Restoring the buyer–seller relationship through online return shipping: The role of return shipping method and return shipping fee

Martínez–López, Francisco J.; Feng, Changyuan; Li, Yangchun; Mata, Marc Sansó

doi:10.1016/j.elerap.2022.101170

Cited by 7 publications

(4 citation statements)

References 101 publications

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“…This may be due to the fact that the retailers encourage customers' product return behaviors because they make it easy for the customers to do so by having the trial time available as a part of the after-sale service package. This makes it easier for customers to return products that consumers have already used for personal purposes and, consequently, sellers have to pay shipping and handling fees unintentionally (Martnez-López et al ., 2022).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In line with the linear growth of e-commerce, so does product return. At least 30% of all e-commerce orders are ultimately returned (Martnez-López et al ., 2022). According to a poll, 67% of customers who went to an online store with the intention of making a purchase could not obtain all the necessary information and may have planned to return the item if it was unsuitable.…”

Section: Introductionmentioning

confidence: 99%

“…(2022) claimed that the process of returning goods (especially electronics) to manufacturing leaves a waste management problem, which is not in line with the environmentally friendly movement that is increasingly being echoed in this era. As a result of this circumstance, product returns are now the 21st largest economy in the world and remain one of the biggest challenges of retailers that still seen as a pertinent service failure issue (De et al ., 2013; Cheng, 2015; Martnez-López et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

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Credibility of Youtube product reviews and customers' product return intention: a product fit uncertainty perspective

Nugroho

Wang

2023

ITP

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PurposeThis research aims to examine the factors that influence customers' product return intentions and proposes that YouTube product reviews can mitigate customers' desire to return a product.Design/methodology/approachThe proposed theoretical research model and hypothesized relationship were investigated using a quantitative process. This study used 302 data from Indonesian young adult respondents to examine the structural model, which was analyzed using the SmartPLS 3.2 software package.FindingsThe results show that YouTube product reviews, product fit uncertainty and customer satisfaction are the key determinants of customers' product return intention. Furthermore, the results show that the credibility of YouTube product reviews has a major impact on customers' familiarity with a product, satisfaction and the likelihood of returning goods to sellers.Practical implicationsIn the e-commerce industry, increasing the use of YouTube product reviews will help businesses eliminate unnecessary product returns. Sellers are also encouraged to collaborate with YouTube producers to review specific products, which can benefit companies by raising brand awareness and gaining customer feedback. Furthermore, YouTube online product reviews can help consumers avoid having an unpleasant shopping experience that causes emotional reactions and lowers satisfaction.Originality/valueMost research has not considered antecedents in observing the product return phenomenon; this study observes a prerequisite of consumer product returns (i.e. information asymmetry and product familiarity) and investigates the relationships between YouTube product reviews, customer satisfaction and product return intention.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Credibility of Youtube product reviews and customers' product return intention: a product fit uncertainty perspective

Nugroho

Wang

2023

ITP

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“…Wang and Dai (2004) propose a fuzzy constraint satisfaction approach for electronic shopping assistance based on satisfaction with each product. Martínez-López et al (2022) investigated the role of return method and return fee on the buyerseller relationship. Jiang and Benbasat (2014) examine the virtual product experience on the perception of diagnosticity and flow in e-commerce-related to pick-up point inventory Ren et al (2022) proposes an integrated forecast-optimisation approach (Machine Learning -Quantile Regression, MLQR) to optimise the predictive shipping inventory of pick-up points, taking into account emergency shipping based on the historical transaction data of the online retailer.…”

Section: Introductionmentioning

confidence: 99%

The memory concept behind deep neural network models: An application in time series forecasting in the e-Commerce sector

Ramos¹,

Pereira²,

Oliveira³

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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A good command of computational and statistical tools has proven advantageous when modelling and forecasting time series. According to recent literature, neural networks with long memory (e.g., Short-Term Long Memory) are a promising option in deep learning methods. However, only some works also consider the computational cost of these architectures compared to simpler architectures (e.g., Multilayer Perceptron). This work aims to provide insight into the memory performance of some Deep Neural Network architectures and their computational complexity. Another goal is to evaluate whether choosing more complex architectures with higher computational costs is justified. Error metrics are then used to assess the forecasting models' performance and computational cost. Two-time series related to e-commerce retail sales in the US were selected: (i) sales volume; (ii) e-commerce sales as a percentage of total sales. Although there are changes in data dynamics in both series, other existing characteristics lead to different conclusions. "Long memory" allows for significantly better forecasts in one-time series. In the other time series, this is not the case.

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Reducing ecommerce returns with return credits

Martínez–López

Feng

et al. 2022

Electron Commer Res

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Restoring the buyer–seller relationship through online return shipping: The role of return shipping method and return shipping fee

Cited by 7 publications

References 101 publications

Credibility of Youtube product reviews and customers' product return intention: a product fit uncertainty perspective

Credibility of Youtube product reviews and customers' product return intention: a product fit uncertainty perspective

The memory concept behind deep neural network models: An application in time series forecasting in the e-Commerce sector

Reducing ecommerce returns with return credits

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