Search results

1 – 10 of 101
To view the access options for this content please click here
Article
Publication date: 14 July 2021

Manoj Arora, Harpreet Singh and Sanjay Gupta

In the era of digitalization and technology, tremendous changes have taken place in the taxi industry worldwide. The traditional taxi service has transformed into the…

Abstract

Purpose

In the era of digitalization and technology, tremendous changes have taken place in the taxi industry worldwide. The traditional taxi service has transformed into the latest innovative technology-based e-hailing service. There are innumerable factors that drive the user adoption of e-hailing apps. This study aims to primarily concentrate on identifying, analyzing and ranking these factors which have an impact on the user intention toward using e-hailing apps.

Design/methodology/approach

The e-hailing app users in the state of Punjab and Chandigarh are the target population for the study. A fuzzy analytical hierarchy process technique has been applied to analyze and codify the determinants that influence the user intention of adopting e-hailing apps. The primary factors that have been considered for the study are social influence, perceived usefulness, facilitating conditions, perceived ease of use, self-efficacy, perceived risk, compatibility and trust.

Findings

The study revealed that “Perceived Usefulness” is the factor that influences user intention to use e-hailing apps the most, while “Perceived Risk” the least. The sub-criteria codified in the top priority was as follows: “Overall, I find the e-hailing app useful in booking a taxi (C15)”; “I do not need some people to use e-hailing apps (C52); “I believe e-hailing app is compatible with existing technology (C61).” The sub-criterion “E-hailing app service provider keeps its promise (C72)” was demonstrated to have the least impact on the user intention of adopting e-hailing apps.

Research limitations/implications

The study has been confined to only eight factors selected from the extended technological acceptance model framework and some related technology acceptance theories. Some more other factors may have an impact on user adoption of e-hailing apps, which need to be added further. Also, the scope of the study should be enhanced by expanding the geographical area beyond the selected region.

Practical implications

The findings of the study enable the e-hailing service providers and marketers to understand the users’ intention in a better way, to make improvements in e-hailing apps and formulate strategies accordingly.

Originality/value

The previous literature provides the base to the present study for identifying the factors affecting user behavioral intention toward e-hailing apps and information technology. The findings and results of the present research make value addition to the existing knowledge base.

Details

Journal of Science and Technology Policy Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2053-4620

Keywords

To view the access options for this content please click here
Article
Publication date: 11 December 2017

Xiaoguang Wang, Ningyuan Song, Lu Zhang and Yanyu Jiang

The purpose of this paper is to understand the subjects contained in the Dunhuang mural images as well as their relation structures.

Abstract

Purpose

The purpose of this paper is to understand the subjects contained in the Dunhuang mural images as well as their relation structures.

Design/methodology/approach

This paper performed content analysis based on Panofsky’s theory and 237 research papers related to the Dunhuang mural images. UNICET software was also used to study the correlation structures of subject network.

Findings

The results show that the three levels of subject have all captured the attention of Dunhuang mural researchers, the iconology occupy the critical position in the whole image study, and the correlation between iconography and iconology was strong. Further analysis reveals that cultural development, production, and power and domination have high centralities in the subject network.

Research limitations/implications

The research samples come from three major Chinese journal databases. However, there are still many authoritative monographs and foreign publications about the Dunhuang murals which are not included in this study.

Originality/value

The results uncover the subject hierarchies and structures contained in the Dunhuang murals from the angle of image scholarship which express scholars’ intention and contribute to the deep semantic annotation on digital Dunhuang mural images.

Details

Journal of Documentation, vol. 74 no. 2
Type: Research Article
ISSN: 0022-0418

Keywords

To view the access options for this content please click here
Article
Publication date: 5 November 2019

Jinesh Jain, Nidhi Walia and Sanjay Gupta

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually…

Downloads
1392

Abstract

Purpose

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions.

Design/methodology/approach

The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias.

Findings

The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).”

Research limitations/implications

Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study.

Practical implications

The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions.

Originality/value

The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.

Details

Review of Behavioral Finance, vol. 12 no. 3
Type: Research Article
ISSN: 1940-5979

Keywords

To view the access options for this content please click here
Book part
Publication date: 19 November 2014

Miguel Belmonte and Gary Koop

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying…

Abstract

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact method for implementing DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an inflation forecasting application. We find strong evidence of model switching. We also compare different ways of implementing DMA/DMS and find forgetting factor approaches and approaches based on the switching Gaussian state space model to lead to similar results.

To view the access options for this content please click here
Book part
Publication date: 19 November 2014

Angela Vossmeyer

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or…

Abstract

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.

To view the access options for this content please click here
Book part
Publication date: 21 October 2019

Miriam Sosa, Edgar Ortiz and Alejandra Cabello

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use…

Abstract

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.

Details

Disruptive Innovation in Business and Finance in the Digital World
Type: Book
ISBN: 978-1-78973-381-5

Keywords

To view the access options for this content please click here
Book part
Publication date: 23 June 2016

Daniel J. Henderson and Christopher F. Parmeter

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there…

Abstract

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

Details

Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

To view the access options for this content please click here
Book part
Publication date: 18 March 2014

Kevin W. Caves and Hal J. Singer

In antitrust class-action litigation, courts are increasingly unlikely to accept the presumption that all class members were harmed by price-fixing among a group of firms…

Abstract

In antitrust class-action litigation, courts are increasingly unlikely to accept the presumption that all class members were harmed by price-fixing among a group of firms or by exclusionary behavior by a single firm. Econometric methods typically applied in antitrust and other settings estimate the average effect of the challenged conduct, but do not inform impact for individual class members. We present classwide econometric methods and statistical tests for detecting the existence (or lack thereof) of common impact and determining what proportion (if any) of the proposed class suffered injury in many class actions. We conclude that econometric tools can meaningfully inform the legal process, even when courts demand proof of common impact.

Details

The Law and Economics of Class Actions
Type: Book
ISBN: 978-1-78350-951-5

Keywords

To view the access options for this content please click here
Book part
Publication date: 23 June 2016

Yu Yvette Zhang, Ximing Wu and Qi Li

We propose a nonparametric estimator of the Lorenz curve that satisfies its theoretical properties, including monotonicity and convexity. We adopt a transformation…

Abstract

We propose a nonparametric estimator of the Lorenz curve that satisfies its theoretical properties, including monotonicity and convexity. We adopt a transformation approach that transforms a constrained estimation problem into an unconstrained one, which is estimated nonparametrically. We utilize the splines to facilitate the numerical implementation of our estimator and to provide a parametric representation of the constructed Lorenz curve. We conduct Monte Carlo simulations to demonstrate the superior performance of the proposed estimator. We apply our method to estimate the Lorenz curve of the U.S. household income distribution and calculate the Gini index based on the estimated Lorenz curve.

Details

Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

To view the access options for this content please click here
Book part
Publication date: 19 March 2018

Jordan French

The most popular method for calculating asset prices is the Capital Asset Pricing Model (CAPM). What is the appropriate amount of years to use in the estimation and which…

Abstract

The most popular method for calculating asset prices is the Capital Asset Pricing Model (CAPM). What is the appropriate amount of years to use in the estimation and which variation of the capital asset pricing beta provides the best results? This research looks at the out-of-sample forecasting capabilities of three popular CAPM ex-post constant beta models from 2005 to 2014. A total of 11 portfolios, five from developed and six from developing markets, are used to test the amount of input years that will reduce the mispricing in both types of markets. It is found that the best beta model to use varies between developed and developing markets. Additionally, in developing markets, a shortened span of historical years improves the pricing, contrary to popular studies that use 5 to 10 years of historical data. There are many different CAPM studies implementing various betas, using different data input lengths and run in various countries. This study empirically tests the best practices for those interested in successfully using the CAPM for their basic needs, finding that overall the simple ex-post constant beta is mispriced by 0.2 (developing) to 0.3 percent (developed). It is better to use short three-year estimation windows with the market beta in developing economies and longer nine-year estimation windows with the adjusted beta in developed economies.

Details

Global Tensions in Financial Markets
Type: Book
ISBN: 978-1-78714-839-0

Keywords

1 – 10 of 101