Customer Evaluation Research Articles

Technology Trends and Their Impact on the E-commerce Industry

E-commerce allows people to buy and sell goods and services anytime and from anywhere they choose. Consumers are shifting from traditional retail toward online buying due to the reduced prices and faster response from online shopping firms/organizations. The study investigated the development of modern technology i.e. E-commerce which has changed the perceptions and buying behavior of customers. Online media is affected by the enormous amount of data that technology collects from consumer interactions with E-commerce platforms. They may register their preferences, giving them the power to manage product availability and validity while quickening the sale-purchase process. How customers see online payments and purchases has significantly changed as a result of technology use in daily life. The study used the quantitative approach to determine how the latest technology trends have impacted the E-commerce industry. The research instrument was online surveys since they are quick and convenient in collecting the responses. The dignity of the respondents was respected and informed permission was obtained from each participant. They were informed that participation was voluntary and that confidentially would be maintained. The chatbot, augmented reality, mobile shopping, online payments, and customer evaluations are the five factors under which the research examines the effects of technological advances. This study found a significant link between modern technology and purchases made through online stores. The findings also provide us with an understanding of how global economies work, how firms operate, and how customers shop around the globe. The supporting technologies of these trends e.g. chatbots, augmented reality, online payments, user reviews, and mobile shopping will certainly have a favorable effect on the E-commerce industry.

Role of Due Diligence in Combating Money Laundering in Lens of ESG: A Concept

This study focuses on the implementation of Customer Due Diligence (CDD) requirements under Section 16 of the Anti-Money Laundering Act (AMLA) in Reporting Institutions (RIs). While RIs have established policies and procedures to comply with AMLA and mitigate risks associated with money laundering, there needs to be more emphasis on gathering information to address Environmental, Social, and Governance (ESG) Factors. ESG concerns have become increasingly important in the financial sector, but many institutions need help integrating ESG considerations into their due diligence processes. This study explores the incorporation of ESG due diligence within CDD practices, as it is crucial for long-term value creation, investor returns, and mitigating ESG risks that might be exposed to money laundering. This research aims to identify and propose practical strategies for integrating ESG factors into the customer evaluation process of RIs OR investigate factors that influence the role of due diligence in combating money laundering through the lens of ESG. This paper examines factors that influence the function of due diligence in tackling money laundering through the lens of ESG.

Effects of perceived values on affective and conative attitudes in cashierless store services

Purpose Despite the growing number of cashierless stores, few studies have examined the factors that influence the success of such stores. This paper aims to identify the influence of store attributes in customers’ affective attitudes regarding cashierless stores and to understand how customers’ need for interaction and risk reduction affect the relationship between customer experiences and evaluation. Design/methodology/approach Quantitative research is conducted using a partial least squares structural equation model. Data was collected from 174 customers with cashierless store experience. Findings The results identify the influence of hedonic and utilitarian characteristics on affective attitudes and the impact of attitudes on behavioral intentions. Also, multigroup analysis reveals that hedonic features are stronger indicators of customer attitudes among those with a low need for interaction, whereas utilitarian attributes are more important among customers with a low need for risk reduction. Customers’ affective attitudes are stronger influences on behavioral intentions among members of customer groups with a low need for both interaction and risk reduction. Originality/value To add to the limited research in customers’ experience with unstaffed stores, this study provides useful insights to achieve sustainable growth in the retailing context. Managerial considerations suggest that operators of cashierless stores should design store environments to guarantee customers’ purchasing and recommendation intentions.

Development of OpenAI API Based Chatbot to Improve User Interaction on the JBMS Website

This study presents an innovative chatbot, powered by OpenAI API, designed to enhance the user experience on the Journal of Business, Management, and Social Studies (JBMS) website. Chatbots have gained prominence for improving online interactions and information retrieval. The chatbot's development followed a structured prototype methodology, including Requirement Gathering, Prototype Building, Requirement Refinement, Customer Evaluation, and Design and Implementation. User Acceptance Testing (UAT) scored an average of 4.14, signifying high user satisfaction. UAT results showed positive user experiences and satisfaction with the chatbot. Integration of OpenAI API improved information extraction from journal articles and personalized article recommendations. Stakeholder feedback from JBMS's CEO, students, and UMN lecturers affirmed high satisfaction levels. Future research will refine the chatbot's features to align better with user needs, solidifying its role as an innovative tool for information retrieval within JBMS, and enhancing user service.

Examining authenticity on digital touchpoint: a thematic and bibliometric review of 15 years' literature

PurposeThe role of customers has moved from reactive to proactive, and they require more control over digital touchpoints. The quest for authenticity is their response to the dark side of interactive marketing – forms of faking, manipulation and exploitation. Authenticity becomes a key topic in interactive marketing as it reflects how customers assess digital touchpoints. However, there is a lack of comprehensive knowledge of authenticity in the interactive environment.Design/methodology/approachThis article consolidates the authenticity studies in various digital touchpoints using the entity-referent correspondence framework. This research employs bibliometric analysis and thematic analysis of 103 articles in the last 15 years.FindingsFive research clusters are identified: (1) human, (2) brand-generated content, (3) user-generated content, (4) branded platforms and (5) new technologies-based touchpoints (artificial intelligence, augmented reality and virtual reality). Most interactive marketing studies focus on human and content authenticity, and new technologies-based touchpoints lack comprehensive conceptualization. The review synthesizes the types of authenticity used in each touchpoint and highlights the importance of true-to-creator-self and true-to-customer-self in customer evaluation. We further propose a research framework with four antecedent groups and outcomes.Practical implicationsOur research supports managers by highlighting the type of authenticity prioritized in each touchpoint's development.Originality/valueTo answer the call from interactive marketing researchers, this research highlights the distinct definitions of authenticity at various digital touchpoints rather than looking at the overall brand. Trends, gaps and future research agenda of the authenticity concept in technology adoption and customer behavior are discussed.

LSTM and Bidirectional GRU Comparison for Text Classification

Although the phrases machine learning and AI are frequently used interchangeably and are frequently discussed together, they do not have the same meanings. While all artificial intelligence (AI) is machine learning, not all AI is machine learning, which is a key distinction. In the beginning, machine learning and natural language processing (NLP) are related since machine learning is frequently employed as a tool for NLP tasks. The advantage of NLP is that it can perform analysis, and examine a lot of data, including comments on social media accounts and hundreds of online customer evaluations. Text classification is essentially what needs to be done. This study compares Bidirectional GRU and LSTM as text classification algorithms using 20,000 newsgroup documents from 20 newsgroups from The UCI KDD Archive.
 After using the suggested model, we compare it to the long short-term memory and bidirectional GRU models for accuracy and validation. The results of the two comparisons show that the bidirectional GRU model performs better than the long short-term memory model. And this is a successful classification of text using a deep learning algorithm that uses a bidirectional GRU.

Is tipping just a game? Applying game theory to restaurant tipping behavior

The practice of voluntary tipping continues to be the most common compensation method for restaurant front-of-house service staff in the United States. Many previous studies have aimed to understand the outcome of the service exchange relationship, the tip, using only customer evaluations of salient variables. This approach marginalizes the role of the service provider to merely a measure of service quality and not an economic agent who is equally invested in the outcome of the relationship. This study sought to include the customer and the server's perspectives using a game-theoretic model to conceptualize the service exchange relationship more holistically. This study aimed to determine the salient factors in the server and customer's relationship that led to a favorable outcome in the form of a tip for the server. Convenience sampling was used to collect data from restaurant customers and servers. A game-theoretic model was created using customer evaluations of salient variables grounded by server-generated cut-off values. AIC model selection was used to determine the model that balanced information loss with information gain. Finally, logistic regression was used to map the utility-maximizing optimal strategy for a server in the game-theoretic model. Results show that the outcome will be desirable when service quality is perceived as good, the customer is a regular and dines with fewer than seven people.

EVALUATION OF LOW ATTENDANCE OF MEMBERS DURING WEEKLY GROUP MEETINGS AT PERMODALAN NASIONAL MADANI (PNM) MEKAAR SOLO

This research evaluated the low attendance of members during weekly group meetings at Permodalan Nasional Madani (PNM) Mekaar in Solo. This research aims to determine the factors that inhibit group members from attending weekly group meetings and identify policies taken by PNM Mekaar to ensure weekly group meetings stay active. Purposive sampling was used to select the research subjects, who must have joined PNM Mekaar at least since 2020, be registered as customers at PNM Mekaar, be registered as employees at PNM Mekaar for at least six months, and have agreed to participate in the research. Based on the findings, internal and external factors contributed to the problem of group members who had to attend weekly group meetings. These internal factors included less friendly account officers, no ceremonial weekly group meetings, weekly group meetings starting to focus solely on instalments, overworked account officers, new account officers still in training, high member turnover within three months, senior account officers and account officers not outlining key points of the cooperation agreement, and PT PNM Mekaar delegating the responsibility of customer education to the group leader. Meanwhile, other external factors, which include customers’ busy schedules and the laziness of some customers after years of paying instalments, prevent group members from attending weekly group meetings. PNM Mekaar's policy of delegating customer evaluation to its officers allows the weekly group meetings to operate as usual.

TRIPADVISOR REVIEWS OF GUEST SATISFACTION IN CONRAD MANILA AS QUARANTINE FACILITY 2020-2021: NARRATIVE ANALYSIS

The research paper utilized customer evaluation and feedback, particularly those found on TripAdvisor, the research discusses the satisfaction of prior quarantine guests of Conrad Hotel Manila. Researchers gathered the information for the study to determine the service quality that satisfied and dissatisfied the guests during their quarantine stay. The research gathered reviews from 2020–2021 in order to analyze and evaluate the following service quality variables including Tangibles, Reliability, Responsiveness, Assurance, and Empathy. To determine the distributions of each review to the service quality variable, the researchers applied narrative analysis to narrate the reviews under each variable, while quantitative research is used to count the total number of reviews under each service quality. The results of the quantitative analysis show that the service quality attribute that satisfied the guests is the reliability variable for the reason that a significant number of previous quarantine guests indicated that Conrad hotel has well-trained staff and excellent food, this was a total of 84 out of 315 positive statements. On the other hand, the majority of the negative reviews are related to the tangible variable with a total of 7 out of 20 negative statements that dissatisfied the guests during their quarantine period.

Big Data and AI-Driven Product Design: A Survey

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

Who’s asking? Social platform and research sponsors’ identity biases in guests’ hospitality experience-reviews: a case study of Spanish hotels

ABSTRACT The study here assesses the systematic variability of service performance assessments in customer reviews placed in alternative online social media platforms in the travel industry as well as responses to the firm’s requests for customer evaluations. The research includes an empirical study conducted by a national (Spanish) hotel chain of nine corporately-owned properties (hotels). Data-analyses were performed of data collected for the nine hotels from TripAdvisor and Booking.com, as well as internal data from the hotel chain’s customer data files. The findings support the general conclusion that hotel reviews on different social media platforms vary in systematic ways. TripAdvisor reviews consistently include the largest share of detractors across all social media platforms in this study, ranging from 11 to 51%. This study contributes useful benchmarks for the relative differences in performances in customer experience assessments across different social media platforms.

Asymmetrical Influences of Service Robots’ Perceived Performance on Overall Customer Satisfaction: An Empirical Investigation Leveraging Online Reviews

Service scholars seem to have empirically overlooked the impact of service robots in the overall customer evaluation of tourism services. This study addresses this gap by leveraging three-factor theory and electronic Word-Of-Mouth data to assess human-robot interaction’ influence on customer satisfaction. Text analytics are deployed alongside a penalty-reward contrast technique on almost 70,000 online reviews spanning 44 hotels worldwide that incorporated service robots into their operations. Customer satisfaction with hospitality services is significantly increased by positive service robots’ performance, while no significant effect is associated with negative service robots’ performance. The traveler type does not moderate the relationship between service robots’ performance and customer satisfaction. These findings, confirmed through Propensity Score Matching, reveal that service robots constitute an “excitement factor” in hospitality service offerings, thus providing a strong incentive for their integration into the workforce. Policymakers are urged to proactively facilitate the transition to a more automated service economy.

The Impact of Fashion x Culinary Co-Branding on Customer Evaluation

Existing literature has extensively explored the influence of co-branding on customer evaluation, particularly concerning variables such as brand personality, product fit, brand fit, brand attachment, and brand awareness. However, a research gap in implementing co-branding exists in the specific context of fashion x culinary. Consequently, this study aims to investigate the impact of these variables on customer evaluation of fashion x culinary co-branding products. To achieve this, a quantitative research methodology is employed, involving collecting data from 498 cases, which is subsequently analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings reveal a significant influence of fashion x culinary co-branding on customer evaluation. Notably, the indirect effect of perceived co-brand personality on customer evaluation, mediated by brand awareness, emerges as the most influential factor. These findings contribute to the existing body of research on co-branding and brand alliances by offering new insights and recommendations that have the potential to enhance industry practices.

Market segmentation based on customer experience dimensions extracted from online reviews using data mining

PurposeThis study aims to develop a practical and effective approach for market segmentation using customer experience dimensions derived from online reviews.Design/methodology/approachThe research investigates over 6,500 customer evaluations of food establishments on Taiwan’s Yelp platform through the Latent Dirichlet allocation (LDA) data mining approach. By using the LDA-derived experience dimensions, cluster analysis discloses market segments. Subsequently, sentiment analysis is used to scrutinize the emotional scores of each segment.FindingsMining online review data helps discern divergent and new customer experience dimensions and sheds light on the divergent preferences among identified customer segments concerning these dimensions. Moreover, the polarity of sentiments expressed by consumers varies across such segments.Research limitations/implicationsAnalyzing customer attributes extracted from online reviews for segmentation can enhance comprehension of customers’ needs. Further, using sentiment analysis and attributes of online reviews result in rich profiling of the identified segments, revealing gaps and opportunities for marketers.Originality/valueThis research presents a new approach to segmentation, which surmounts the restrictions of segmentation methods dependent on survey-based information. It contributes to the field and provides a valuable means for conducting customer-focused market segmentation. Furthermore, the suggested methodology is transferable across different sectors and not reliant on particular data sources, creating possibilities in diverse scenarios.

Neural networks for inline segmentation of image data in punching processes

Punching is a process that is sensitive to a multitude of parameters. The estimation of part and punch quality is often based on expert knowledge and trial-and-error methods, mostly carried out as a separate offline process analysis. In a previous study, we developed an optical inline monitoring system with subsequent image processing which showed promising results in terms of capturing every manufactured part, but was limited by slow image processing. Here, we present a more efficient image processing technique based on neural networks. For our approach, we manually identify the burnish parts in images based on criteria established via an expert survey in order to generate a training dataset. We then employ a combination of region-based and boundary-based losses to optimize the neural network towards a segmentation of the burnish surface which allows for an accurate measurement of the burnish height. The hyperparameter optimization is based on custom evaluation metrics that reflect the requirements of the burnish surface identification problem as well. After comparing different neural network architectures, we focus on optimizing the backbone of the UNet++ structure for our task. The promising results demonstrate that neural networks are indeed capable of an inline segmentation that can be used for measuring the burnish surface of punching parts.

Understanding Antecedents That Affect Customer Evaluations of Head-Mounted Display VR Devices through Text Mining and Deep Neural Network

Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis.

Pengaruh Fitur Customer Review dan Persepsi Risiko Terhadap Keputusan Pembelian pada Pengguna Shopee (Studi pada Mahasiswa Universitas Bandar Lampung Prodi Manajemen Angkatan 2019)

This study aims to analyze the impact of two independent variables, customer evaluation and perceived risk, on Shopee users' purchasing decisions. These are factors that deserve attention in the digital age because they have great potential to influence consumer behavior when shopping on the Shopee platform. Research subjects include classmates from the Faculty of Economics of Bandar Lampung University, Class of 2019. The research sample used is 107 respondents and the sampling is done according to Slovin's formula to ensure the level of study. representation of the data. Collected data were analyzed by the method of coefficient of determination analysis and tested by t-test and F-test to test the research hypothesis. Research results show that customer reviews and perceived risk have a significant influence on the purchasing decision of Shopee users. These results provide valuable information for sellers on the Shopee platform so that they can better understand the importance of product quality assessments from customers and the perceived risk. influence consumers' purchasing decisions. While the results of this study provide relevant insights, the study also recognizes that there are other factors that can influence shopping behavior at Shopee. Therefore, other variables should be included in further research for a more comprehensive understanding of consumer buying behavior in the e-commerce environment.

A Study on the Smart City Project Evaluation Method based on Demand for Proptech Technology using Fuzzy Measurement

This study established an evaluation system for preliminary evaluation of projects combined with smart city based on demand for proptech technology, and suggested a customized evaluation method that can be used for future regional projects. As the main result, first, the evaluation system was established by dividing it into three areas for the establishment of the evaluation system: proptech technology field, smart city living lab field, and smart city business plan field. In addition, the analysis results for this were presented as 0.609 for the internal evaluation field in the proptech technology field, 0.420 for the evaluation field of participation factors in the smart city living lab field, and 0.410 for sustainability in the smart city business planning field. Finally, a customized evaluation system was proposed with an emphasis on methodological application and provision of alternatives, and its utilization method was verified.

Customer responses to service providers’ touch: A meta-analysis

Touch is a critical part of many consumer experiences (e.g., shaking hands with a salesperson or a calming touch from a healthcare provider). In this paper, we synthesize the existing research on service providers’ initiated touch during their interactions with customers. We use meta-analysis to estimate the effect of touch across eight outcome categories relating to customer behavior and evaluations. Our analysis includes 55 effects extracted from 37 studies published from 1979 to 2019 with a total of 6,323 subjects. We use between-study characteristics to explore potential moderators of the touch effect. Overall, we find service providers’ touch to have a moderately strong and positive effect on customers. The touch effect appears to be significantly stronger in service settings than retail settings and is robust across a variety of between-study factors.

An integrated Multi-Criteria Decision Making and Multi-Choice Conic Goal Programming approach for customer evaluation and manager assignment

Companies adopt a customer-centric approach to maintain their competitive position or dominate the market. In this context, managers are pivotal in navigating the competitive markets in accomplishing these goals. Setting targets and conducting performance evaluations are vital for managers’ long-term success. This study aims to optimise customer evaluation and manager assignment in the defence industry. An integrated approach utilising the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to conduct a comprehensive market analysis and conduct this evaluation. A bi-objective integer programming model is formulated to maximise​ total integrated scores and potential returns. The efficient number of visits is determined by combining Multi-Criteria Decision Making (MCDM) and Multi-Choice Conic Goal Programming (MCCGP). This study offers an objective alternative to traditional survey-based evaluations by proposing a novel decision support system for Goal-Based Performance Appraisals (GBPA) of managers. The proposed approach yields performance values that are considerably more reliable than questionnaire results. Moreover, the proposed decision support system can effectively benefit employees across diverse sectors, extending beyond the confines of the defence industry.