Decision-making Process Of Customers Research Articles

Increasing student engagement through digital branding in higher education marketing

As a result of the employment of social media, interpersonal contact and user involvement are made possible, which results in the modification of traditional hierarchical communication techniques and the introduction of an unexpected component into the decision-making process of customers. Even though it is considered to be a component of internet marketing, social media marketing is a crucial component for individuals and businesses that need to retain their competitiveness. On the other hand, the success of social media marketing is dependent on the presence of innovative items, the responsiveness of organizations to the market, and the construction of powerful online platforms. Twitter, Instagram, and Facebook are just some of the social networking sites that have evolved into indispensable tools for marketers. Since they enable interactive contact, these platforms are of tremendous value when it comes to the marketing of companies and services. Websites that facilitate social networking play an important part in the spread of information about educational institutions of higher learning. These websites also serve as effective means for recruiting prospective students. It has not yet been determined whether or not the content that is broadcast on these internet platforms has an effect on the cognitive processes that prospective students use when making choices about their education. The acknowledgment of user interaction on social media platforms is the most important non-financial benefit that organizations may get from their use of these platforms. When we have a thorough awareness of user interaction across a variety of platforms, we may have a better grasp of the influence that it has on how people perceive businesses. Instagram has become a significant medium for educational institutions of higher learning due to its visual-centric qualities, as well as its heightened engagement rates among a varied, youthful, and multinational community. This article places a strong emphasis on the relevance of brand equity within the context of the marketing strategy used by educational institutions of higher learning. When it comes to the idea of brand equity, there are several elements that may have an impact on it. Some of these characteristics include, but are not limited to, the personality of the brand, social credibility, and market position. It is quite necessary for educational institutions to make certain that they provide excellent services and facilities to their students. Nevertheless, the construction of a communication system that is both competent and effective is equally important for the purpose of boosting the positioning of the university's brand and effectively managing the value of its brand. The concept of "brand soul" serves as the basis for this, and it refers to the way the teachers and staff of the educational institution become the embodiment of the brand.

Investigating the use experience of restaurant service robots: the cognitive–affective–behavioral framework

This study investigates the approach behavior of the respondents by using the cognitive–affective–behavioral framework in the context of restaurant service robots. An online survey was conducted among customers who had patronage experience visiting service robot restaurants in Taiwan. A total of 230 valid samples were employed for the final analysis using the maximum likelihood estimation method. The hypothesized relationships were tested using structural equation modeling. Furthermore, a multigroup analysis based on the visit experience (frequency of visit) and generation was conducted. Cognitive components such as competence and coolness positively influence affective components such as emotions and satisfaction. Approach behavior, the behavioral outcome, is positively influenced by emotions and satisfaction. Likewise, emotions positively influence satisfaction. This study highlights the sequential relationship in terms of cognitive, affective and behavioral perspectives in the decision-making process of customers after their encounter with service robots at restaurants.

Pricing strategy and marketing distribution channels on customer satisfaction and purchasing decision for green products

This paper aims to investigate the impact of pricing strategy and distribution channels on the decision-making process of customers when purchasing green products. The focus is on the satisfaction of customers who purchase these products. The study is conducted on the population of all customers who purchase green products from small and medium-sized enterprises (SMEs). The sample size is determined using a formula that considers the number of variables or indicators. The study uses Partial Least Squares Structural Equation Modelling (PLS-SEM), which is a method used to test variants-based structural equation models with the support of SmartPLS software. The results show that all seven hypotheses are supported, indicating that pricing strategy and distribution channels play a critical role in customer satisfaction and decision-making processes when purchasing green products. The results have implications for SMEs that sell green products as they need to focus on their pricing strategies and distribution channels to increase customer satisfaction and decision-making. This study provides essential insights into the impact of pricing strategy and distribution channels on customer satisfaction and decision-making when purchasing green products. The findings can guide SMEs in developing effective marketing strategies to persuade more customers to purchase green products and contribute to environmental sustainability.

Customer Choice Modelling: A Multi-Level Consensus Clustering Approach

Customer Choice Modeling aims to model the decision-making process of customers, or segments of customers, through their choices and preferences identified by the analysis of their behaviors in one or more specific contexts. Clustering techniques are used in this context to identify patterns in their choices and preferences, to define segments of customers with similar behaviors, and to model how customers of different segments respond to competing products and offers. However, data clustering is an unsupervised learning task by nature, that is the grouping of customers with similar behaviors in clusters must be performed without prior knowledge about the nature and the number of intrinsic groups of data instances, i.e., customers, in the data space. Thus, the choice of both the clustering algorithm used and its parameterization, and of the evaluation method used to assess the relevance of the resulting clusters are central issues. Consensus clustering, or ensemble clustering, aims to solve these issues by combining the results of different clustering algorithms and parameterizations to generate a more robust and relevant final clustering result. We present a Multi-level Consensus Clustering approach combining the results of several clustering algorithmic configurations to generate a hierarchy of consensus clusters in which each cluster represents an agreement between different clustering results. A closed sets based approach is used to identified relevant agreements, and a graphical hierarchical representation of the consensus cluster construction process and their inclusion relationships is provided to the end-user. This approach was developed and experimented in travel industry context with Amadeus SAS. Experiments show how it can provide a better segmentation, and refine the customer segments by identifying relevant sub-segments represented as sub-clusters in the hierarchical representation, for Customer Choice Modeling. The clustering of travelers was able to distinguish relevant segments of customers with similar needs and desires (i.e., customers purchasing tickets according to different criteria, like price, duration of flight, lay-over time, etc.) and at different levels of precision, which is a major issue for improving the personalization of recommendations in flight search queries.

목표지향적 행동모델을 이용한 국내 저비용항공사 승객의 항공사 선택속성, 열망, 행동의도의 관계에 관한 탐색적 연구

As global LCC(Low Cost Carrier) market is growing as an outstanding trend, domestic LCC market has grown as another pillar of the aviation market with remarkable pace and scale of growth. However, prior research on the selection attributes of LCC and the decision-making process of customers in Korea is insufficient. This study applied the model of goal-oriented behavior to analyze the relations among LCC selection attributes, decision-making process, desire and behavior intention. Among selection attributes, this study found that in-flight service, safety, price, airline image had impact on the decision-making process. Attitude, subjective norm, positive anticipated emotion and perceived behavior control affected desire and desire affected behavior intention in sequence. Negative anticipated emotion was the only factor with no impact on desire and mediated effect of desire between perceived behavior control and behavior intention was verified.

An Aspect-Sentiment Pair Extraction Approach Based on Latent Dirichlet Allocation

Online user reviews have a great influence on decision-making process of customers and product sales of companies. However, it is very difficult to obtain user sentiments among huge volume of data on the web consequently; sentiment analysis has gained great importance in terms of analyzing data automatically. On the other hand, sentiment analysis divides itself into branches and can be performed better with aspect level analysis. In this paper, we proposed to extract aspect-sentiment pairs from a Turkish reviews dataset. The proposed task is the fundamental and indeed the critical step of the aspect level sentiment analysis. While extracting aspect-sentiment pairs, an unsupervised topic model Latent Dirichlet Allocation (LDA) is used. With LDA, aspect-sentiment pairs from user reviews are extracted with 0.86 average precision based on ranked list. The aspect-sentiment pair extraction problem is first time realized with LDA on a real-world Turkish user reviews dataset. The experimental results show that LDA is effective and robust in aspect-sentiment pair extraction from user reviews.

Can setting hotel rate restrictions help balance the interest of hotels and customers?

Purpose– The purpose of this study is to assist hoteliers to design hotel rate restrictions that are in the interest of hotels and of customers, under the joint influence of room rates and rate fences.Design/methodology/approach– This study used a questionnaire as the data collection instrument. Conjoint analysis was applied to examine customers’ preferences regarding hotel rate fences.Findings– The results indicate that refundability, price and advance requirement, in that order, are identified as the most important attributes in the overall decision-making process of customers. Changes allowed and rule type are comparatively less important attributes.Research limitations/implications– This study extends the hotel revenue management literature by examining customer preferences for hotel rate restrictions based on their socio-demographic characteristics and travel behavior. This study also illustrates the usefulness of conjoint analysis in determining the utility values of hotel-room rates and rate fences.Practical implications– The findings of this study may help professional hoteliers to better understand customers’ decision-making process and underlying needs, thus helping them to design attractive rate fences that are in the interests of both hotels and customers.Originality/value– This study is the first to investigate customer preferences regarding hotel rate restrictions based on demographic and travel behavior information. The identification of these preference values should help hoteliers to identify the decision-making processes and needs of customers.

Transit Prepayment Challenges: Factors Influencing Customers’ Willingness To Purchase High-Value Tickets

Despite the availability of, and benefits associated with, prepaid storedvalue fare cards, many transit riders continue to select traditional singleride or round-trip tickets for payment of their fares. At the San Francisco Bay Area Rapid Transit District (BART), an effort was made to assess why many riders select low-value fare instruments and to evaluate potential actions to increase the share of riders buying higher-value tickets. The qualitative and quantitative research techniques utilized at BART to explore this issue are described. Focus groups were conducted to identify important decision-making factors, which were later quantified through a telephone survey. Fear of losing high-value tickets coupled with a lack of information regarding the availability of these tickets appears to limit high-value ticket sales. Some possible approaches to encouraging the purchase of high-value tickets may include a last-ride-free feature and increased awareness of the availability of discount tickets at off-site sales outlets. This study provides insight into the decision-making process of customers when purchasing stored-value tickets and can help inform the policy-making process regarding ticket systems.