applied original research in industry or commerce. Most students, holding a good first degree, will first register for the MPhil course taking two to three years. However, transfer to the three to four year PhD registration may be granted after a year to 18 months. At the end of the course the student will present a thesis and take an oral examination.
– The purpose of the study is to examine the way different motivational types from Self-Determination Theory (SDT) influence antecedents of customer satisfaction.
Abstract
Purpose
The purpose of the study is to examine the way different motivational types from Self-Determination Theory (SDT) influence antecedents of customer satisfaction.
Design/methodology/approach
The findings in this study were generated with a quantitative design using path analysis on data collected at two stages during an extended service encounter.
Findings
Each motivation type played a unique and important role in influencing the antecedents of satisfaction, namely, positive and negative emotions and perceptions of service quality. As hypothesised, motives associated with higher levels of autonomy were consistently stronger predictors of positive emotions and service quality. The influence of motives on the antecedents did not change significantly over time, whereas significant differences were noted between all antecedents and satisfaction. The model explained 54 and 63 per cent of the variance in satisfaction in times one and two, respectively.
Originality/value
This is the first time that motivation as conceptualised from an SDT perspective has been applied to understanding the dynamic nature of customer satisfaction. The findings offer considerable opportunities for follow-up studies and the motivation types can provide practitioners with a stable and efficient segmentation option.
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Mauro Sciarelli, Mario Tani, Anna Prisco and Francesco Caputo
The paper aims at investigating antecedents and predictors of consumers' buying and consumption processes within the Italian Solidarity Purchasing Groups (SPGs) to enrich current…
Abstract
Purpose
The paper aims at investigating antecedents and predictors of consumers' buying and consumption processes within the Italian Solidarity Purchasing Groups (SPGs) to enrich current debate about drivers and levers on which act for fostering ethical consumption in food sector.
Design/methodology/approach
Building upon the theory of planned behavior (TPB) a theoretical model is proposed for depicting possible antecedents and predictors of consumers' buying and consumption processes in food sector. The validity of the model has been tested via partial least squares structural equation modeling (PLS-SEM) using SmartPLS for analyzing primary data collected through a structured questionnaire from 354 consumers engaged in SPGs.
Findings
Within the domain of food buying and consumptions through SPGS, results show that consumers' behavior intention (BI) is influenced by consumers' attitude (ATT) and perceived behavioral control (PBC). Moreover, the research also demonstrates that consumers' ATT is influenced by consumers' ethical self-identity (ETH), consumers' willingness to support local economy (SLE), and food safety concern (FSC).
Originality/value
The study contributes to the ongoing debate on sustainable consumerism focusing the attention on SPGs as emerging social organizations inspired by ethical food consumption. Both theoretical development and empirical evidences enrich current knowledge about drivers and levers on which act for fostering ethical consumption in food sector.
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Ajay Kumar and Anil Kumar Kashyap
The purpose of this study is to identify distinct segments of apparel shoppers based on their fashion shopping orientation. The difference among the segments based on mall…
Abstract
Purpose
The purpose of this study is to identify distinct segments of apparel shoppers based on their fashion shopping orientation. The difference among the segments based on mall attractive dimension is also examined.
Design/methodology/approach
The data were collected through mall intercept survey from the mall shoppers. Samples of 375 respondents are used for data analysis purpose. Exploratory factor analysis is used to extract the factors of fashion shopping orientation and mall attractive dimensions while K-means cluster analysis is applied to identify the segments.
Findings
This study resulted in three factors of fashion orientation of apparel shoppers, i.e. fashion involvement, variety seeking and economic value, and four factors of mall attractive dimensions: convenience, entertainment, atmosphere and architecture design. Based on these factors, this study came out with three distinct segments of fashion shoppers: pragmatic shoppers, variety seeking shoppers and highly fashioned shoppers. These three segments are attracted towards the mall dimension differently.
Originality/value
This paper presents the three distinct profiles of fashion shoppers based on their fashion shopping orientation and mall attractive dimensions. The findings of this study may help retailers and mall developers to target mall visitors appropriately.
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Fuad Ali Mohammed Al-Yarimi, Nabil Mohammed Ali Munassar and Fahd N. Al-Wesabi
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are…
Abstract
Purpose
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.
Design/methodology/approach
Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.
Findings
From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.
Originality/value
The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.