Search results

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The purpose of this paper is to provide evidence-based policy recommendations for improving the implementation of universal service funds (USF) with a view to closing the digital divide in Africa.

The paper adopts a qualitative approach that draws examples from various African countries supported by 25 interviews from key stakeholders with hands-on experience and roles that shape telecommunications policy in Africa and other developing countries.

The study's findings point out that institutional voids which characterize several African countries inhibit the effectiveness of USF in African countries. The authors identify several institutional and organisational factors and explain how they negatively affect the performance of USF. The authors find that in order to overcome these obstacles, there is a need for a clear redefinition of Universal Access and Service (UAS) policies, restructuring the governance of USF, encouraging cross-sectoral collaborations, and bottom-up initiatives to bridge the digital divide in African countries.

The paper contributes to the underexplored USF literature by shedding light on the role of institutional factors in determining the success of USF. The paper thus complements and provides a different perspective on promoting digital inclusion in Africa from the viewpoint of institutional voids, bringing new insights into the existing literature on how to deal with an intractable area of UAS policy and the wider digital divide debate in developing countries.

This study aims to illuminate the contribution of neurophysiological techniques in the field of marketing and consumer decision-making and to highlight avenues and research questions that marketing researchers can take advantage of from neuroscience and psychology to inform marketing phenomena.

The authors first reviewed the roots and definition of consumer neuroscience. Then, the authors outlined the main characteristics of the most commonly used neurophysiological tools (namely, skin conductance, facial electromyography, electrocardiogram, eye-tracking, electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy, magnetoencephalography and transcranial magnetic stimulation) with a special emphasis on their advantages and weaknesses. Finally, the authors propose the development of research lines that could be implemented by marketing researchers with an appropriate application and understanding of tools and theories of neuroscience and psychology.

The authors propose research questions to be addressed within four thematic areas: opportunities in product decisions (predicting product purchasing decisions, consumer responses to branding efforts and packaging), pricing, communication and retailing scenarios. The authors also incorporate insights into the complementarity of neurophysiological tools to traditional ones and situations in which these tools are useful for enhancing marketing theory. The authors finally shed light on the moral–ethical criticisms of this new branch of marketing.

To the best of the authors’ knowledge, this research constitutes the first study in identifying the research opportunities that marketing researchers could take advantage from neuroimaging and physiological tools to inform marketing theory and practice.

Esta investigación tiene como objetivo esclarecer la contribución de las técnicas neurofisiológicas en el campo del marketing y la toma de decisiones de los consumidores y destacar las vías y preguntas de investigación que los investigadores de marketing pueden aprovechar de la neurociencia y la psicología para informar sobre los fenómenos del marketing.

En primer lugar, revisamos el origen y la definición de la neurociencia del consumidor. A continuación, esbozamos las principales características de las herramientas neurofisiológicas más utilizadas (a saber, la conductancia, la electromiografía facial, el electrocardiograma, el seguimiento ocular, la electroencefalografía, la resonancia magnética funcional, la espectroscopia funcional en el infrarrojo cercano, la magnetoencefalografía y la estimulación magnética transcraneal), haciendo especial hincapié en sus ventajas y debilidades. Finalmente, se propone el desarrollo de líneas de investigación que podrían ser implementadas por los investigadores de marketing con una adecuada aplicación y comprensión de las herramientas y teorías de la neurociencia y la psicología.

Proponemos preguntas de investigación para ser abordadas dentro de cuatro áreas temáticas: oportunidades en las decisiones de producto (predicción de las decisiones de compra de productos, respuestas de los consumidores a los esfuerzos de marca y envasado), precios, comunicación y distribución. También incorporamos ideas sobre la complementariedad de las herramientas neurofisiológicas con las tradicionales y las situaciones en las que estas herramientas son útiles para mejorar la teoría del marketing. Por último, arrojamos luz sobre las críticas ético-morales a esta nueva rama del marketing.

本研究旨在阐明神经生理学技术在营销和消费者决策领域的贡献, 并强调营销研究人员可以从神经科学和心理学中利用的途径和研究问题, 以告知营销现象。

我们首先回顾了消费者神经科学的根基和定义。然后, 我们概述了最常用的神经生理学工具（即皮肤电导率、面部肌电图、心电图、眼球追踪、脑电图、功能性磁共振成像、功能性近红外光谱、脑磁图和经颅磁刺激）的主要特点, 特别强调了它们的优势和劣势。最后, 我们提出了研究路线的发展, 这些路线可以由营销研究人员通过适当的应用和理解神经科学和心理学的工具和理论来实施。

我们提出了四个主题领域的研究问题：产品决策中的机会（预测产品购买决策、消费者对品牌推广工作的反应和包装）、定价、沟通和零售场景。我们还纳入了对神经生理学工具与传统工具的互补性的见解, 以及这些工具对加强营销理论有用的情况。最后, 我们对这个新的营销分支的道德伦理批评进行了说明。

纸张类型 – 研究论文

The study aims to investigate the effect of the dialogue embedded synectics model of teaching on the creative thinking of students.

The research design of the study was a nonequivalent control group design of quasi experimental research. This study collected data from 80 students in the seventh grade from two different government schools in Bathinda, Punjab, India, which were selected using a random method. The subjects of experimental group were taught by the investigator using the dialogue embedded synectics model of teaching and the subjects of control group were taught by their teacher using the traditional teaching method, i.e. the Herbartian method. The data collections were done using the creative thinking test developed by Baqer Mehdi (1995). The data analysis techniques used t-test.

Key finding indicates that the dialogue embedded synectics model of teaching is effective in enhancing the creative thinking of students as compared to the traditional method.

The present work is unique in terms of development of an innovative pedagogy, i.e. the dialogue embedded synectics model of teaching, which has the potential to encourage students' creative thinking, a key concern for society in the 21st century. Therefore, it is suggested to conduct similar type of studies on this innovative pedagogy and this model of teaching may be used by teachers for enhancing creative thinking of seventh class students.

The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance.

A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators.

The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities.

The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes.

Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.

This paper aims to examine lean thinking in medical commodities supply chains by considering its applications and success factors. It determines the drivers and wastes of medical commodity supply chain, and the existing lean tools and practices together with their application in the supply chain processes. The paper also examined the challenges and success factors for effective lean application in the medical commodities supply chains.

The study used qualitative approaches, in-depth interviews and focus group discussions with key informants to form the basis for data collection. Through thematic analysis, the collected data were analyzed by developing themes reflecting the objectives of the study.

The main drivers for waste associated with the supply chain were demand management, supplier development, institutional framework and governance. The wastes were observed at the level of inventory, operation costs, transaction costs, delays in terms of service, commodity delivery time and quality. Digitalization, information technology and standardization were the tools for medical supply chain. Poor infrastructure, unreliable internet supply, environmental uncertainty and poor management support were challenges to realizing an effective supply chain.

Although the qualitative approach used in the study provides detailed information, a quantitative study covers a larger sample for generalization.

Capacity building and professionalism should be given a priority because the philosophy of lean focuses on waste removal and continuous improvement, which highly depends on the quality of human resource (Brito et al., 2020). Limited human resource capabilities in supply chain management will, therefore, result into poor operational efficiency, which are wasted. Moreover, systems interoperability is key waste minimization and, therefore, demands interventions.

The government under the Ministry of Health and other key sector ministries such as local and regional governments should better understand the role of the waste drivers and adopt system-wide reforms to support improvements to remove waste in the medical supply chain. For example, the current institutional framework creates an administrative block and hence leads to wastes. This bureaucratic procedure should be removed to minimize wastes along the chain.

This study is among the first studies to determine applicability and implementation of lean in a resource-constrained context. The paper identifies contextual factors for lean implementation. This paper focused on a holistic view of the entire supply chains to enhance a well-functioning supply chain in delivering health commodities.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

This study aims to understand independence in internal auditing by investigating how internal auditor independence is constructed when analysed in its corporate governance context.

A critical discourse analysis (CDA) of the corporate governance reports of Swedish large stock market listed non-financial companies, for three consecutive years, is undertaken, using a theoretical lens of organisational embeddedness and operational coupling to understand independence as a situated practice.

The study develops four archetypes of internal auditor independence – autarchic, instrumental, symbiotic and subservient – and discusses each archetype's implications for independence, related to tripartite relations with management and the audit committee, regarding who has the mandate to direct work and how the work is done. It finds that internal auditors always have a capacity to be independent. Although they are not independent in relation to agents in the subservient archetype, they are independent of those down the organisational chain of command, suggesting independence is both situational and relational.

The analysis contributes a novel approach to the literature and develops a conception of independence using the dimensions of embeddedness and coupling. The archetypes offer an analytical framework for future studies on independence.

Internal auditors may understand their practice differently through the archetypes that result from this study.

Internal auditors' power relations within corporate governance further an understanding of the pressures on internal auditors and their role.

This study contributes new knowledge on the situatedness of independence by showing how internal auditors are embedded and coupled helps build their independence.