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

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

Beyond Risk Profiling: Achieving better investment outcomes for consumers and industry

In the wake of the Retail Distribution Review, there remain fundamental questions about how best to support consumers to make sound investment decisions, particularly those with modest amounts of money to invest, for whom a poor investment decision may have a disproportionate adverse impact. The advent of new pension freedoms from April 2015, which give people more choice and flexibility about how they use their retirement savings, adds further impetus to the issue. To help inform policy and practice on this important subject, in June 2015 we brought together consumer and industry experts to explore possible new approaches to improve risk profiling and investment decision-making

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Incorporating service quality tools into Kansei Engineering in services: A case study of Indonesian tourists

Due to market dynamics and challenges, it is imperative for companies to put their concern on strategic marketing orientation. In facts, products and services of similar quality are ubiquitous in today’s global market. Basically, functionality and usability alone are no longer prominent success factors in product and service innovation because customers today concern themselves more on satisfying their emotions than merely their cognition. Kansei Engineering (KE) has shown its superiority in investigating and modelling customer emotion (“Kansei” in Japanese) for product development. In dealing with customer needs, service quality tools such as quality function deployment (QFD) and the Kano model, have been applied extensively. But none have been able to incorporate and model customer’s emotional needs. Some attention has been given to investigate this but, thus far, there is no formal methodology that can account for customer emotional needs in service design. To fill this niche, this study proposed an integrative framework of KE incorporating the Kano model and QFD applied to services. This study extended the work by Hartono and Tan (2011) and Hartono et al. (2012) and presented a survey on luxury hotel services involving more than a hundred Indonesian tourists as the subject of study. Luxury hotels are reported to have greater strength of emotion than any other hotel segment. This work confirmed that emotion is to be more important than cognition in impacting overall customer satisfaction. Practically, it gives insight on which service attributes deserve more attention with regard to their impact on customer emotion. Indonesian tourists shared a common response to the Kansei word “elegant” which correlates with their common cultural dimension of “power distance”. Performing a Kansei evaluation to understanding cultural backgrounds may yield valuable insights for international tourist marketing strategies and companies’ business sustainability