Like Talking To a Wall : a Study of Multinational Customers\u27 Online Shopping Experiences

Using online reviews of multinational customers, we explored various attributes of their experiences when they engage in online shopping. Our analysis revealed eight key themes: interaction with customer service, paid subscription service experience, experience with e-retailers, delivery service experience, return policy experience, cost experience, product quality experience, and convenience in online shopping. We discuss these themes and how the experiences of customers in different regions compare based on their unique cultural factors and the level of economic development. The study contributes to our knowledge of online service experience and e-retail services

The disinformation pandemic : understanding, identification, and mitigation in COVID-19 era

In 2020 during COVID-19, in addition to the spread of coronavirus disease, we also observed a pandemic of disinformation about the disease. This pandemic of disinformation became known as Infodemic in the medical world. Just as coronavirus was infecting our bodies, Infodemic was infecting our information ecosystem and exasperating the fight against the COVID-19 pandemic. Disinformation can be produced by various sources including scientists, media personalities, and others and it can be disseminated by news media, webpages, and social media from one source to another. Additionally, disinformation can spread easily from web media to social media where it can spread even faster to a wider audience. Therefore, it is important that disinformation be detected before it has a chance to spread. However, the identification of disinformation is fraught with several challenges. This fact highlights the importance of studying and identifying disinformation both in the content of web pages and social media posts before it is allowed to spread. In this dissertation, I pursued a three-essay approach to understand, identify, and mitigate the disinformation pandemic. While manual fact-checking is difficult, time-consuming, and expensive, various automated detection solutions could speed up this process. Therefore, in my first essay, I explored whether Machine Learning (ML) techniques can be used to develop predictive models for automatic identification of disinformation. Computational linguistics methods are used to extract content-based, and sentiment-based features of selected webpage’ articles to construct our study dataset. This dataset is used to train various ML algorithms to develop predictive models to identify disinformation. The results showed that there are significant differences among features of true and false information that can be used to identify disinformation. Since the spread of disinformation happens both on media pages and on social media platforms, it is important to analyze disinformation at both levels. Moreover, the literature shows that disinformation spreads six times faster than true information on social media, demonstrating that users get more engaged with disinformation. Therefore, I extended my research to enhance the understanding of disinformation detection based on content-based features and its impact on users’ engagement in social media posts. The findings of the second essay highlighted the critical role of linguistic structure, emotional tone, and the psychological load of social media posts on users’ engagement that can be used to differentiate information from disinformation. The results of the first two essays confirmed that negative emotional tone was one of the most important factors in disinformation posts and was associated with a high engagement score. So, in the third essay, I explore the impact of negative emotional tones in developing users’ perceptions regarding the accuracy of the content. Three separate experiments were developed to explore this. The results of experiments in the third essay highlighted the significant role of negative emotional tones on the believability of the content and their potential influence on behavioral change. My research findings allow for a better understanding and identification of disinformation by highlighting and identifying content-based features that are meant to mislead users to falsely perceive disinformation as information

Degradation Behavior of Polypropylene during Reprocessing and Its Biocomposites: Thermal and Oxidative Degradation Kinetics

Non-isothermal thermogravimetric analysis (TGA) was employed to investigate the degradation of polypropylene (PP) during simulated product manufacturing in a secondary process and wood–plastic composites. Multiple batch mixing cycles were carried out to mimic the actual recycling. Kissinger–Akahira–Sunose (KAS), Ozawa–Flynn–Wall (OFW), Friedman, Kissinger and Augis models were employed to calculate the apparent activation energy (Ea). Experimental investigation using TGA indicated that the thermograms of PP recyclates shifted to lower temperatures, revealing the presence of an accelerated degradation process induced by the formation of radicals during chain scission. Reprocessing for five cycles led to roughly a 35% reduction in ultimate mixing torque, and a more than 400% increase in the melt flow rate of PP. Ea increased with the extent of degradation (α), and the dependency intensified with the reprocessing cycles. In biocomposites, despite the detectable degradation steps of wood and PP in thermal degradation, a partial coincidence of degradation was observed under air. Deconvolution was employed to separate the overlapped cellulose and PP peaks. Under nitrogen, OFW estimations for the deconvoluted PP exposed an upward shift of Ea at the whole range of α due to the high thermal absorbance of the wood chars. Under air, the Ea of deconvoluted PP showed an irregular rise in the initial steps, which could be related to the high volume of evolved volatiles from the wood reducing the oxygen diffusion

Estimation of the Stress Intensity Factors for Surface Cracks in Spherical Electrode Particles Subject to Phase Separation

Experiments have frequently shown that phase separation in lithium-ion battery electrodes could lead to the formation of mechanical defects, hence causing capacity fading. The purpose of the present work has been to examine stress intensity factors for pre-existing surface cracks in spherical electrode particles during electrochemical deintercalation cycling using both analytical and numerical methods. To this end, we make use of a phase field model to examine the time-dependent evolution of the concentration and stress profiles in a phase separating spherical electrode particles. By using a geometrical approximation scheme proposed in the literature, stress intensity factors at the deepest point of the pre-existing surface cracks of semi-elliptical geometry are calculated with the aid of the well-established weight function method of fracture mechanics. By taking advantage of a sharp-interphase core-shell model, an analytical solution for the maximum stress intensity factors arising at the deepest point of the surface cracks during a complete deintercalation half-cycle is also developed. Numerical results for evolution of the concentration profile and the distribution of the hoop stresses in the particle are presented; further, the stress intensity factors found numerically based on the phase field model are compared with those predicted by the analytical core-shell model. The results of the numerical model suggest that the maximum stress intensity factor could significantly vary with changes in the surface flux, increasing potentially by a factor of two within the range of parameters considered here, when the concentration difference between the two phases is decreased

Competing effects of current density and viscoplastic deformation on the critical conditions for dendrite growth into solid-state lithium battery electrolytes

All-solid-state lithium (Li) batteries provide a promising pathway toward high energy and power density. Dendrite penetration through the solid electrolyte causing battery short-circuit, however, persists to be one of the challenges impeding their widespread application. Here, considering a pre-existing surface crack in the electrolyte initially filled with an infinitely thin layer of Li, and assuming Li deposit to behave in accordance with rigid-viscoplasticity, we seek for the steady state Li-filled crack opening profile that could potentially form at a given constant current density. Treating the chemical potential of Li ions in the electrolyte and the electric potential to be uniform along the crack face, the model accounts for the coupling between stress buildup in the dendrite, deposition rate, viscoplastic flow of Li deposit, and crack opening induced by electrolyte deformation using singular integral equations of fracture mechanics. The model establishes limiting conditions for crack growth before a steady state dendrite is reached, triggering a cycle of crack growth and dendrite elongation. Using material properties adopted from literature, the model predicts that the critical condition can be met for a microcrack at typical current densities. The effect of pressure applied to the cell is further discussed

Effect of the simultaneous curing and foaming kinetics on the morphology development of polyisoprene closed cell foams

Pianki o zamkniętych komórkach oparte na kauczuku poliizoprenowym (IR) wytworzono przez formowanie tłoczne z zastosowaniem azodikarbonamidu (ADC), jako chemicznego poroforu. Zbadano wpływ temperatury przetwarzania na rozkład ADC oraz na wulkanizację IR z użyciem ADC i bez ADC, w celu określenia wpływu tego parametru na końcową morfologię pianki i właściwości mechaniczne. Badanie kinetyczne wykazało, że do interpretacji danych eksperymentalnych odpowiedni jest model autokatalityczny. Stwierdzono, że energia aktywacji rozkładu ADC (Ea = 181,8 kJ/mol) jest znacznie wyższa niż wulkanizacji IR bez ADC (Ea = 79,6 kJ/mol) lub z ADC (Ea = 72,3 kJ/mol) Wynika z tego, że wraz ze wzrostem temperatury, szybkość rozkładu ADC wzrasta bardziej niż szybkość wulkanizacji kauczuku, więc należy przeprowadzić optymalizację procesu. Zwiększenie temperatury ze 140 do 150°C zmniejszyło średni rozmiar komórek z 355 do 290 μm, zwiększając jednocześnie gęstość komórek z 73 do 118 komórek/mm3. Dalszy wzrost temperatury doprowadził jednak, ze względu na równowagę pomiędzy koalescencją komórek a sieciowaniem, do zwiększenia rozmiaru komórek oraz niższej gęstości komórek. Dla zoptymalizowanej temperatury (150°C) pianki miały najwyższy moduł sprężystości przy ściskaniu oraz twardość.Closed cell foams based on polyisoprene rubber (IR) were produced via compression molding using azodicarbonamide (ADC) as a chemical blowing agent. The effect of processing temperature on ADC decomposition, as well as IR curing with and without ADC were studied to determine the effect of this parameter on the final foam morphology and mechanical properties. The kinetic study showed that the autocatalytic model is appropriate to represent the experimental data. The activation energy for ADC decomposition (Ea = 181.8 kJ/mol) was found to be much higher than for IR curing without (Ea = 79.6 kJ/mol) or with (Ea = 72.3 kJ/mol) ADC. This indicates that with increasing temperature the rate of ADC decomposition accelerates faster than rubber vulcanization, so an optimization must be performed. For example, increasing the temperature from 140 to 150°C decreased the average cell size from 355 to 290 μm while increased the cell density from 73 to 118 cell/mm3. But further temperature increase led to larger cell size and lower cell density because of a balance between cell coalescence and crosslinking. For the optimized temperature (150°C), the foams had the highest modulus of elasticity and hardness

Sharing Economy: Application of Structural Topic Models

The sharing economy is known as collaborative consumption or the peer-to-peer based activity of acquiring, providing, or sharing goods and services. To improve the consumer-based sharing economy, researchers study customer reviews about their experiences of provided services. Expectation-confirmation theory (ECT) suggests that customers use mental comparison standards to evaluate the real performance of provided services, which ultimately influences customer satisfaction. Recommendations made in customer reviews have emerged as a critical feature of a business-to-consumer website, however there is a lack of empirical evidence supporting their influence on customer satisfaction. In this study, we contribute to the sharing economy knowledge base by adding a consumer recommendation component to the original EC