Search Results (57)

E-commerce sites have generated large amounts of unstructured data as they allow millions of users to generate product reviews. Thus, although there have been significant improvements in the characteristics of big data, such as speed and volume, developing various analysis techniques to monitor, understand, and extract useful information from this web-based data has become challenging. This study aims to analyze cosmetic products on a Turkish-based e-commerce website with sentiment analysis and to create a new domain-specific Turkish sentiment dictionary model with manual labeling. In the study, a Turkish sentiment dictionary consisting of 65,378 words was created by manually labeling 875,455 product reviews for 24 cosmetic brands sold on the Turkey-based trendyol e-commerce site, and sentiment analysis was performed using this dictionary. The dataset, divided into seven product groups, was analyzed using K-NN, SVM, DT, RF, and LR algorithms to address three classification problems. The algorithms were evaluated with comparative analysis using accuracy, precision, recall, and f-1 score metrics. SVM gave the highest performance result with over 93% accuracy, 92% precision, 93% recall, and a 91% f-1 score in all product groups. The dictionary model created for the cosmetics industry in the study helps businesses and researchers to use their resources more efficiently and save time by performing fast and low-cost analyses on large datasets of product reviews. Moreover, by analyzing customer feedback, brands can offer long-lasting and environmentally friendly products that align with customers’ feelings. Thus, businesses have the opportunity to develop or improve products. Full article

Digital supply chains (DSCs) are value-driven and collaborative digital systems designed to generate business value for firms through various innovative technologies. Today, we are witnessing companies transitioning from traditional supply chain models to DSCs through digital technologies. The effective selection of digital suppliers during these digital transformation processes is a strategic research topic. Additionally, factors such as the proliferation of information and communication technologies, globalization, and the pandemic have contributed to the expansion of e-commerce platforms. In this rapid growth phase, identifying the right supplier is crucial for the success of e-commerce sites. This study aims to develop an innovative, integrated, and comprehensive decision-making methodology to assist e-commerce platforms in selecting appropriate suppliers for their DSCs. To achieve this, an extended fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method is tested, where criteria and alternative evaluations made by decision-makers (DMs) are characterized by interval-valued intuitionistic fuzzy numbers (IVIFNs). The proposed decision mechanism is tested on the DSS problem of an e-commerce platform specializing in household products. Findings of the application, which uses three experts’ opinion to evaluate four digital suppliers based on the seven criteria, are discussed to help e-commerce sites conduct the DSS process more effectively. Full article

This paper examines the role of digital economy tools, including big data, mobile applications, e-commerce, and sharing economy platforms, in the sustainable development of the tourism sector. The focus is on studying how the digital economy tools can contribute to more efficient and sustainable tourism services, to service quality improvement, to reducing the negative environmental impact, and thus increase the availability of tourism resources in local destinations. Using the example of the successful use of digital technologies in Russian regions, this paper discusses the introduction of online platforms for booking services, the use of mobile applications for navigation and obtaining information about tourist sites, as well as the use of digital tools for predicting consumer preferences. A systematic approach to the analysis of tourism services digitalization, based on a set of technical and functional–digital indicators, allowed us to evaluate the impact of the digitalization level on the local destination’s sustainable development in transition economy conditions. The proposed methodology for assessing and applying tourism services digitalization tools in Russian regions takes into account the transition economy specifics and aims to promote more sustainable practices. This study will add to the existing literature by defining both technical and functional criteria for the implementation of digital technologies as tools for the creation of new business models in tourism, and the development of a tourism services digitalization model, based on the assessment of the regional digitalization level, to ensure the movement towards achieving sustainable development goals in local destinations. Full article

The advent of social commerce platforms fueled by the growing commercialization of social media and networking sites represents a significant evolution in e-commerce dynamics. This study investigates the pivotal role of key opinion leaders (KOLs), particularly YouTubers, in shaping consumer purchasing behavior. Recognizing the powerful influence exerted by KOLs, we examined their ability to promote product diffusion through credibility, specialized knowledge, and strategic word-of-mouth campaigns. This study employs a robust theoretical framework that foregrounds the influence of KOLs while integrating critical constructs, such as perceived value and risk, into a comprehensive model. Our empirical analysis, based on data from 411 valid responses, yields the following insights: the expertise and renown of KOLs exert a profound effect on consumer purchase intentions; consumer perceptions of value positively correlate with trust, whereas perceived risk negatively affects it; and trust mediates the relationship between KOL characteristics (popularity and professionalism) and consumers’ relationship strength with purchase intentions. The findings advocate leveraging KOLs’ renown and expertise while mitigating perceived risks to amplify consumer purchase intentions, thus providing actionable strategies for marketers in the burgeoning social commerce landscape. Full article

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition between a service provider and a client in mobile–mobile interaction is not trivial. This is because of not only the avoidance of face-to-face communication due to safety and health concerns but also the difficulty of matching up the online user using mobiles with the real person in the physical world. So, a novel mutual recognition scheme for mobile–mobile interaction is highly necessary. This paper comes up with a novel cyber-physical secure communication scheme relying on the digital twin paradigm. The proposed scheme designs the digital twin networking architecture on which real-world users form digital twins as their own online abstraction, and the digital twins authenticate each other for a smart service interaction. Thus, inter-twin communication (ITC) could support secure mutual recognition in mobile–mobile interaction. Such cyber-physical authentication (CPA) with the ITC is built on the dynamic BLE beaconing scheme with accurate proximity detection and dynamic identifier (ID) allocation. To achieve high accuracy in proximity detection, the proposed scheme is conducted using a wide variety of data pre-processing algorithms, machine learning technologies, and ensemble techniques. A location-dependent ID exploited in the CPA is dynamically generated by the physical user for their own digital twin per each mobile service. Full article

The objective of this article is to recommend products using association rule mining from an E-commerce site. This helps us to recommend products through utilizing the filtering concept. In this article, we use the Apriori and FP-Growth algorithms. Our model not only suggests products but also gives tips on how to make strong suggestion systems that can deal with a lot of data and give quick responses. Our objective is to predict ratings so that the users could be recommended and buy products. There are 1,048,100 records in the dataset. This dataset consists of four features, and these are are follows: {user-id, productid, Ratings, and timing}. Here, we consider the rating as our dependent attribute, and others factors are independent features. In this article, we use collaborative filtering algorithms (SVD, SVD+, and ALS) and also item-based filtering techniques (KNNBasic) to recommend products. Apart from these, sssociation rule mining, hybridization of Apriori, and FP-Growth are used. K-means clustering is used to identify anomalies as well as to create a dashboard, using Power BI for data visualization. Apart from these, we have also developed a hybridization algorithm using Apriori and FP-Growth. Among all the recommendation algorithms, SVD outperforms in recommending the product, and the average RMSE and MAE values are 1.31, and 1.04, respectively. Full article

Digital innovations have significantly transformed the marketing landscape, with visual technology solutions having become mainstream in the fashion industry approximately a decade ago. Digital technology offers a range of benefits to online fashion retailers, enhancing their online shopping platforms with augmented reality features that allow customers to “try on” products digitally before making a purchase. This research aims to explore the key factors influencing the use of augmented reality applications and e-commerce sites for purchasing apparel. A qualitative study was conducted to examine the visual experience and usage of augmented reality applications among young customers. The findings highlight the most relevant factors in the online fashion purchasing process, the visual experience, and the potential future use of augmented reality applications in fashion product purchasing. These insights are crucial for developing effective marketing strategies and communication messages. Full article

Brand reputation directly influences customer trust and decision-making. A good reputation can lead to greater customer loyalty, commitment, and advocacy. This study aims to understand the effects of brand reputation on customer trust and loyalty and to determine how brands can protect their reputation. This study, which was conducted on the iPhone 11 sample by obtaining statistical data from customer reviews, can be adapted and used by researchers and companies that want to measure brand reputation. In this study, customer reviews for the iPhone 11 phone on the Trendyol e-commerce site, the largest e-commerce platform in Turkey, are analyzed using sentiment analysis and machine learning methods. While 85 percent of customers are satisfied with the iPhone 11, 13 percent are dissatisfied with it. The neutral comment rate of 2 percent indicates that some customers do not express a clear positive or negative opinion about the product. In the comments of customers who bought the iPhone 11, there are those who express satisfaction with the quality, technical features, performance, and price/performance ratio of the product, as well as those who express significant complaints about delivery, quality, price, and customer service. Neutral comments generally focus on the product itself, price, quality, shipping, and packaging, and make informative evaluations. A sustainable reputation is based on the extent to which an organization embraces ethical principles, social responsibility, and sustainable practices throughout its operations and business relationships. Brands can improve, protect, and increase their brand reputation by considering and analyzing the thoughts and feelings of their customers. For this, they should develop policies and strategies to reinforce their strong features and improve their faulty and deficient features. Full article

Sustainable development is growing in importance in today’s climate crisis. With the percentage of sales via digital channels increasing annually and consumers becoming aware of the environmental impact of their choices, a huge opportunity presents itself for promoting sustainable goods online if designers can find an effective way to raise awareness in consumers. Using a simulated e-commerce site (webshop), we investigated whether the presence or absence of sustainability labels displayed next to product images influenced users’ product selections. There was a significant association between the presentation of sustainability labels and the number of selected sustainable products. Overall, participants were familiar with sustainability labels and indicated willingness to pay ‘extra’ for sustainable products, while there was more variation in the way they felt that sustainability labels influenced their product choices. The findings highlight the complexities of factors influencing purchasing decisions and the need for more design-inspired research in this area. Whilst user interface design may be an effective means to influence sustainable product choices, design should also enable consumers to make informed product choices, while still providing a ‘fair’ e-commerce environment. Full article

As a product of the development of e-commerce over a specific period of time, the “new retail model” breaks the barriers between the traditional retail industry and e-commerce. Supported by Internet technology, it builds a new business model of “physical store + e-commerce + logistics” through the integration of online, offline, and logistics, which also leads to a great change in consumer behavior. Therefore, in order to meet consumer demand and achieve the long-term development of shopping malls, while taking into account the fair allocation of urban space resources, the indicators and methods of shopping mall site selection evaluation in the new retail era will be significantly different from traditional shopping mall site selection decisions. In this paper, the Wuhan East Lake Hi-Tech Zone is selected as the research object, and a comprehensive AHP-GIS assessment model is proposed. By investigating the impact of consumers’ behavioral changes on shopping mall location in the new retail era, a suitability evaluation system containing eight evaluation indicators is constructed, and the weights of each factor are determined using hierarchical analysis. At the same time, GIS is used to process the spatial analysis of the indicators, and combined with the weights of the factors, superposition analysis and quantitative research are carried out. Finally, based on the correlation analysis between ratings and customer flow, the suitability evaluation results are further supported in order to provide a more objective and scientific basis for the location of shopping malls from the perspective of the change in consumer behavior under the new retail model, and to put forward universal suggestions for the construction and development of shopping malls in the future. Full article

As the largest coastal city in China, Shanghai’s rapid development in transportation, tourism, trade, and commerce has facilitated the spread and invasion of non-native aquatic organisms. Aquatic organisms are highly elusive, and once established, eradicating them becomes a challenging task. Currently, our understanding of the invasion risk posed by non-native aquatic species in Shanghai is limited. Therefore, it is imperative to investigate the pathways of introduction, distribution, and dispersion and the invasion risk and impacts of non-native aquatic organisms in Shanghai. This study investigated aquatic organisms in Shanghai’s primary water bodies, including Huangpu River, Suzhou River, and Dianshan Lake. The risk assessment was conducted using the Aquatic Species Invasiveness Screening Kit (AS-ISK), and field monitoring was performed with environmental DNA (eDNA) technology. Results of the risk assessment indicate that among the 21 evaluated species, 9 fall into the medium-to-high-risk category with scores ≥26, while 12 are classified as low-risk with scores <26. The top four species with the highest invasion risk are Gambusia affinis, Pomacea canaliculata, Lepomis macrochirus, and Coptodon zillii. This study identified 54 fish species belonging to seven orders, 16 families, and 42 genera at 16 sampling sites in Shanghai, among which Channa maculata, Micropterus salmoides, and Misgurnus bipartitus are non-native. The results suggest that Shanghai faces a high invasion risk of aquatic species, necessitating enhanced scientific prevention and control measures. Early monitoring is essential for species with medium-to-high invasion risk, and a further evaluation and analysis of the risks associated with introduced fish species already present in Shanghai are recommended for aquaculture practices. Full article

Nowadays, e-commerce websites have become part of people’s daily lives; therefore, it has become necessary to seek help in assessing and improving the usability of the services of e-commerce websites. Essentially, usability studies offer significant information about users’ assessment and perceptions of satisfaction, effectiveness, and efficiency of online services. This research investigated the usability of two e-commerce web-sites in Saudi Arabia and compared the effectiveness of different behavioral measurement techniques, such as heuristic evaluation, usability testing, and eye-tracking. In particular, this research selected the Extra and Jarir e-commerce websites in Saudi Arabia based on a combined approach of criteria and ranking. This research followed an experimental approach in which both qualitative and quantitative approaches were employed to collect and analyze the data. Each of the behavioral measurement techniques identified usability issues ranging from cosmetic to catastrophic issues. It is worth mentioning that the heuristic evaluation by experts provided both the majority of the issues and identified the most severe usability issues compared to the number of issues identified by both usability testing and eye-tracking combined. Usability testing provided fewer problems, most of which had already been identified by the experts. Eye-tracking provided critical information regarding the page design and element placements and revealed certain user behavior patterns that indicated certain usability problems. Overall, the research findings appeared useful to user experience (UX) and user interface (UI) designers to consider the provided recommendations to enhance the usability of e-commerce websites. Full article

This paper presents an empirical analysis of e-commerce data obtained through Google Analytics (GA) from two small businesses’ perspectives: an IT components company and a tourism agency website located within the same county in Romania. The objective of our study is to examine the enduring effects of the COVID-19 pandemic and seasonal variations over the last four years. The data collection spanned from January 2019, predating the onset of the COVID-19 pandemic, until mid-February 2023. To facilitate our analysis, we categorize the GA metrics into groups that encompassed website performance, site accessibility, and user behavior for the IT company. As for the tourism agency, we focus on website accessibility, user behavior, and marketing campaigns. Our goal is to empirically group or associate GA metrics according to their intrinsic meaning and check if each group reflects a certain latent concept (such as user behavior or site accessibility). Furthermore, our study aims to formulate and test five hypotheses regarding the immediate and long-lasting impact of the COVID-19 pandemic on the operations of small businesses. Our contribution consists of formulating and verifying the five hypotheses by providing descriptive data from the results of the Pearson correlation test, empirically grouping the GA metrics and verifying whether they reflect certain latent factors or topics, interpreting the results from the application of the ANOVA technique and Scarpello’s adaptation of the one factor test, respectively. Full article

Large amounts of online reviews from e-commerce sites and social media platforms can help potential consumers to better understand products and play an important part in assisting potential consumers in making purchase decisions. Moreover, while multiple consumers purchase the same product, the index parameters of the product that are of concern among them are usually different, i.e., they have different expectations for the product. Therefore, the question of how to effectively analyze online product reviews and consider multiple consumers’ expectations to select products is an important issue that needs to be addressed. The objective of this study is to propose a product selection method based on intuitionistic fuzzy soft sets and TODIM. Firstly, the online reviews are extracted by the web crawler and are pretreated. Next, the sentiment orientations of each online review concerning product index parameters are recognized using the dictionary-based sentiment analysis algorithm. Then, the evaluation values of sentiment orientations for product index parameters are firstly expressed by intuitionistic fuzzy numbers and are then transformed into intuitionistic fuzzy soft sets. Further, the alternative product set is obtained according to the uni-int decision function and multiple consumers’ expectations, and we then rank the alternative products using the TODIM method. Finally, a case study is provided to illustrate the validity and feasibility of the proposed method. Full article

Due mainly to COVID-19 and the demanding work schedules of many individuals, online purchasing sites have become indispensable. However, the dynamic online environment and everchanging customer demands make sustainable competitiveness challenging for e-commerce platforms. Humans primarily influence the preference for online purchase platforms. This study aimed to discover Türkiye’s top popular online shopping sites by adopting an extended intuitionistic fuzzy ORESTE (Organisation, Rangement Et Synthèse De Données Relationnelles) approach. Our study targeted this by surveying female users of four online shopping platforms using IF-ORESTE. The criteria were determined according to customer preferences. These were as follows: easy accessibility to the platform, providing regular discounts and campaigns, advanced filtering settings, the contractual merchants’ reliability, quick delivery, being more affordable than competing platforms, positive feedback in user comments, having a large brand volume, having an installment option, and having partnered cargo companies. The least important factor was the large volume of brands on the online websites. Quick delivery of orders and positive feedback in reviews were equally important. Similarly, the decision-makers considered regular discounts and promotions and the comprehensive filtering settings as equally critical. However, these criteria were less significant than quick delivery and positive customer feedback. This work’s novelty lies in implementing the IF to the ORESTE in the Turkish e-commerce industry. The implications and future directions are discussed. Full article