An Autoethnographic Approach to Examining Electronic Retail Development

Autoethnographic approaches to doing research in retailing are rare. Through the researcher reflecting on and analysing her own personal experiences as a fashion retail store proprietor, this study reconstructed the process of her strategic decision making with regard to moving from selling fashion goods via an independent high street store to selling online. The study is concerned with the issues surrounding the adoption of e-commerce. In doing so, the study reviewed the various development models that exist within e-commerce literature, and in particular, examined the extent to which a retailer adoptions an evolutionary and linear approach to developing a web site. Hence the study’s contribution to advances in retailing is in the field of strategic decisions pertaining to electronic retailing. Specifically the aim of the study was to either confirm or adjust the models within e-commerce literature that describe the internet adoption process. Through the adoption of an autoethnographical approach, the study acknowledges that there is a complex interdependency between the researcher and the researched and thereby utilizes subjective experience as an intrinsic part of the research process. This is achieved through offering the retail proprietor’s ‘insider’ perspective based upon both self narratives and self observations. Whilst the author’s acknowledge that the subject of the study needs to be examined in a broader sense, beyond the self generated data presented in the study, they argue that such self introspections can be considered as a basis of useful, albeit non-scientific, knowledge in itself. In this study the intention is to use the data as a means of generating hypotheses which will be tested in a future study by a more traditional research technique. This study is a work in progress

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201