Search results

1 – 5 of 5
To view the access options for this content please click here
Article

Tejas R. Shah and Tejal T. Shah

The purpose of the study is to explore and analyze m-car rental service quality dimensions.

Abstract

Purpose

The purpose of the study is to explore and analyze m-car rental service quality dimensions.

Design/methodology/approach

Exploratory factor analysis method is applied to explore the m-car rental service quality dimensions. Further, confirmatory factor analysis is done to prove the reliability and validity of the factors using AMOS 22.0.

Findings

The results reveal the m-car rental service quality dimensions: ambient quality, technical quality, comfort, safety and employee service, mobile convenience, mobile responsiveness, mobile efficiency and reliability and mobile safety and billing.

Research limitations/implications

The explored dimensions of car rental services are in Indian environment. So, these dimensions can be further validated in other similar cultural context.

Practical implications

The proposed measurements can also be applied to measure and compare the service quality performance of car rental firms.

Originality/value

Current literature does not confirm the stable factor structure of m-car rental service quality. This study confirms the reliable and valid dimensions of care rental service through mobile app.

Details

International Journal of Innovation Science, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1757-2223

Keywords

To view the access options for this content please click here
Article

Tejas R. Shah

This paper aims to identify the dimensions of service quality in the case of ride-sourcing services in Indian context.

Abstract

Purpose

This paper aims to identify the dimensions of service quality in the case of ride-sourcing services in Indian context.

Design/methodology/approach

The service quality dimensions of ride-sourcing services are identified using an exploratory factor analysis (EFA). Further, the reliability and validity of the factors are established through confirmatory factor analysis (CFA) using AMOS.

Findings

The service quality dimensions of ride-sourcing services are identified: comfort, internal environment, safety and personnel, mobile convenience and reliability, mobile system efficiency and availability, mobile customer service and billing and mobile security and privacy.

Research limitations/implications

The various dimensions are identified to measure service quality of ride-sourcing services in India. So, these dimensions can be tested for ride-sourcing services of countries having similar culture as India.

Practical implications

The proposed dimensions can be used as a diagnostic tool to identify and compare important criteria for service quality of ride-sourcing services.

Originality/value

Most relevant studies about dimensions of service quality for ride-sourcing services do not have stable factor structure. The dimensions identified include the traditional taxi service quality and mobile app service quality, which are not covered in current literature.

Details

Benchmarking: An International Journal, vol. 28 no. 1
Type: Research Article
ISSN: 1463-5771

Keywords

To view the access options for this content please click here
Article

Tejas R. Shah and Mahendra Sharma

The purpose of this paper is to develop a scale for measuring benefits of third party logistics service providers for co‐operative dairies in an Indian context. The…

Abstract

Purpose

The purpose of this paper is to develop a scale for measuring benefits of third party logistics service providers for co‐operative dairies in an Indian context. The objective is to measure benefits of third party logistics service providers for co‐operative dairies.

Design/methodology/approach

A standard scale development research procedure recommended by experts was followed. First, the literature review of studies to measure benefits of third party logistics was undertaken. Later, Delphi method was used. Interviews were conducted of experts and customers for understanding and generating items for measuring benefits of third party logistics service providers for co‐operative dairies. A survey was then undertaken first for development of the scale and later for validation purpose.

Findings

A reliable and valid scale is developed to measure the five dimensions of benefits of using 3PLSPs for co‐operative dairies: responsiveness, accuracy, customization of service, inventory handling and order processing and information sharing.

Research limitations/implications

This scale is developed to outsource logistics functions at operational levels in the context of co‐operative dairies in India. So, this scale can be tested for co‐operative dairies of countries other than India. The scale can also be tested where outsourcing of logistics activities is done at operational level, other than co‐operative dairies.

Practical implications

The proposed scale can be used as a diagnostic tool to identify important benefits to consider in outsourcing operational function of logistics management to 3PLSPs in co‐operative dairies.

Originality/value

Most relevant studies about benefits of third party logistics service providers do not have stable factor structure, especially for co‐operative dairies. The new scale fills the gap of the absence of a validated scale to measure benefits of 3PLSPs for co‐operative dairies at operational level.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 24 no. 3
Type: Research Article
ISSN: 1355-5855

Keywords

To view the access options for this content please click here
Article

Rajit Nair, Santosh Vishwakarma, Mukesh Soni, Tejas Patel and Shubham Joshi

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had…

Abstract

Purpose

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

Design/methodology/approach

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

Findings

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

Research limitations/implications

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Originality/value

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

To view the access options for this content please click here
Article

Maqsood Ahmad

The purpose of this article is to clarify the mechanism by which underconfidence heuristic-driven bias influences the short-term and long-term investment decisions of…

Abstract

Purpose

The purpose of this article is to clarify the mechanism by which underconfidence heuristic-driven bias influences the short-term and long-term investment decisions of individual investors, actively trading on the Pakistan Stock Exchange.

Design/methodology/approach

Investors' underconfidence has been measured using a questionnaire, comprising numerous items, including indicators of short-term and long-term investment decision. In order to establish the influence of underconfidence on the investment decisions in both the short and long run, a 5-point Likert scale questionnaire has been used to collect data from the sample of 203 investors. The collected data were analyzed using SPSS and AMOS graphics software. Hypotheses were tested using structural equation modeling technique.

Findings

This article provides further empirical insights into the relationship between heuristic-driven biases and investment decision-making in the short and long run. The results suggest that underconfidence bias has a markedly negative influence on the short-term and long-term decisions made by investors in developing markets. It means that heuristic-driven biases can impair the quality of both short-term and long-term investment decisions.

Practical implications

This article encourages investors to avoid relying on cognitive heuristics, namely, underconfidence or their feelings when making short-term and long-term investment strategies. It provides awareness and understanding of heuristic-driven biases in investment management, which could be very useful for finance practitioners' such as investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making its financial management strategies. They can improve the quality of their decision-making by recognizing their behavioral biases and errors of judgment, to which we are all prone, resulting in more appropriate investment strategies.

Originality/value

The current study is the first to focus on links between underconfidence bias and short-term and long-term investment decision-making. This article enhanced the understanding of the role that heuristic-driven bias plays in the investment management and more importantly, it went some way toward enhancing understanding of behavioral aspects and their influence on the investment decision-making in an emerging market. It also adds to the literature in the area of behavioral finance specifically the role of heuristics in investment strategies; this field is in its initial stage, even in developed countries, while, in developing countries, little work has been done.

Details

Management Decision, vol. 59 no. 3
Type: Research Article
ISSN: 0025-1747

Keywords

1 – 5 of 5