Online purchasing, and hence e-commerce packaging production and use, have grown steadily in recent years, and so has their environmental impact as a result. This paper reviews the evolution of packaging over the last century through a compilation of scientific literature on e-commerce packaging focusing on its environmental side. The primary aims were to identify research gaps in e-commerce packaging and to propose new research lines aimed at reducing its environmental impact. A systematic search of abstracts was conducted to identify articles dealing with sustainability in e-commerce packaging in order to better understand changes in materials and formats, identify problems such as oversizing and allow prospective readers to become acquainted with the latest innovations in materials, sustainability and logistics. Based on existing research, packaging materials and technology evolved rapidly until the 1990s. Later, however, it has become increasingly difficult to further reduce their cost and environmental impact. Also, some packaging products continue to be made from non-renewable materials and thus restrict growth of e-commerce. Further research is needed with a view to producing new packages from renewable sources such as cellulose-containing materials, which are widely available in nature, or from recycled cellulose-based materials such as cartonboard. Improving distribution processes with new, more effective tools could additionally help alleviate the environmental impact of packaging. Similarly, new production processes such as additive manufacturing and 3D printing might help optimize package volume and shape, thereby facilitating more sustainable production through, for example, reduced CO2 emissions. Currently available technology can be useful to rethink the whole e-commerce packaging paradigm, which has changed very little over the past few decades.

The emergence of e-commerce and express delivery services has significantly transformed business operations and consumer shopping experience. However, the resulting problem of packaging waste, particularly from overpackaging, poses serious challenges to environmental sustainability and human health. Existing research has proposed many solutions from various perspectives, but very few have considered the acceptability and consumer preference for these proposals. Using the value co-creation (VCC) theory, we established a research model to explore consumer preferences for e-commerce overpackaging solutions. A survey of 632 online consumers in Guangzhou and Shenzhen was conducted, and data were analyzed using the SmartPLS software. The results show that establishing a recycling system, government policy, and consumers' environmental awareness have a significant positive impact on consumer preference, while combined packaging has a significant negative impact. We also found that government policy plays an intermediary role in establishing a recycling system and consumer preference. Based on these findings, we recommend that enterprises establish and improve their packaging recycling systems and that e-commerce platforms provide alternative options to combined packaging. Also, the government should play a guiding and coordinating role for enterprises and consumers, and environmental awareness among consumers should actively be promoted.

Persuasive strategies are used to influence the behavior or attitude of people without coercion and are commonly used in online systems such as e-commerce systems. However, in order to make persuasive strategies more effective, research suggests that they should be tailored to groups of similar individuals. Research in the traits that are effective in tailoring or personalizing persuasive strategies is an ongoing research area. In the present study, we propose the use of shoppers' online shopping motivation in tailoring six commonly used influence strategies: scarcity, authority, consensus, liking, reciprocity, and commitment. We aim to identify how these influence strategies can be tailored or personalized to e-commerce shoppers based on the online consumers' motivation when shopping. To achieve this, a research model was developed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) and tested by conducting a study of 226 online shoppers. The result of our structural model suggests that persuasive strategies can influence e-commerce shoppers in various ways depending on the shopping motivation of the shopper. Balanced buyers-the shoppers who typically plan their shopping ahead and are influenced by the desire to search for information online-have the strongest influence on commitment strategy and have insignificant effects on the other strategies. Convenience shoppers-those motivated to shop online because of convenience-have the strongest influence on scarcity, while store-oriented shoppers-those who are motivated by the need for social interaction and immediate possession of goods-have the strongest influence on consensus. Variety seekers-consumers who are motivated to shop online because of the opportunity to search through a variety of products and brands, on the other hand, have the strongest influence on authority.

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers' opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.

In recent years, secondhand products have received widespread attention, which has raised interest in them. The susceptibility issues that consumers encounter while buying online products in reference to the display images of the products are also not well researched.

Poverty is a problem that occurs in many countries, notably in Indonesia. The common methods used to obtain poverty information are surveys and censuses. However, this process takes a long time and uses a lot of human resources. On the other hand, governments and policymakers need a faster approach to know social-economic conditions for area development plans. Hence, in this paper, we develop e-commerce data and machine learning algorithms as a proxy for poverty levels that can provide faster information than surveys or censuses. The e-commerce dataset is used and this high-dimensional data becomes a challenge. Hence, feature selection algorithms are employed to determine the best features before building a machine learning model. Furthermore, three machine learning algorithms such as support vector regression, linear regression, and k-nearest neighbor are compared to predict the poverty rate. Hence, the contribution of this paper is to propose the combination of statistical-based feature selection and machine learning algorithms to predict the poverty rate based on e-commerce data. According to the experimental results, the combination of f-score feature selection and support vector regression surpasses other methods. It shows that e-commerce data and machine learning algorithms can be potentially used as a proxy for predicting poverty.

This research investigates the cost-result relationship of the Inbound Marketing actions used by grocery e-commerce. The analysis is based on the application of the Dorfman and Steiner (1954) model for optimal advertising budget, which is adapted by the authors to digital marketing and verified with empirical statistical analysis. Considering 29 leading companies in six countries over a time horizon of six years, an analysis of the mix of SEO and SEM techniques aimed at the attraction and conversion of Internet users to the web pages of their companies is carried out. The results confirm that e-commerce is optimizing Digital Inbound Marketing in line with the established model. Differences are identified depending on the type of format (pure player versus brick and mortar) and at the country level (UK and USA versus others).

Little is known about the global bat souvenir trade despite previous research efforts into bat harvest for bushmeat. We screened eBay listings of bats in Australia, Canada, Italy, Switzerland, United Kingdom and USA to assess the nature and extent of the online offers. A total of 237 listings were retrieved in between the 11th and 25th of May 2020 with a median price per item of US$38.50 (range: US$8.50-2,500.00). Items on offer were mostly taxidermy (61.2%) or skull (21.1%) specimens. Overall, 32 different species of bat were advertised, most of which (n = 28) are listed as "Least Concern" on the International Union for Conservation of Nature (IUCN) Red List. One species (Nycteris javanica) is classified as "Vulnerable" and one (Eidolon helvum) as "Near Threatened." Pteropus spp. specimens were the most expensive specimens on offer and the conservations status of these species may range from "Critically Endangered" to "Data Deficient" by IUCN and the entire genus is listed in the Appendix II by the Convention on the International Trade in Endangered Species of Wild Fauna and Flora (CITES). However, the exact species concerned, and their respective conservation status, could not be confirmed based on the listings' photos. The sourcing of bat was restricted to mostly South-East Asian countries (a third of items sourced from Indonesia) and to two African countries. Our survey revealed that the online offer of bat products is diverse, abundant, and facilitated by worldwide sellers although most offered bats species are from South-East Asia. With a few exceptions, the species on offer were of little present conservation concern, however, many unknowns remain on the potential animal welfare, biosecurity, legal implications, and most importantly public health risks associated with this dark trade.

E-commerce saw a paradigm shift during COVID. Consumers turned to online shopping when pandemic lockdowns caused brick-and-mortar stores to shut for extended periods. Although the pandemic drove more buyers online, it had negative impacts that affected e-commerce performance. This study assesses both positive and negative impacts and their relative significance. The findings are then used to prioritize different strategies for e-commerce development in four vibrant Middle Eastern economies: United Arab Emirates, Saudi Arabia, Qatar, and Kuwait. The study employs a hybrid approach incorporating grey analytical hierarchy process (GAHP) and grey relational analysis (GRA). The GAHP evaluates the relative significance of impacts, whereas the GRA ranks the strategies. The study is based on the responses from 36 local e-commerce specialists. The findings revealed that the supply chain disruption was a rather significant factor, and that "expanding supplier base" was a top-ranked strategy. The study suggests that increasing market share of e-commerce will necessitate the improvement of the supply chains, including the expansion of the supply base, as well as the establishment of sustainable supply chains. In addition to that, the moment has come to implement meaningful changes, such as digital transformation of supply chains, in order to fulfil customer expectations.

Grounded on the American Customer Satisfaction Framework (ACSF) of Bryant (1995), the European Customer Satisfaction Framework Model (ECSF) of Anderson and Fornell (2000), and Eleven-factor Customer Satisfaction Model of Hokanson (1995), this study develops reliable and valid five constructs of customer satisfaction theoretical model and build up a questionnaire in the unique context of Beauty and Cosmetic Online Shopping in the Vietnamese market. More specifically, we identified five main constructs including 5 implementation constructs (i.e., online shopping experience, customer service, external incentives, security/privacy, and personal characteristics), and one outcome construct (i.e., customer satisfaction). A detailed questionnaire was then developed with items for these constructs along with the questions on the personal characteristics of the respondents. The questionnaire was sent to randomly consumers via online channels including Facebook. The target participants are Vietnamese people who have experience in shopping online products like beauty and cosmetics. Based on 334 full responses have been received, we use Exploratory Factor Analysis (EFA) to test for 167 observations to ensure that all items in each scale reflected sufficiently the scope of each construct. This technique helps to identify the underlying dimensions of consumer satisfaction. Furthermore, we also employ Confirmatory Factor Analysis (CFA) to validate the constructs and items in questionnaire. To do this, we have used the sample of 167 responses. Finally, together with results from SEM models, our study contributes to providing a reliable and valid questionnaire which fully reflect for our self-constructed theoretical model of five constructs including Online shopping experience (OSE), External incentives (EI), Seller service (SS), and Security/privacy (SP), as well as personal characteristics (PC).

The severe situation caused by THE COVID-19 epidemic has not only hindered the steady development of social economy, but also had a great impact on the development of e-commerce logistics. For e-commerce enterprises, logistics cost is an important factor that affects the operation effect and consumer experience. Based on this, this study proposes cost control methods for e-commerce logistics in the prevention and control of COVID-19 environment. In this study, based on the actual environment of COVID-19 prevention and control, the logistics cost algorithm during the epidemic period is designed on the basis of the analysis of the influencing factors of e-commerce logistics cost, and the cross-border logistics strategy that conforms to the background of COVID-19 prevention and control and the demand of e-commerce logistics cost control is developed to better reduce the operating cost of logistics enterprises. The e-commerce logistics cost control method proposed in this article is effective in the prevention and control of new crown pneumonia, and the overall actual cost is within the budgeted cost range. The experimental results prove that the e-commerce logistics cost control method designed in this paper can help e-commerce companies achieve good economic benefits and proves that it has higher application advantages.

Over the past 50 years, there has been a tremendous expansion in the measurement of chemical contaminants in environmental media. But how many chemicals have actually been determined, and do they represent a significant fraction of substances in commerce or of chemicals of concern? To address these questions, we conducted a bibliometric survey to identify what individual chemicals have been determined in environmental media and their trends over the past 50 years. The CAplus database of CAS, a Division of the American Chemical Society, was searched for indexing roles "analytical study" and "pollutant" yielding a final list of 19,776 CAS Registry Numbers (CASRNs). That list was then used to link the CASRNs to biological studies, yielding a data set of 9.251 × 106 total counts of the CASRNs over a 55 year period. About 14,150 CASRNs were substances on various priority lists or their close analogs and transformation products. The top 100 most reported CASRNs accounted for 34% of the data set, confirming previous studies showing a significant bias toward repeated measurements of the same substances due to regulatory needs and the challenges of determining new, previously unmeasured, compounds. Substances listed in the industrial chemical inventories of Europe, China, and the United States accounted for only about 5% of measured substances. However, pharmaceuticals and current use pesticides were widely measured accounting for 50-60% of total CASRN counts for the period 2000-2015.

Humans can change their behaviors to obtain environmental rewards (e.g., money, food, and sex). However, our knowledge regarding how rewards affect human behaviors by priming and whether there are differences among types of rewards is limited. This study focused on whether monetary and social rewards have different priming effects on product rating decisions in e-commerce by using a behavioral experiment and event-related potentials (ERPs). Using cash/discount coupons as a monetary reward and greeting cards as a social reward, the behavioral data showed that unsatisfactory products with a monetary reward induced a less negative consumer attitude than those with a social reward or no reward; additionally, such products were associated with a longer reaction time while rating products than those with a social reward, reflecting that monetary rewards made it more difficult for the subjects to rate unsatisfactory products than social rewards. The P2, N2, and P3 components of the ERP data were evaluated. Unsatisfactory products caused negative emotion, which could be compensated more by the monetary reward than the social reward as reflected by a smaller P2 amplitude. Due to the compensation effect of the monetary reward, unsatisfactory products were associated with more decision conflict than the social reward as reflected by a more negative N2 amplitude, which is consistent with the behavioral results. However, in the subsequent controlled process, regardless of whether the products were satisfactory or unsatisfactory, the monetary reward caused more attention reallocation and was more motivating than the social reward as reflected by a larger P3 component. These findings have implications for the marketing strategy of online sellers and value of online reviews and suggest attaching importance to ethical issues induced by monetary rewards in rating behaviors.

The dataset contains full-page screenshots of homepages of commercial banking (N = 1033), online shopping (N = 1064), and university (N = 1059) websites, as well as the raw and aggregated user ratings of webpage design prototypicality, visual aesthetics, perceived usability and trustworthiness, and user demographic information. Design prototypicality was measured with three items, including typicality, exemplar goodness, and family resemblance, whereas the other design dimensions were measured with a single item each. Amazon Mechanical Turk crowdworkers (N = 3319 rating sessions) provided their demographic data and rated the homepages online. The demographic data have been anonymized, with generated unique participant IDs replacing MTurk crowdworker IDs. The screenshots are identified with generated IDs to provide partial anonymization for the websites, limiting their potential misuse outside design-related or user experience-related academic research. The raw rating data contain all collected ratings, whereas the aggregated data contain the per-webpage, per-dimension ratings derived solely from the ratings of study-compliant crowdworkers. The non-compliance among crowdworkers was detected based on several indicators, including rate-rerate consistency, seen-unseen webpage recognition, free-form feedback analyses, demographic data analyses, and other indicators. Future research could utilize the dataset either in user studies that require full-page webpages as stimuli, e.g., studies on the determinants of first impression, user preference, and user experience, or in computational research on web design, including computational aesthetics, as this type of research requires a large number of user-rated webpages, which this dataset provides.

Online shopping has various advantages, such as convenience, easy access to information, a greater variety of products or services, discounts, and lower prices. However, the absence of salespeople's personalized assistance decreases the online customer experience. Business-to-consumer e-commerce companies are increasingly implementing online shopping assistants (OSAs), interactive and automated tools used to assist customers without salespeople's assistance. However, no comprehensive model of OSA acceptance in e-commerce exists, including constructs from multiple information system disciplines, sociopsychology, and information security. This study aims to fill these gaps by empirically investigating consumers' intention to accept OSAs from a functional, social, relational, and security perspective. It identifies OSA acceptance factors in e-commerce through an extensive literature review and expert opinion. A research model is proposed after identifying structural relationships among the study's variables from the literature. The study employs partial least squares-structural equation modeling (PLS-SEM) to validate the proposed model empirically. The results indicate that anthropomorphism, attitude, ease of use, enjoyment, privacy, trust, and usefulness are crucial determinants of acceptance variables. There are significant moderating effects of respondents' gender and education on OSA acceptance. The study's results have substantial implications for academia, extending and validating the Technology Acceptance Model (TAM) for OSA acceptance in e-commerce. The study will help e-commerce marketers develop optimal adoption strategies when implementing OSAs on social media platforms.

In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer's purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies.

The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2 ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors.

Background: Electronic cigarettes (e-cigarettes) have been increasingly advertised and marketed in China in recent years. This study examined the practice and impact of e-cigarette online marketing on a major retail website-Tmall.com. Methods: Data were obtained by crawling 449 online pages of e-cigarette marketing. Content analysis was conducted to summarize the marketing practices for four types of e-cigarettes, and multilevel modeling (MLM) was implemented to explore factors predictive of the online sales of the products. Results: The sales volume of e-cigarettes ranged from 0 to 28,169, with the price per item varying from RMB 218.1 ($31.84) to RMB 385.5 ($56.29). Fruit (44.3%, n = 199), mint (33%, n = 148) and cream/sugar/ice (29.4%, n = 132) were the three flavors most often listed for sale online. Moreover, 63.4% (n = 285) of e-cigarette ads emphasized the role of the products as an aid to quit smoking. Nice taste (75.1%), big vapor (65.7%), high capacity batteries (67.9%), fashionable models (61.3%), discounted price (49.7%), and suitability for gifting (45.9%) were the most frequently touted product features in online ads. Type of e-cigarettes, diversity of products, number of online comments, and location of manufacturers were significantly associated with sales volume. Conclusions: Online marketing of e-cigarettes was common on one of China's leading e-commerce websites. Sellers employed advertising strategies targeting a wide range of potential consumers-from youth to the elderly. Stricter regulations of online marketing for e-cigarettes should be enforced in China.

Background: It is crucial for companies to understanding users' choices and learning behaviors, and the corresponding influencing factors and cognitive patterns regarding social network services to communicate with potential customers. Methods: In this study, a casual structural model was constructed and developed to model and characterize the relationships between problems to be resolved as antecedent variables and success factors as consequent variables with the intermediary variables based on human learning behaviors, whereas the concept of social network service was introduced to summarize the current issues of social network services and empirically factors affecting effectiveness of social network services. Discussion: This study highlighted the corporate need to examine the intelligent role and learning effectiveness of social network services when studying social creativity and intelligence in a social networking environment. Firstly, the framework and hypotheses of social network services were introduced to summarize the current issues of social network services and the main influencing factors affecting the working patterns of social network services. Subsequently, the empirically established model was further tested to explore the possible meaningful relationships among those variables used. Results: The study revealed that the social network services provider and the customer should improve their social creativity and community collaboration; these could be expanded and enhanced by increasing the social intelligence to raise the social network services' effect and the customer's externalization. Furthermore, social intelligence, community collaboration, and customer externalization were factors significantly influencing customers' social creativity, while the customer externalization and community collaboration were the two important factors affecting social intelligence. Conclusion: The study implied that social network services providers should provide more and more intelligent and inspiring services for their customers.

Prenatal chemical exposures can influence maternal and child health; however, few industrial chemicals are routinely biomonitored. We assessed an extensive panel of contemporary and emerging chemicals in 171 pregnant women across the United States (U.S.) and Puerto Rico in the Environmental influences on Child Health Outcomes (ECHO) Program. We simultaneously measured urinary concentrations of 89 analytes (103 total chemicals representing 73 parent compounds) in nine chemical groups: bactericides, benzophenones, bisphenols, fungicides and herbicides, insecticides, organophosphate esters (OPEs), parabens, phthalates/alternative plasticizers, and polycyclic aromatic hydrocarbons (PAHs). We estimated associations of creatinine-adjusted concentrations with sociodemographic and specimen characteristics. Among our diverse prenatal population (60% non-Hispanic Black or Hispanic), we detected 73 of 89 analytes in ≥1 participant and 36 in >50% of participants. Five analytes not currently included in the U.S. biomonitoring were detected in ≥90% of samples: benzophenone-1, thiamethoxam, mono-2-(propyl-6-carboxy-hexyl) phthalate, monocarboxy isooctyl phthalate, and monohydroxy-iso-decyl phthalate. Many analyte concentrations were higher among women of Hispanic ethnicity compared to those of non-Hispanic White women. Concentrations of certain chemicals decreased with the calendar year, whereas concentrations of their replacements increased. Our largest study to date identified widespread exposures to prevalent and understudied chemicals in a diverse sample of pregnant women in the U.S.