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

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

"Looking behind the veil": invisible corporate intangibles, stories, structure and the contextual information content of disclosure

Purpose – This paper aims to use a grounded theory approach to reveal that corporate private disclosure content has structure and this is critical in making "invisible" intangibles in corporate value creation visible to capital market participants. Design/methodology/approach – A grounded theory approach is used to develop novel empirical patterns concerning the nature of corporate disclosure content in the form of narrative. This is further developed using literature of value creation and of narrative. Findings – Structure to content is based on common underlying value creation and narrative structures, and the use of similar categories of corporate intangibles in corporate disclosure cases. It is also based on common change or response qualities of the value creation story as well as persistence in telling the core value creation story. The disclosure is a source of information per se and also creates an informed context for capital market participants to interpret the meaning of new events in a more informed way. Research limitations/implications – These insights into the structure of private disclosure content are different to the views of relevant information content implied in public disclosure means such as in financial reports or in the demands of stock exchanges for "material" or price sensitive information. They are also different to conventional academic concepts of (capital market) value relevance. Practical implications – This analysis further develops the grounded theory insights into disclosure content and could help improve new disclosure guidance by regulators. Originality/value – The insights create many new opportunities for developing theory and enhancing public disclosure content. The paper illustrates this potential by exploring new ways of measuring the value relevance of this novel form of contextual information and associated benchmarks. This connects value creation narrative to a conventional value relevance view and could stimulate new types of market event studies

Social Commerce: An Empirical Examination of the Antecedents and Consequences of Commerce in Social Network Platforms

This paper studies a pioneering venture of integrating e-business with social network platforms and seeks to understand the antecedents and consequences of social commerce . In particular, we conduct an econometric analysis examining how the characteristics of the users and their social networks affect their decision to participate in this novel service. Based on the empirical results, we find that the social neighbors of the users and their economic behavior, the brand loyalty of the users, and their familiarity with the platform have significant effects on the likelihood of social purchases. Additionally, we build predictive models in order to both identify the effective disseminators of information and discover their distinguishing characteristics. Finally, we both contribute to the related literature, discovering new rich findings, and provide actionable insights with major implications for brands and marketers who would like to generate direct sales on social network platforms and orchestrate word-of-mouth

TOWARDS MINING BRAND ASSOCIATIONS FROM USER-GENERATED CONTENT (UGC): EVIDENCE FROM LINGUISTIC CHARACTERISTICS

Consumers’ brand associations offer qualitative explanations on a brand’s success or failure and are typically elicited using survey-based instruments. Marketers are interested in time- and cost-efficient, automated brand association elicitation approaches. To enable an automated brand association elicitation, we show that brand associations can be formalized and described by patterns of linguistic part-of-speech sequences that differ from ordinary speech which is required for an automated extraction via text mining. Furthermore, we provide evidence that UGC is an adequate data-source for an automated brand association elicitation. We do that by comparing survey-based and UGC data-sources using linguistic part-of-speech sequence- and n-gram analysis as well as sequential pattern mining. We contribute to exiting research by establishing prerequisites for the construction of novel information systems that use text mining to extract brand associations automatically from UGC

Please, talk about it! When hotel popularity boosts preferences

Many consumers post on-line reviews, affecting the average evaluation of products and services. Yet, little is known about the importance of the number of reviews for consumer decision making. We conducted an on-line experiment (n= 168) to assess the joint impact of the average evaluation, a measure of quality, and the number of reviews, a measure of popularity, on hotel preference. The results show that consumers' preference increases with the number of reviews, independently of the average evaluation being high or low. This is not what one would expect from an informational point of view, and review websites fail to take this pattern into account. This novel result is mediated by demographics: young people, and in particular young males, are less affected by popularity, relying more on quality. We suggest the adoption of appropriate ranking mechanisms to fit consumer preferences. © 2014 Elsevier Ltd