Social Shopping: The Good, the Bad and the Ugly
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ItemA Real-Time Detection Algorithm for Identifying Shill Bidders in Multiple Online Auctions( 2018-01-03)Online auctions are highly susceptible to fraud. Shill bidding is where a seller introduces fake bids into an auction to drive up the final price. If the shill bidders are not detected in run-time, innocent bidders will have already been cheated by the time the auction ends. Therefore, it is necessary to detect shill bidders in real-time and take appropriate actions according to the fraud activities. This paper presents a real-time shill bidding detection algorithm to identify the presence of shill bidding in multiple online auctions. The algorithm provides each bidder a Live Shill Score (LSS) indicating the likelihood of their potential involvement in price inflating behavior. The LSS is calculated based on the bidding patterns over a live auction and past bidding history. We have tested our algorithm on data obtained from a series of realistic simulated auctions and also commercial online auctions. Experimental results show that the real-time detection algorithm is able to prune the search space required to detect which bidders are likely to be potential shill bidders.
ItemOnline Retailing Channel Addition: Risk Alleviation or Risk Maker?( 2018-01-03)The retailing industry traditionally considers the optimal products selection and pricing problem, a complex and challenging one, from marketing and consumer behavior's perspectives. In this study, we take a risk perspective and offer an alternative solution to tackling the problem, echoing the most recent literature that looks at non-risk aspects, such as expected consumer preference, market size and predicted profitability. Adopting a mean-variance framework, our approach explicitly takes into account the interconnectedness of retail products and their impact on risk at the portfolio (retailer) level. Extending the analysis to multiple-channel decisions, our results suggest that the introduction of a new retailing channel (e.g. online shops) can reduce the portfolio risk, whereas a lack of synergy between the new channel and the existing ones may lead to a negative impact on the overall performance. We also provide managerial implications on several conditions when retailers are more economically inclined to introduce more retail channels. Interestingly, our model indicates that larger retailers are less likely to expand their online platform.
ItemCarrot-or-Stick: How to Trigger the Digitalization of Local Owner Operated Retail Outlets?( 2018-01-03)Local Owner Operated Retail Outlets (LOOROs) are threatened in their very existence by the digitalization pressure from online and offline competitors on the one hand and by changing shopping habits of their customers on the other. Despite all digitally-enabled opportunities to regain competitive power, LOOROs still hesitate to adopt digital tools and applications. Politicians, city managers and retail lobbies seek for triggers to support the local structures and to push the digitalization efforts of local retailers. Building on AndreoniÂ´s "Carrot-and-Stick Approach", this study examines the impact of the availability of resources (carrot) and the perception of pressure (stick) on the use of digital applications among 223 owners of LOOROs from 26 cities in Germany. Our findings show that LOOROs are receptive for "Carrot-and-Stick". LOOROs seek for orientation while suffering under a shortage of time and capacities and seem to be disconnected from the development of their competi-tors and their customers.
ItemImpact of Mobile Electronic Word of Mouth (EWOM) on Consumers Purchase Intentions in the Fast-Causal Restaurant Industry in Indonesia( 2018-01-03)Latest media technology may shape consumers’ new motivations for disseminating word of mouth via mobile social networking sites (SNSs).This research aims to investigate the impact of social media marketing activities in terms of mobile eWOM on different dimensions of online CBBE and behavioral intentions towards online fast causal restaurant industry, using the S-O-R consumer response model. Participants (n= 351) resided in Indonesia and had prior experience of mobile application usage. SEM was used to analyze the data. The results indicate that mobile eWOM (stimulus) significantly influenced both consumer emotional, affective and cognitive responses. The emotional affective and cognitive responses significantly influenced behavioral responses. In addition, full mediation effects of CBBE were found between mobile eWOM and consumer response. Study findings emphasize the importance of examining consumer response in terms of mobile eWOM and CBBE. This study offers important theoretical and practical contributions to restaurant operators.
ItemAn Empirical Analysis of Repurchase Behavior in Mobile Commerce According to Different Mobile Channels( 2018-01-03)Smartphone-based m-commerce enables customers to purchase products in different channels. In the complicated mobile shopping channels, retaining existing customers becomes important. In light of these developments, this study focuses on crucial factors of repurchase behavior of consumers based on the recency, frequency, and monetary value (RFM) model and analyzes how their effects differ among mobile channels and the online channel. The mobile channels are divided into three channels: mobile application ("app"), mobile browser, and mobile shopping portal channels. Real purchase data for three million orders from online shopping sites is used for our empirical analysis. The results show that all RFM variables significantly affect repurchase behavior. Our findings imply that mobile app users are more likely to repurchase than users in other channels. It is also found that the frequency variable is more important for mobile channel users, while the recency variable is more important for online channel users.