Trust barriers to online shopping: Investigating and prioritizing trust barriers in an intuitionistic fuzzy environment

Abstract

Although the internet plays a significant role in daily activities, particularly shopping, some individuals are reluctant to shop online due to distrust in the online environment. This research aims to investigate trust barriers to online shopping from the relevant literature and prioritize them in an intuitionistic fuzzy environment. After reviewing the relevant literature, 18 barriers were extracted and a questionnaire based on these barriers was distributed among 150 internet users to rank three criteria of Failure Mode and Effect Analysis (FMEA), namely severity, occurrence, and detection. Afterward, using the entropy method based on FMEA in an intuitionistic fuzzy environment, the priority of each barrier was assessed. The findings showed that the most important barriers to trust in online shopping are ‘privacy risk’, ‘lack of feel‐and‐touch associated with online purchases’, ‘psychological risk’, ‘social risk’ and ‘feeling that e‐vendors are pretending to care about buyers’ welfare’. Based on the findings, marketing managers should focus on improving and promoting the infrastructure security of their online shopping sites, providing comprehensive information, and benefiting from influencers and celebrities’ endorsements in order to increase customer trust.

Social Distancing, Health Concerns, and Digitally Empowered Consumption Behavior Under COVID-19: A Study on Livestream Shopping Technology

“…Consumers are more likely to trust livestream shopping if they believe the technology offers less risk, more benefits, and fewer restrictions. The results are consistent with previous research ( 7 , 39 , 40 ). However, the result for significant positive effect of perceived susceptibility and perceived severity on trust is contrary to previous studies, such as Yuen et al ( 7 ), who found a significant negative effect of perceived safety threat on consumers' trust about the cruise services.…”

“…In this context, perceived barriers would include an individual's perception of the expense and difficulty of taking health precautions (31). Existing research has shown that perceived barriers can significantly reduce consumer trust (39) while, according to Abror et al (40), perceived benefits is an important antecedent of consumer trust. Chen and Chang (41) also posited that there is a positive association between perceived benefits and consumer trust because a high degree of perceived benefits may boost post-purchase product confidence.…”

A time of transition: changes in Irish food behaviour and potential implications due to the COVID-19 pandemic

“…In considering the medium to long term, such decision-making should consider the factors identified in previous research as barriers to online shopping which may resurface in the future. For example, Rasty et al (2020) reported that the most important barriers to trust in online shopping are "privacy risk", "lack of feel-and-touch associated with online purchases", "psychological risk", "social risk" and "feeling that e-vendors are pretending to care about buyers' welfare". While increased shopper experience with such services may reduce or eliminate the perceived risks, the sensory aspects associated with food shopping in particular may become more important again when anxiety levels lower.…”

Multifeatured Image Retrieval Techniques Based on Partial Differential Equations for Online Shopping

“…Shape features With the image feature extraction model, the extracted feature vectors are generally of two types: a floating-point vector and a binary-valued hash-encoded vector that has been dimensionally reduced by a hash-encoding algorithm. For these two types of feature representations, choosing an appropriate similarity matching method can directly improve the efficiency of image identification retrieval [19][20][21]. Euclidean distance, cosine distance, and Hamming distance are commonly used similarity measures in image identification retrieval Euclidean distance, and cosine distance applies to floating-point vectors, while Hamming distance applies to binary-valued hash-encoded vectors [22,23].…”

“…Some classical metric functions often only refer to a single attribute of the data, and the reference sign of the correlation is not obvious. The output of such a calculation, which only considers one attribute and ignores the potential correlation, is not a very effective indication of the distance between the data [20].…”