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Article
Publication date: 7 August 2017

Yalan Yan, Xi Zhang, Xianjin Zha, Tingting Jiang, Ling Qin and Zhiyuan Li

Digital libraries and social media are two sources of online information with different characteristics. The purpose of this paper is to integrate self-efficacy into the…

Abstract

Purpose

Digital libraries and social media are two sources of online information with different characteristics. The purpose of this paper is to integrate self-efficacy into the analysis of the relationship between information sources and decision making, and to explore the effect of self-efficacy on decision making, as well as the interacting effect of self-efficacy and information sources on decision making.

Design/methodology/approach

Survey data were collected and the partial least squares structural equation modeling was employed to verify the research model.

Findings

The effect of digital library usage for acquiring information on perceived decision quality (PDQ) is larger than that of social media usage for acquiring information on PDQ. Self-efficacy in acquiring information (SEAI) stands out as the key determinant for PDQ. The effect of social media usage for acquiring information on PDQ is positively moderated by SEAI.

Practical implications

Decision making is a fundamental activity for individuals, but human decision making is often subject to biases. The findings of this study provide useful insights into decision quality improvement, highlighting the importance of SEAI in the face of information overload.

Originality/value

This study integrates self-efficacy into the analysis of the relationship between information sources and decision making, presenting a new perspective for decision-making research and practice alike.

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Article
Publication date: 7 November 2016

Congying Guan, Shengfeng Qin, Wessie Ling and Guofu Ding

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive…

Abstract

Purpose

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

Design/methodology/approach

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

Findings

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Originality/value

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

Details

International Journal of Clothing Science and Technology, vol. 28 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 13 March 2019

Congying Guan, Shengfeng Qin and Yang Long

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion…

Abstract

Purpose

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.

Design/methodology/approach

This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.

Findings

The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.

Originality/value

The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 25 January 2013

Yicheng Liang, Marcus W. Feldman, Shuzhuo Li and Gretchen C. Daily

The aim of this paper is to address a local separability character partly identified by non‐farm participation behaviors in the context of multiple market imperfections.

Abstract

Purpose

The aim of this paper is to address a local separability character partly identified by non‐farm participation behaviors in the context of multiple market imperfections.

Design/methodology/approach

The paper develops a model to analyze agricultural household's non‐farm participation based on heterogeneous asset endowments. The model is applied to recent data from Zhouzhi, a mountainous county in rural western China.

Findings

The paper shows that human capital, social capital and other capital assets have significant but different effects on the agricultural household's participation in non‐farm activities, and they help to break down non‐farm labor constraints. Nonseparability holds only for those households unable to participate in non‐farm activities due to poor asset endowments.

Originality/value

The agricultural household model developed in this paper and its application in China provide insights into theory and empirical analysis of agricultural households' behavior and rural development.

Details

China Agricultural Economic Review, vol. 5 no. 1
Type: Research Article
ISSN: 1756-137X

Keywords

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Article
Publication date: 3 April 2020

Preeti Virdi, Arti D. Kalro and Dinesh Sharma

Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely…

Abstract

Purpose

Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely provide suggestions as “people who bought this also bought this” while, consumers are unaware about the source of these recommendations. By amalgamating CF–RS with consumers' social network information, e-commerce sites can offer recommendation from social networks of consumers. These social network embedded systems are known as social recommender systems (SRS). The extant literature has researched on the algorithms and implementation of these systems; however, SRS have not been understood from consumers' psychological perspective. This study aims to qualitatively explore consumers' motives to accept SRS in e-commerce websites.

Design/methodology/approach

This qualitative study is based on in-depth interviews of frequent online shoppers. SRS are currently not very widespread in the Indian e-commerce space; hence, a vignette was shown to respondents before they responded to the questions. Inductive qualitative content analysis method was used to analyse these interviews.

Findings

Three main themes (social-gratification, self-gratification and information-gratification) emerged from the analysis. Out of these, social-gratification acts as an enabler, while self-gratification along with some elements of information-gratification act as inhibitors towards acceptance of social recommendations. Based on these gratifications, we present a conceptual model on consumer's acceptance of social recommendations.

Originality/value

This study is an initial attempt to qualitatively understand consumers' attitudes and acceptance of social recommendations on e-commerce websites, which in itself is a fairly new phenomenon.

Details

Online Information Review, vol. 44 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

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Article
Publication date: 4 October 2019

Saswati Tripathi, Krishnamachari Rangarajan and Bijoy Talukder

Pharmaceutical industry involves highly specialized business processes where strong research and development focus along with market differentiation and localization are…

Abstract

Purpose

Pharmaceutical industry involves highly specialized business processes where strong research and development focus along with market differentiation and localization are the deciders of success. This has led to evolution of segments and complexities in supply chain. This paper aims to focus on segmental differences in supply chain performance of Indian Pharmaceutical firms.

Design/methodology/approach

This paper measures supply chain performance of select segmental players of the pharmaceutical industry using financial metrics and supply chain operations reference (SCOR) key performance indicators through a five-year timeline. The best performance results are compared across the segments to identify unique performance features, if any. The sample results are validated through hypothesis testing methodology.

Findings

This paper has evidenced that the innovators segment is performing better in cash-to-cash cycle time and supply chain working capital productivity, whereas generics segment is doing better in distribution cost efficiency and total cost to serve aspects.

Research limitations/implications

The paper is based on historical financial data of firms and measures the firm focused supply chain performance. The results may not be generalized in a global context but serve as a motivator for other researchers to take similar studies. The paper may further be analyzed with primary data of the firms to understand the segmental difference in customer focus supply chain performance measures.

Practical implications

This paper has brought out important segmental supply chain performance features of the Indian pharmaceutical firms and identified segment-specific problems by integrating SCOR KPIs and financial metrics.

Originality/value

This paper has integrated both SCOR KPIs and financial metrics to provide unique insights on segmental differences in the performance behavior of pharmaceutical supply chain.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. 13 no. 4
Type: Research Article
ISSN: 1750-6123

Keywords

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Article
Publication date: 26 June 2019

Emmelie Gustafsson, Patrik Jonsson and Jan Holmström

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer…

Abstract

Purpose

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products.

Design/methodology/approach

A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature.

Findings

With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling.

Research limitations/implications

Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts.

Practical implications

The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes.

Originality/value

This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

Details

Supply Chain Management: An International Journal, vol. 24 no. 5
Type: Research Article
ISSN: 1359-8546

Keywords

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Article
Publication date: 30 January 2019

Efendi Nasibov, Murat Demir and Alper Vahaplar

Beside the development of technology and accessibility, ease of use, ability to reach various products and compare many products at the same time make online shopping even…

Abstract

Purpose

Beside the development of technology and accessibility, ease of use, ability to reach various products and compare many products at the same time make online shopping even more popular. Despite the great advantages provided by online shopping for either consumers or retailers, there are certain issues that must be solved to improve online shopping advantages. Finding right size is one of the biggest barriers against apparel online retailing. Since the use of apparels is directly related with fitting, choosing right size is becoming more critical for retailers and consumers. The purpose of this paper is to contribute to the solution of the problem.

Design/methodology/approach

For the study, the specific size measurements of male shirts (collar, shoulder, chest, waist, arm length in cm) from four different sizes (small, medium, large, x-large) and from eight different brands were collected and stored in a database. Totally, weight, height and body measurements (collar, shoulder, chest, waist and arm length in cm) of 80 male candidates, between the ages of 18 and 35, were measured individually. These data were then used for experiments.

Findings

Any product with known measurements can be compared with users’ body measurement based on fuzzy logic rule and the best-fitted size can be selected for users. Similarly, using the proposed web design, users are able to see desired products on users with similar body type.

Originality/value

In this study, a new mathematical method based on fuzzy relations for apparel size finder is proposed. Beside, this method can group users based on body measurements in order to find people with similar size.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 2
Type: Research Article
ISSN: 0955-6222

Keywords

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Book part
Publication date: 8 May 2019

Barrie Gunter

Abstract

Details

Children and Mobile Phones: Adoption, Use, Impact, and Control
Type: Book
ISBN: 978-1-78973-036-4

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Book part
Publication date: 21 November 2018

Lloyd Ling and Zulkifli Yusop

The US Department of Agriculture (USDA), Soil Conservation Services (SCS) rainfall-runoff model has been applied worldwide since 1954 and adopted by Malaysian government…

Abstract

The US Department of Agriculture (USDA), Soil Conservation Services (SCS) rainfall-runoff model has been applied worldwide since 1954 and adopted by Malaysian government agencies. Malaysia does not have regional specific curve numbers (CN) available for the use in rainfall-runoff modelling, and therefore a SCS-CN practitioner has no option but to adopt its guideline and handbook values which are specific to the US region. The selection of CN to represent a watershed becomes subjective and even inconsistent to represent similar land cover area. In recent decades, hydrologists argue about the accuracy of the predicted runoff results from the model and challenge the validity of the key parameter, initial abstraction ratio coefficient (λ) and the use of CN. Unlike the conventional SCS-CN technique, the proposed calibration methodology in this chapter discarded the use of CN as input to the SCS model and derived statistically significant CN value of a specific region through rainfall-runoff events directly under the guide of inferential statistics. Between July and October of 2004, the derived λ was 0.015, while λ = 0.20 was rejected at alpha = 0.01 level at Melana watershed in Johor, Malaysia. Optimum CN of 88.9 was derived from the 99% confidence interval range from 87.4 to 96.6 at Melana watershed. Residual sum of square (RSS) was reduced by 79% while the runoff model of Nash–Sutcliffe was improved by 233%. The SCS rainfall-runoff model can be calibrated quickly to address urban runoff prediction challenge under rapid land use and land cover changes.

Details

Improving Flood Management, Prediction and Monitoring
Type: Book
ISBN: 978-1-78756-552-4

Keywords

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