Yes / The competitive landscape of multiple e-commerce platforms and the vast amount of product reviews associated with these platforms have supported both consumers’ online shopping decision making and also served as a reference for product attribute performance improvement. This paper proposes a sentiment-driven fuzzy cloud multi-criteria model for online product ranking and performance to provide purchase recommendations. In this novel model, Bi-directional Long Short-Term Memory Network-Conditional Random Fields (BiLSTM-CRF), sentiment analysis, and K-means clustering are first integrated to mine product attributes and compute sentiment values based on reviews from various platforms. Next, considering the confidence of the sentiment value, the cloud model is combined with q-rung orthopair fuzzy sets to define the new concept of the q-rung orthopair fuzzy cloud (q-ROFC) and the interaction operational laws between q-ROFCs are given. The sentiment values of each product attribute from different platforms are cross-combined and transformed into a type of q- ROFC, while multiple interactive information matrices are established. To investigate the correlation among homogeneous attributes, the q-ROFC interaction weighted partitioned Maclaurin Symmetric mean operator is proposed. Finally, we provide real-world examples of online mobile phone ranking and attribute performance evaluation. The results show that our proposed method offers significant advantages in dealing with customer purchase decisions for online products and problems with performance direction identification. Managerial implications are discussed.

I denna studie jämförs 14 kostnadsfria e-handelsplattformar utifrån funktionalitet som har identifierats för att öka försäljning och underlätta skalbarhet. För att betygsätta funktionernas påverkan för ökad försäljning respektive underlätta skalbarheten så har ehandlare inom nischen kläder och accessoarer låtits besvara en enkät. För att identifiera relevant funktionalitet till de två olika synsätten utgick forskarna från litteraturen. Det är utifrån dessa funktioner som sedan jämförelsen av e-handelsplattformarna och enkäten bygger på. Studien indikerar på att responsiv design och att kunna erbjuda sina kunder flera betallösningar är de två mest relevanta funktionerna för att nå en ökad försäljning. För att underlätta skalbarhet visar resultatet att det är relevant att kunna köpa till tillägg och teman. Sett till vilken e-handelsplattform som innehar mest relevant funktionalitet så ger studiens resultat indikationer på att Abantecart och OpenCart är de e-handelsplattformar som har den mest relevanta funktionaliteten för att öka försäljning och underlätta en framtida expansion. / In this study 14 e-commerce platforms are compared based on their functionality. The functionality has been identified to increase sales and facilitate scalability. To rate the impact of the functions, e-tailors in the niche of clothing and accessories were asked to respond a questionnaire. To identify relevant functionality for both of the approaches, the researchers used literature. The functionality were used as a framework when comparing the 14 e-commerce platforms. This study indicates that all functions are not equal. Responsive design and multiple payment solutions are considered most critical and are directly linked to increased sales figures. For the e-retailers to achieve scalability, this study finds that purchasable add-ons and themes plays a big role. The e-commerce platforms were rated thanks to data collection and data analysis. Abantecart and Opencart both received high ratings regarding functionality and the ability to increase sales numbers and enable future expansions.

<div> <p>E-commerce organizations, these days, need to keep striving for constant innovation. Customers have a massive impact on the performance of an organization, so industries need to have solid customer retention strategies. Various big data analytics methodologies are being used by organizations to improve overall online customer experience. While there are multiple techniques available, this research study utilized and tested a framework proposed by Laux et al. (2017), which combines Big Data and Six Sigma methodologies, to the e-commerce domain for identification of issues faced by the customer; this was done by analyzing online product reviews and ratings of customers to provide improvement strategies for enhancing customer experience. </p> <p>Analysis performed on the data showed that approximately 90% of the customer reviews had positive polarity. Among the factors which were identified to have affected the opinions of the customers, the Rating field had the most impact on the sentiments of the users and it was found to be statistically significant. Upon further analysis of reviews with lower rating, the results attained showed that the major issues faced by customers were related to the product itself; most issues were more specifically about the size/fit of the product, followed by the product quality, material used, how the product looked on the online portal versus how it looked in reality, and its price concerning the quality.</p> </div> <br>