A Novel K-Means Clustered Support Vector Machine Technique for Prediction of Consumer Decision-Making Behaviour

A greater number of consumers are using social networks to express their feedback about the level of service provided by hotels. Online reviews from patrons can be used as a forum to enhance the level of service of hotels. Customer reviews are indeed a reliable and dependable source that aid diners in determining the quality of their cuisine. It is critical to develop techniques for evaluating client feedback on hotel services. In order to accurately anticipate the consumers' decision-making behaviors based on hotel internet evaluations, this study proposes a novel K-Means Clustered Support Vector Machine (KMC+SVM) technique. Principal Component Analysis (PCA) is employed to determine the characteristics from the preprocessed data while the Min-Max normalization approach is used to standardize the raw data. The performance of the suggested technique is then evaluated and contrasted with a few other methods that are currently in use in terms of accuracy, sensitivity, RMSE, and MAE. The findings demonstrated that segmenting customers based on their online evaluations can accurately predict their choices and assist hotel management in establishing priorities for service quality enhancements

An Empirical Study of Branding Strategy at Dealer point for Selling of Car-a qualitative & systematic Review of Literature

India is one of the world’s fastest growing automobile markets and is poised to become the third largest passenger’s car market by 2020 (Philip, L. 2016, Economic Times). The recorded sales growth of 4 wheelers like passenger car & utility vehicle has also risen up to 7.87 % and 6.25% respectively during April-March 2016 (SIAM, 2015-16). But what makes a car maker like Japan’s Maruti Suzuki and Korea’s Hyundai enjoys more than 67% of market share while others like US car makers Ford India and General Motors combined market share is just 4-5%(Philip,L.2016,The Economic Times). Sales in the North & East region have evidenced only 5%of changes in the FY16 which is comparatively lower than the west & south region (Khan,A.N,2016, The Economic Times). The Japanese car makers(Honda, Hyundai, Isuzu Motors, Nissan &Toyota) achieved an average of 48.01% of growth till July 2016 having a better stand from the Indian car makers (Hindustan Motors, M&M,M&S, Tata & Force motors) i.e. 6.74% (Autocar Pro News Desk, July 2016). In this study the researcher explored the factors affecting the satisfaction of prospective car buyers and existing car users at dealer point and facilitate dealer to create a brilliant “moment of truth” (Pioneered by JanCarlzon) when a customer encounter with company.(Madge, Davidson & Beaujean, 2006

How buyers perceive the credibility of advisors in online marketplace: review balance, review amount and misattribution

In an online marketplace, buyers rely heavily on reviews posted by previous buyers (referred to as advisors). The advisor’s credibility determines the persuasiveness of reviews. Much work has addressed the evaluation of advisors’ credibility based on their static profile information, but little attention has been paid to the effect of the information about the history of advisors’ reviews. We conducted three sub-studies to evaluate how the advisors’ review balance (proportion of positive reviews) affects the buyer’s judgement of advisor’s credibility (e.g., trustworthiness, expertise). The result of study 1 shows that advisors with mixed positive and negative reviews are perceived to be more trustworthy, and those with extremely positive or negative review balance are perceived to be less trustworthy. Moreover, the perceived expertise of the advisor increases as the review balance turns from positive to negative; yet buyers perceive advisors with extremely negative review balance as low in expertise. Study 2 finds that buyers might be more inclined to misattribute low trustworthiness to low expertise when they are processing high number of reviews. Finally, study 3 explains the misattribution phenomenon and suggests that perceived expertise has close relationship with affective trust. Both theoretical and practical implications are discussed

Digital Analytics:Modeling for Insights and New Methods

Firms are increasingly turning towards new-age technologies such as artificial intelligence (AI), the internet of things (IoT), blockchain, and drones, among others, to assist in interacting with their customers. Further, with the prominence of personalization and customer engagement as the go-to customer management strategies, it is essential for firms to understand how to integrate the new-age technologies into their existing practices seamlessly to aid in the generation of actionable insights. Towards this end, this study proposes an organizing framework to understand how firms can use digital analytics, within the changing technology landscape, to generate consumer insights. The proposed framework begins by recognizing the forces that are external to the firm that then leads to the generation of specific capabilities by the firm. Further, the firm capabilities can lead to the generation of insights for decision making that can be data-driven and/or analytics-driven. Finally, the proposed framework identifies the creation of value-based outcomes for firms and customers, resulting from the insights generated. Additionally, we identify moderators that influence (a) the impact of external forces on the development of firm capabilities, and (b) the creation of insights and subsequent firm outcomes. This study also identifies questions for future research that combines the inclusion of new-age technologies, generation of strategic insights, and the achievement of established firm outcomes

Can we sense shift in consumer behaviour in Portuguese retail companies due to the pandemic?

The 2019 coronavirus pandemic (COVID-19) has effects in the most diverse fields of our society, from mental health and lifestyle to commerce and education. A huge adaptation by the population and restructuring of habits was necessary to make progress in this new reality, leading several companies to reinvent the way they conducted their businesses and a complete metamorphosis of their business plans. As such, there was an interest in conducting this research to understand how consumer behaviour in Portuguese retail companies was affected by the lockdown in the country, aiming to identify the change in the purchase intention of consumers living in Portugal and what motivated this same change, allowing extracting information to help organizations in the decision making. Thus, 15,000 comments were collected from the social network Facebook referring to the pre-lockdown, lockdown, and post-lockdown period in Portugal. Then, data mining techniques and processes were used to clean the set of collected data and extract knowledge. Furthermore, an Intention Mining analysis was carried out to assess the collected comments and draw conclusions. Finally, the results of this study indicate a negative evolution in the purchase intention of consumers, verifying that the relationship with the company deteriorated and problems in the supply chain increased, indicating that it is necessary to redirect strategies to improve the service of customer support and distribution channels to meet customer satisfaction and may apply to other countries in similar contexts.A pandemia do coronavírus 2019 (COVID-19) tem efeitos nos mais diversos campos da sociedade, desde a saúde mental e estilo de vida, ao comércio e educação. Foi necessária uma enorme adaptação da população e reestruturação de hábitos para conseguir avançar nesta nova realidade, levando várias empresas a reinventar a forma como conduziam os seus negócios e a uma completa metamorfose dos respetivos planos de negócio. Como tal, surgiu o interesse em realizar esta investigação para compreender como o comportamento do consumidor nas empresas de retalho portuguesas foi afetado pelo confinamento no país, tendo como objetivo identificar a mudança na intenção de compra dos consumidores a viver em Portugal e o que motivou essa mesma mudança, permitindo extrair informações que permitam auxiliar na tomada de decisão das organizações. Assim, recolheram-se 15,000 comentários da rede social Facebook referentes ao período pré-confinamento, confinamento e pós-confinamento em Portugal. Em seguida, foram utilizados técnicas e processos de mineração de dados para limpeza do conjunto de dados recolhidos e extração de conhecimento. Ainda, realizou-se uma análise de mineração de intenções para avaliar os comentários recolhidos e extrair conclusões. Por fim, os resultados deste estudo indicam uma evolução negativa na intenção de compra dos consumidores, verificando-se que a relação com a empresa deteriorou-se e problemas ao nível da supply chain aumentaram, indicando ser necessário redirecionar as estratégias para melhorar o serviço de apoio ao cliente e os canais de distribuição para ir ao encontro da satisfação dos clientes, podendo ser aplicável a outros países em contextos semelhantes

Research on the Construction Mechanism of Consumers’ Trust Intentions and Behaviors in the Context of Live Streaming Shopping

As a new-type media form integrating text, image, video, and audio, live streaming itself is an excellent way of informationcarrying and interaction. And the development of information technology makes the use of live streaming shopping easier and more convenient. At the same time, with the popularity of live streaming marketing, there are also some consumption traps, which not only harm the rights and interests of consumers but also affect its own development. Thus, how to build trust and improve the credit evaluation mechanism has become a common concern of academic and industrial circles. Anchored in the theory of planned behavior (TPB) and other research results, this paper expounds on the definition and connotation of trust intention in live streaming shopping in detail. From the perspective of consumers, the trust model of live streaming shopping is constructed based on the comprehensive consideration of social presence, consumers\u27 personal attitude, and structural assurance. It adopts partial least squares (PLS) structural equation modeling (SEM) to evaluate the research model and hypothesis. On the basis of 259 samples, the result shows that consumers\u27 trust behavior in live streaming shopping is mainly affected by live streamers\u27 personalities, comment information, social presence, platform characteristics, usefulness, and structural assurance. The research result of this paper will play a positive role in building a more credible environment, improving the trust relationship with consumers, and promoting potential transactions. Meanwhile, it also lays a foundation for understanding consumers\u27 trust behavior and related theories in the context of China

Taking Stock of the Digital Revolution: A Critical Analysis and Agenda for Digital, Social Media, and Mobile Marketing Research

Marketing has been revolutionized due to the rise of digital media and new forms of electronic communication. In response, academic researchers have attempted to explain consumer- and firm-related phenomena related to digital, social media, and mobile marketing (DSMM). This paper presents a critical historical analysis of, and forward-looking agenda for, this work. First, we assess marketing’s contribution to understanding DSMM since 2000. Extant research falls under three eras, and a fourth era currently underway. Era 1 focused on digital tools and platforms as consumer and marketer decision aids. Era 2 studied online communications channels (e.g., online forums) as word of mouth marketing “laboratories,” capturing the potential of DSMM for social information transmission. Era 3 embraced the notion of “connected consumers” by considering various antecedents and consequences of socially interconnected consumers in marketplaces. Era 4, currently starting, considers mobile marketing and brings psychological and social theories to bear on emergent DSMM issues. Second, we critique the DSMM literature and advance a series of recommendations for future research. While we find much to applaud, we argue that several problems limit the relevance of this research moving forward and suggest ways to alleviate these concerns moving forward

Towards Reliable Online Feedback : The Impact of User Preference and Visual Cues in Rating Scales and User Ratings

With the rise of dependency on online shopping and service providers, consumer ratings and reviews help users decide between good and bad options. Reliable and useful ratings can ensure better consumer service, product sales, brand management. Any underlying bias or external factors affecting users emotional stability can corrupt the credibility of user feedback. Prior studies suggest that the visual representation and design elements provided with a rating scale can affect the user's responses, specially if the rating scales have visual labels that generate an emotional response in users. Since there are a number of rating scale designs used in online e-commerce sites and recommender systems, it is also important that users get a say in which rating scale they are comfortable in using. Online marketplace still does not provide a platform to consider user's own choice in this matter. This preferential choice of scales can make users more involved in the rating process and help get the best response from them. Earlier research have already proved that users have specific personalized preferences when it comes to using rating scales to give feedback online. Further emphasis on how this preference and visual cues together can elicit more reliable online feedback mechanism is required in this area. This thesis aims to investigate whether the preference of users in rating scales influences the reliability and authenticity of user's ratings. It also explores the user's reaction to certain visual cues in rating scales, and how user's preferences of rating scale are influenced by such visual elements. A within-subject study ($n$ = 187) was conducted to collect user ratings of popular products with six different rating scale designs, using two types of visual icons (stars and emojis) and colour-metaphors (using a warm-cool and a traffic-light metaphors). Statistical analysis from the survey shows that users prefer the scale with most visually informative design (traffic-light metaphor colours with emoji icons). It also shows that users tend to give their true ratings on scales they prefer most, rather than the scale design they are most familiar with. The rating score analysis also demonstrates a positive shift and better consistency in the ratings given on more visually rich scales. Based on these results, it can be concluded that user involvement is desirable in selecting the rating scale designs, and meaningful visual cues can contribute in getting more accurate (truthful) rating scores from users. The proposed approach of user preference based rating system has novelty because I elicited the user's own opinion on what their accurate or ``true" rating is; rather than only relying on analysing the data received from the rating scores. This work can offer insights for online rating scale designs to improve the rating decision quality of users and help online business platforms obtain more credible feedback from customers which can significantly improve their services and user satisfaction