Good or Better; The Effect of Comparative Mindset with Recommended Products on Product Evaluation and Purchase Decision

Recommender system elicits the interest of users and makes recommendations to assist product search and evaluation (Xiao & Benbasat, 2007). Recommender system is an effective marketing tool by generating 35 percent of revenue on Amazon and 75 percent of selection on Netflix (Mackanzie, Meyer, & Noble, 2013). In order to understand how recommender systems affects consumer decision making, this study investigates the effect of comparative vs. decision-only mindsets on product evaluation and purchase decision. A 2 (Comparative vs. Decision only mindset) X 2 (Hedonic vs. Utilitarian product) between-subjects experiment was conducted. The findings suggest that comparing with alternatives alters the value perception and decision even though the actual value of the product remains the same. Value perception mediates effects of mindset on preference and purchase. The result provides implications that recommender system can leverage consumers to perceive relative value and promote purchase decision. Online retailers can boost sales by making comparison easier between products and convincing customers of selecting a better option

Effects of Perceived Integration Quality and Attitude toward Information Seeking on Perceived Shopping Value in Omni-channel Shopping Experience

Omni-channel shopping experience involves not only simultaneous use of multiple shopping channels but the integrated connection of them (Lazaris & Vrechopoulos, 2014). Despite the importance of the seamless connection between channels in omni-channel shopping, little empirical study tested effects of the integration of more than two channels to date. Thus, this study investigated how perceived integration quality, perceived brand innovativeness, and attitudes toward information seeking, influence shopping values with Structural Equation Modeling analysis. As proposed, integration quality and individual\u27s information seeking tendency was positively associated with shopping value perception but there showed no relationship between perceived brand innovativeness and utilitarian shopping value. The findings imply providing a seamless experience for consumers with information seeking tendency can be extremely important because they are more motivated to use multiple channels concurrently. It also suggests enhanced brand innovativeness perception is a symbolic quality, which increases hedonic shopping value but not utilitarian shopping value

Energy-efficient CO2 hydrogenation with fast response using photoexcitation of CO2 adsorbed on metal catalysts.

Many heterogeneous catalytic reactions occur at high temperatures, which may cause large energy costs, poor safety, and thermal degradation of catalysts. Here, we propose a light-assisted surface reaction, which catalyze the surface reaction using both light and heat as an energy source. Conventional metal catalysts such as ruthenium, rhodium, platinum, nickel, and copper were tested for CO2 hydrogenation, and ruthenium showed the most distinct change upon light irradiation. CO2 was strongly adsorbed onto ruthenium surface, forming hybrid orbitals. The band gap energy was reduced significantly upon hybridization, enhancing CO2 dissociation. The light-assisted CO2 hydrogenation used only 37% of the total energy with which the CO2 hydrogenation occurred using only thermal energy. The CO2 conversion could be turned on and off completely with a response time of only 3 min, whereas conventional thermal reaction required hours. These unique features can be potentially used for on-demand fuel production with minimal energy input

Investigating the Asymmetric Behavior of Oil Price Volatility Using Support Vector Regression

This paper investigates the asymmetric behavior of oil price volatility using different types of Asymmetric Power ARCH (APARCH) model. We compare the estimation and forecasting performance of the models estimated from the maximum likelihood estimation (MLE) method and support vector machine (SVM) based regressions. Combining nonparametric SVM method with parametric APARCH model not only enables to keep interpretations of the parametric models but also leads to more precise estimation and forecasting results. Daily or weekly oil price volatility is investigated from March 8, 1991 to September 13, 2019. This whole sample period is split into four sub-periods based on the occurrence of certain economic events, and we examine whether the asymmetric behavior of the volatility exists in each sub-period. Our results indicate that SVM regression generally outperforms the other method with lower estimation and forecasting errors, and it is more robust to the choice of different APARCH models than the MLE counterparts are. Besides, the estimation results of the SVM based regressions in each sub-period show that the ARCH models with asymmetric power generally perform better than the models with symmetric power when the data sub-period includes large swings in oil price. The asymmetric behavior of oil price volatility, however, is not detected when the analysis is done using the whole sample period. This result underscores the importance of identifying the dynamics of the dataset in different periods to improve estimation and forecasting performance in modelling oil price volatility. This paper, therefore, examines volatility behavior of oil price with both methodological and economic underpinnings.publishedVersio

Behavior-based anomaly detection on big data

Recently, cyber-targeted attacks such as APT (Advanced Persistent Threat) are rapidly growing as a social and national threat. It is an intelligent cyber-attack that infiltrates the target organization and enterprise clandestinely using various methods and causes considerable damage by making a final attack after long-term and through preparations. These attacks are threatening cyber worlds such as Internet by infecting and attacking the devices on this environment with the malicious code, and by destroying them or gaining their authorities. Detecting these attacks requires collecting and analysing data from various sources (network, host, security equipment, and devices) over the long haul. Therefore, we propose the method that can recognize the cyber-targeted attack and detect the abnormal behavior based on Big Data. The proposed approach analyses faster and precisely various logs and monitoring data using Big Data storage and processing technology. In particular, we evaluated that the suspicious behavior analysis using MapReduce is effective in analysing large-scale behavior monitoring and log data from various sources