Information effect in Social Commerce: A case of TicketMonster

Social Commerce sites are in vogue enough to be recognized as a new trend in online shopping arena. Social Commerce can be defined as the electronic commerce triggered by social media. It has been growing very rapidly with enormous discount rate, quality services and precise information. This research analyzes effects of posted numeric information on daily sales volumes. Hetrosckedasticity arises with the real transaction data that was acquired from TicketMonster which is one of the biggest Social Commerce sites in Korea. Therefore, GLS model was applied to have results that original price, discounted price, minimum quantity to have discounted price, and maximum units of sales are statistically significant. Minimum quantity of sales to meet the requirement to have discounted prices has threshold effect on the purchase of consumers like the ways they have group buying on the Internet. However, additional studies are required to identify if this correlated information can be results of reasonable estimates by the vendors and the intermediary or play a role of signal to attract sales. More research opportunities are addressed on services types, consumer groups and information richness

Against Inefficacy Objections: The Real Economic Impact of Individual Consumer Choices on Animal Agriculture

When consumers choose to abstain from purchasing meat, they face some uncertainty about whether their decisions will have an impact on the number of animals raised and killed. Consequentialists have argued that this uncertainty should not dissuade consumers from a vegetarian diet because the “expected” impact, or average impact, will be predictable. Recently, however, critics have argued that the expected marginal impact of a consumer change is likely to be much smaller or more radically unpredictable than previously thought. This objection to the consequentialist case for vegetarianism is known as the “causal inefficacy” (or “causal impotence”) objection. In this paper, we argue that the inefficacy objection fails. First, we summarize the contours of the objection and the standard “expected impact” response to it. Second, we examine and rebut two contemporary attempts (by Mark Budolfson and Ted Warfield) to defeat the expected impact reply through alleged demonstrations of the inefficacy of abstaining from meat consumption. Third, we argue that there are good reasons to believe that single individual consumers—not just individual consumers taken as an aggregate—really do make a positive difference when they choose to abstain from meat consumption. Our case rests on three economic observations: (i) animal producers operate in a highly competitive environment, (ii) complex supply chains efficiently communicate some information about product demand, and (iii) consumers of plant-based meat alternatives have positive consumption spillover effects on other consumers

Measuring compulsive buying behaviour: Psychometric validity of three different scales and prevalence in the general population and in shopping centres

Due to the problems of measurement and the lack of nationally representative data, the extent of compulsive buying behaviour (CBB) is relatively unknown. Methods: The validity of three different instruments was tested: Edwards Compulsive Buying Scale (ECBS; Edwards, 1993), Questionnaire About Buying Behavior (QABB; Lejoyeux & Adès, 1994) and Richmond Compulsive Buying Scale (RCBS; Ridgway, et. al., 2008) using two independent samples. One was nationally representative of the Hungarian population (N=2710) while the other comprised shopping mall customers (N=1447). Results: A new, four-factor solution for the ECBS was developed (ECBS-R), and confirmed the other two measures. Additionally, cut-off scores were defined for all measures. Results showed that the prevalence of CBB is 1.85% (with QABB) in the general population but significantly higher in shopping mall customers (8.7% with ECBS-R, 13.3% with QABB and 2.5% with RCBS-R). Conclusions: Due to the diversity of content, each measure identifies a somewhat different CBB group

The Dynamics of Viral Marketing

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a 'long tail' where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product and pricing categories for which viral marketing seems to be very effective

Price Sensitivity of Demand for Prescription Drugs: Exploiting a Regression Kink Design

This paper investigates price sensitivity of demand for prescription drugs using drug purchase records for at 20% random sample of the Danish population. We identify price responsiveness by exploiting exogenous variation in prices caused by kinked reimbursement schemes and implement a regression kink design. Thus, within a unifying framework we uncover price sensitivity for different subpopulations and types of drugs. The results suggest low average price responsiveness with corresponding price elasticities ranging from -0.08 to -0.25, implying that demand is inelastic. Individuals with lower education and income are, however, more responsive to the price. Also, essential drugs that prevent deterioration in health and prolong life have lower associated average price sensitivity.Prescription drugs; price; reimbursement schemes; regression kink design

Impulsivity Relates to Relative Preservation of Mesolimbic Connectivity in Patients with Parkinson Disease.

IntroductionThe relationship between Parkinson Disease (PD) pathology, dopamine replacement therapy (DRT), and impulse control disorder (ICD) development is still incompletely understood. Given the sensorimotor-lateral substantia nigra (SN) selective degeneration associated with PD, we posit that a relative sparing of the limbic-medial SN in the context of DRT drives impulsive, reward-seeking behavior in PD patients with recent history of severe impulsivity.MethodsImpulsive and control participants were selected from a consecutive list of PD patients receiving pre-operative deep brain stimulation (DBS) planning scans including 3T structural MRI and 64 direction diffusion tensor imaging (DTI). Using previously identified substantia nigra (SN) subsegment network connectivity profiles to develop classification targets, split-hemisphere target-based SN segmentation with probabilistic tractography was performed. The relative subsegment volumes and strength of connectivity between the SN and the limbic, associative, and motor network targets were compared.ResultsOur results show that there is greater probability of connectivity between the SN and limbic network targets relative to motor and associative network targets in PD patients with recent history of severe impulsivity as compared to PD patients without impulsivity (P = 0.0075). We did not observe relative volumetric subsegment differences across groups.ConclusionFirstly, our results suggest that fine-grained, atlas-derived classification targets may be used in PD to parcellate and classify functionally distinct subsegments of the SN, with the apparent preservation of previously reported topographical limbic-medial SN, associative-ventral SN, and sensorimotor-lateral SN orientation. We suggest that relative, as opposed to absolute, degeneration amongst SN-associated dopaminergic networks relates to the impulsivity phenotype in PD

Detecting Singleton Review Spammers Using Semantic Similarity

Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).Comment: 6 pages, WWW 201