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S1567422313001099 | Software-as-a-service (SaaS) allows customers to use software over the Internet by paying a subscription fee rather than by buying shrink-wrapped software and installing it on their computers. To maintain customer interests in SaaS, the provider’s dynamic quality decision is important. Thus, we consider a dynamic programming model that assumes demand is quality sensitive and influenced by customer perception. In both finite and infinite planning horizons, we show that a unique optimal policy exists for the SaaS provider to set quality periodically. We demonstrate that the SaaS provider may have a constrained opportunistic behavior towards its quality decisions when considering customer perception. This behavior results in a transient fluctuating quality decision path before it converges to a steady state. We find that the software’s initial quality plays a critical role in long term decisions, and that customers’ expectations of quality improvement positively affect the quality, although the actual improvement may not meet their expectations. | Dynamic quality decisions of software-as-a-service providers based on customer perception |
S1567422314000088 | In Web 2.0 environment, the influence of customers’ voices is increasing. Many companies have built their online brand communities for listening to the diverse voices of the customer (VOC) and promoting customer’s engagement. The information obtained from these brand communities is exploited for organizational innovation.However, the VOC with negative contents could possibly present threats to a firm in an online context. In this study, we develop a research model that includes the types of customer expectations, opinion leader engagement, negative valence of VOC, interaction, and innovation. Subsequently, we empirically validate the effects of customer expectations on the interactions among community members and organizational innovation by using a dataset from MyStarbucksIdea.com. The results show that the effects of VOC on the interaction within a brand community and organizational innovation are different depending on the types of expectation in the VOC. Opinion leaders’ engagement positively affects the interaction among community members.Moreover, the negative valence of VOC affects the relationships between customer expectations and interactions, and results in a possible threat within an online brand community.The research results give some insight into managing the brand community and analyzing the VOCs so as to achieve organizational innovation. | Gift or threat? An examination of voice of the customer: The case of MyStarbucksIdea.com |
S156742231400009X | To date, the utilitarian benefits of online consumption have only been partially investigated. This study undertakes an exhaustive approach to fully delimit the dimensional structure related to the utilitarian motivations for online consumption. First, an in-depth literature review is carried out, in order to allow the proposal of an aprioristic base structure of eleven categories of utilitarian motivations. Next, qualitative analyses (focus groups and personal interviews) are applied to assess and eventually refine the structure of utilitarian motivations proposed after the literature review, their labels and respective measurement scales. Finally, this qualitative phase concludes with ten motivational categories and 46 items. Then, quantitative analyses (exploratory and detailed confirmatory factor analyses) are applied, based on a questionnaire administered to a sample of 667 Internet users, to keep refining and to eventually validate both the dimensional structure of motivations and the related measurement scales. Finally, a structure of 9 utilitarian motivations (and corresponding set of 36 items) is established, with the following labels: assortment, economy, convenience, availability of information, adaptability/customization, desire for control, payment services, anonymity, and absence of social interaction. The nomological validity of this structure is satisfactorily tested using a second-order factor model. The article finishes by discussing some implications for practitioners. | Utilitarian motivations in online consumption: Dimensional structure and scales |
S1567422314000106 | Most search engines use the weighted Generalized Second Price (wGSP) auction to sell keyword-based text ads, generating billions of dollars of advertising revenue every year. Designing and implementing near-optimal reserve prices for these wGSP auctions are naturally important problems for both academia and industry. In this paper, we show how to calculate and implement the near-optimal reserve price of the wGSP mechanism in realistic settings. Unlike reserve prices in standard single-item auctions, optimal reserve prices in wGSP auctions are discriminatory, different even for advertisers bidding on the same keyword. The optimal reserve price results can be extended to support CPA/CPC/CPM 1 CPM: cost per mille (impressions), CPA: cost per action, CPC: cost per click. 1 hybrid auctions. Our simulations indicate that setting a proper reserve price will transfer some bidder utility (payoff) to auctioneer utility, resulting in higher revenue for the search engine. We describe a practical methodology to implement optimal reserve prices in production systems. | Optimal reserve prices in weighted GSP auctions |
S1567422314000179 | This study examined the behavior of online searchers in relation to keyword advertising according to the theory of advertising avoidance. A total of 451 volunteers were recruited for an experiment. A computer program and an eye-tracking device were used to measure the number of clicks and eye movements. The findings show that the number of clicks for to obtain organic search results is higher than that for keyword advertising. There is no difference in observation count between the area of keyword advertising placed above the organic search results and the area of the organic search results themselves. However, observation counts for the organic search results and keyword advertising placed above the organic search results are higher than for the keyword advertising placed at the right-hand side of the page. Searchers seem to spend the longest observation time on the organic search results, then on the keyword advertising placed above the organic search results, and finally on the keyword advertising placed at the right-hand side of the page. | Keyword advertising is not what you think: Clicking and eye movement behaviors on keyword advertising |
S1567422314000271 | Trust and distrust are both considered to be crucial in online truster–trustee relationships. Although some research has proposed that trust and distrust are distinct, other research continues to hold that they are merely opposite ends of the same continuum. Given this debate, it is important to consider how distrust is distinguished from trust. To that end, this paper extends the nomological network of distrust and introduces two novel antecedents never introduced in online behavior literature: situational abnormalities and suspicion. For this nomological network, we also propose that trust and distrust coexist in online e-commerce relationships and can result in ambivalence when they both have high attitudinal values (represented in emotions, beliefs, or behaviors). Using an empirical study of online consumer behavior with 521 experienced online consumers, we found strong empirical validation for our newly proposed model. We provide evidence that suspicion and situational abnormalities are separate, important antecedents to distrust. We also examine the effect of ambivalence on the truster’s intentions toward the website and find a small positive effect that increases the user’s intentions toward the website. Finally, we empirically demonstrate the coexistence of trust and distrust as separate constructs and emphasize that distrust has a much larger impact on the truster’s intentions than does trust. We conclude with implications for theory and practice, along with a discussion of the limitations of and future opportunities revealed by this study. | When trust and distrust collide online: The engenderment and role of consumer ambivalence in online consumer behavior |
S1567422314000283 | A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences. | Unanimously acceptable agreements for negotiation teams in unpredictable domains |
S1567422314000295 | With the rapid development of online social media, social networking services have become an important research area in recent years. In particular, microblogging as a new social media platform draws much attention from both researchers and practitioners. Although most current studies focus on the effect of social networks on the diffusion of services or information, most are descriptions or explanations of what has already happened. This study focuses on future activity by employing probability models such as the Pareto/NBD and BG/NBD models to predict user lifetime vitality. Three experiments were implemented to test the two models. Our results showed that both the Pareto/NBD model and the BG/NBD model were effective in predicting SNS user usage behavior on microblogging websites. It was found that tweeting behavior is more suitable for such probability models than retweeting behavior and user segmentation can improve prediction accuracy by distinguishing between currently active and inactive users. | Predicting microblog users’ lifetime activities – A user-based analysis |
S1567422314000301 | Viral marketing can be an effective marketing technique in social networks. Initiating from a set of influential seed users, it can activate a “chain-reaction” driven by word-of-mouth. The effectiveness of viral marketing lies in the fact that it conveys an implied endorsement from social ties. Existing approaches to selecting influential seeds depend on measures of global centrality within the structure of the social network – they select users that are central in the entire network according to some centrality measure (e.g., Eigenvector centrality). In this paper a new targeted approach to viral marketing is proposed that exploits prior knowledge about the potential market and uses local centrality scores to identify seeds that have high chances of reaching and activating many users in the potential market. The performance gained by the proposed approach is investigated with an experimental evaluation that uses data from real social networks. The results show that targeted approach outperforms existing, global centrality based methods. It is also shown that the relative performance of the targeted approach improves in the case where the majority of users are indifferent (or negative) to the viral marketing campaign. | A targeted approach to viral marketing |
S1567422314000313 | Most of the existing literature on CRM value chain creation has focused on the effect of customer satisfaction and customer loyalty on customer profitability. In contrast, little has been studied about the CRM value creation chain at individual customer level and the role of self-construal (i.e., independent self-construal and interdependent self-construal) in such a chain. This research aims to construct the chain from customer value to organization value (i.e., customer satisfaction→customer loyalty→patronage behavior) and investigate the moderating effect of self-construal. To test the hypotheses suggested by our conceptual framework, we collected 846 data points from China in the context of mobile data services. The results show that customer’s self-construal can moderate the relationship chain from customer satisfaction to customer loyalty to relationship maintenance and development. This implies firms should tailor their customer strategies based on different self-construal features. | How does customer self-construal moderate CRM value creation chain? |
S1567422314000325 | One-sided auctions are used for market clearing in the spot markets for perishable goods because production cost in spot markets is already “sunk.” Moreover, the promptness and simplicity of one-sided auctions are beneficial for trading in perishable goods. However, sellers cannot participate in the price-making process in these auctions. A standard double auction market collects bids from traders and matches the higher bids of buyers and lower bids of sellers to find the most efficient allocation, assuming that the value of unsold items remains unchanged. Nevertheless, in the market for perishable goods, sellers suffer a loss when they fail to sell their goods, because their salvage values are lost when the goods perish. To solve this problem, we investigate the suitable design of an online double auction for perishable goods, where bids arrive dynamically with their time limits. Our market mechanism aims at improving the profitability of traders by reducing trade failures in the face of uncertainty of incoming/departing bids. We develop a heuristic market mechanism with an allocation policy that prioritizes bids of traders based on their time-criticality, and evaluate its performance experimentally using multi-agent simulation. We find out that our market mechanism realizes efficient and fair allocations among traders with approximately truthful behavior in different market situations. | Online double auction mechanism for perishable goods |
S1567422314000337 | Assuring high quality of web services, especially regarding service reliability, performance and availability of e-commerce systems (unified under the term performability), has turned into an imperative of the contemporary way of doing business on the Internet. Recognizing the fact that customers’ online shopping behavior is largely affecting the conduct of e-commerce systems, the paper promotes a customer-centric, holistic approach: customers are identified as the most essential “subsystem” with a number of important, but less well-understood behavioral factors. The proposed taxonomy of customers and the specification of operational profiles is a basis to building predictive models, usable for evaluating a range of performability measures. The hierarchical composition of sub-models utilizes the semantic power of deterministic and stochastic Petri nets, in conjunction with discrete-event simulation. A handful of variables are identified in order to turn performability measures into business-oriented performance metrics, as a cornerstone for conducting relevant server sizing activities. | Behavioral-based performability modeling and evaluation of e-commerce systems |
S1567422314000349 | Display and search ads are the most popular Internet ad formats. Instead of being placed on search engine result pages, display ads are placed on webpages that include more actual content. In order to improve online contextual advertising, the effects of webpage content on embedded display ads must be understood. This study investigates how viewers’ attitudes toward content and the applicability of that content to the adjacent display ads impact the effectiveness of those ads. The moderating effects of viewers’ attention and need for cognition are also examined. The experimental results show that webpage content automatically activates ad evaluations, and that this effect increases when viewers pay less attention to the ad or have a high need for cognition. If the webpage content is highly applicable to the ad, improvements are seen in the attitude toward the ad and the attitude toward the brand. | The impact of context on display ad effectiveness: Automatic attitude activation and applicability |
S1567422314000350 | The buying and selling of goods and services are no longer limited to a general website or a physical store as social networks, such as Facebook or Pinterest, are heavily focusing on social commerce. Prior studies have analyzed impact of trust and culture on social commerce, design and interface aspects of it, and intention to use social commerce by general people. Our study is informed by the literature on information disclosure intention, and Communication Privacy Management theory and is motivated by the fundamental premise that intention to self-disclose in social commerce is affected by perceived ownership of information, privacy apathy, the risks and benefits of disclosure and fairness of information exchange. We analyzed data collected from 252 samples using the scenario method. The results show that shoppers’ information disclosure intention is driven by the fairness of information exchange, privacy benefits and privacy apathy. | Disclosing too much? Situational factors affecting information disclosure in social commerce environment |
S1567422314000465 | Modern online markets are becoming extremely dynamic, indirectly dictating the need for (semi-) autonomous approaches for constant monitoring and immediate action in order to satisfy one’s needs/preferences. In such open and versatile environments, software agents may be considered as a suitable metaphor for dealing with the increasing complexity of the problem. Additionally, trust and reputation have been recognized as key issues in online markets and many researchers have, in different perspectives, surveyed the related notions, mechanisms and models. Within the context of this work we present an adaptable, multivariate agent testbed for the simulation of open online markets and the study of various factors affecting the quality of the service consumed. This testbed, which we call Euphemus, is highly parameterized and can be easily customized to suit a particular application domain. It allows for building various market scenarios and analyzing interesting properties of e-commerce environments from a trust perspective. The architecture of Euphemus is presented and a number of well-known trust and reputation models are built with Euphemus, in order to show how the testbed can be used to apply and adapt models. Extensive experimentation has been performed in order to show how models behave in unreliable online markets, results are discussed and interesting conclusions are drawn. | A simulation testbed for analyzing trust and reputation mechanisms in unreliable online markets |
S1567422314000477 | With the popularity of online shopping, more and more consumers are making their decisions according to online reviews. However, studies on the effects of online scores produce different results, even for the same products. This study analyzes the impact of online scores from the perspective of the interaction between the different forms of online scores to provide a reasonable explanation of the inconsistency of the research results in this regard. The results of this study demonstrate that the effect of the overall score is negatively affected by the difference between the weighted score and the overall score itself. Moreover, it is also positively influenced by the difference between the overall score of one product and those of its substitutes. While considering the difference between the sub-dimensional scores and the overall score, its influence on the effect of the overall score is insignificant. This research theoretically validates the interactive effects of online scores and provides the basis for an online-score-based operation. | Research on the interactive effects of online scores |
S1567422314000489 | Google has been steadily increasing its market share in the US, although its main competitor, Yahoo, began developing a successful knowledge-sharing service in 2005. To verify whether a knowledge-sharing service may increase a search engine’s competitiveness, this study considers the competition between an inferior search engine that has an option of introducing a knowledge-sharing service and a superior search engine without this service. We specifically investigate the conditions under which it would be more profitable for the inferior search engine to introduce a knowledge-sharing service rather than increase its search quality. We show that the inferior search engine’s profit-maximizing strategy mainly depends on both the amount of information available on the Internet and the difference in search quality between it and the superior search engine. When the search quality difference is small, the inferior search engine should introduce a knowledge-sharing service keeping its answer database inaccessible to the superior search engine. When the search quality difference is large, the inferior search engine generally had better improve its search technology. We also show the inferior search engine’s market-share-maximizing strategy when it introduces a knowledge-sharing service. | Inferior search engine’s optimal choice: Knowledge-sharing service versus search quality |
S1567422314000490 | The interest in 3D technology and virtual reality (VR) is growing both from academia and industry, promoting the quick development of virtual marketplaces (VMs) (i.e. e-commerce systems in VR environments). VMs have inherited trust problems, e.g. sellers may advertise a perfect deal but doesn’t deliver the promised service or product at the end. In view of this, we propose a five-sense feedback oriented reputation mechanism (supported by 3D technology and VR) particularly for VMs. The user study confirms that users prefer VMs with our reputation mechanism over those with traditional ones. In our reputation mechanism, five-sense feedback is objective and buyers can use it directly in their reputation evaluation of target sellers. However, for the scenarios where buyers only provide subjective ratings, we apply the approach of subjectivity alignment for reputation computation (SARC), where ratings provided by one buyer can then be aligned (converted) for another buyer according to the two buyers’ subjectivity. Evaluation results indicate that SARC can more accurately model sellers’ reputation than the state-of-the-art approaches. | Reputation mechanism for e-commerce in virtual reality environments |
S1567422314000507 | While research in mobile advertising is abundant, limited attention has been paid to date to how consumers respond to mobile advertisements for different product categories and in which way impulsivity affects intentions to purchase. In this paper, we study the dimensionality of the product involvement construct and its effects on consumers’ purchase intentions via a simulated field experiment (N =736). We show that the cognitive dimension of product involvement and impulsiveness significantly affect purchase intentions. We also present that the relationship between product involvement and purchase intention is moderated by the consumers’ impulse buying personality traits. These findings progress the current state-of-the-art in mobile advertising research, while also having significant practical consequences for the design of effective mobile SMS advertising campaigns. | The effects of product involvement and impulse buying on purchase intentions in mobile text advertising |
S1567422314000519 | Mobile payment has long been discussed but has still not reached mass market in Western societies. Banks and telecom operators often struggle to develop platforms for authorization and authentication of mobile payment services. This paper analyses an in-depth case on collaboration between three major Dutch banks and three Dutch telecom operators who jointly developed a trusted service manager for mobile payment. Collective action theory and platform theory is combined to study the issues of collaboration and competition between banks and operators. We find that differing strategic objectives and interests, conflicts, lack of dependencies and governance issues led to dissolution of the mobile payment platform. These problems partly result from platform characteristics of openness to third parties, governance of relations with third parties and platform competition. | Collective action for mobile payment platforms: A case study on collaboration issues between banks and telecom operators |
S1567422314000702 | The primary objective of this research is to develop a theory-based model of utilitarian and hedonic website features, customer commitment, trust, and e-loyalty in an online hotel booking context. Structural Equation Modeling was deployed to test research hypotheses. Findings highlight the importance of creating loyalty by focusing on both hedonic and utilitarian features. Affective commitment is more influenced by hedonic features whereas calculative commitment is driven by utilitarian ones. Both commitment dimensions sway customers’ trust towards the online vendor and trust is an important antecedent of e-loyalty. Findings confirm that web design features are important for online relationship marketing. Both commitment dimensions were found to be precursors of trust whereas affective commitment is the precursor of e-loyalty. | The effect of website features in online relationship marketing: A case of online hotel booking |
S1567422314000714 | Many social network websites have been aggressively exploring innovative electronic word-of-mouth (eWOM) advertising strategies using information shared by users, such as posts and product reviews. For example, Facebook offers a service allowing marketers to utilize users’ posts to automatically generate advertisements. The effectiveness of this practice depends on the ability to accurately predict a post’s influence on its readers. For an advertising strategy of this nature, the influence of a post is determined jointly by the features of the post, such as contents and time of creation, and the features of the author of the post. We propose two models for predicting the influence of a post using both sources of influence, post- and author-related features, as predictors. An empirical evaluation shows that the proposed predictive features improve prediction accuracy, and the models are effective in predicting the influence score. | Predicting the influence of users’ posted information for eWOM advertising in social networks |
S1567422314000726 | Having a good understanding of users’ interests has become increasingly important for online retailers hoping to create a personalized service for a target market. Generally speaking, user’s browsing behaviors (when looking at websites) represent a comprehensive reflection of their interests. Users with various interests will visit multiple categories and research various items. Their browsing paths, the frequency of page visits and the time spent on each category all vary widely. Based on these considerations, a novel approach to discovering consumers’ interests is proposed and is systematically studied in this paper. The browsing behavior of a number of consumers – including their visiting sequence, frequency and time spent on each category – are mined via the click-stream data recorded on an e-commerce website. Given this behavioral data, we construct an improved leader clustering algorithm and leverage it with a rough set theory in order to generate users’ interest patterns. Furthermore, a case study is conducted based on nearly three million click-stream data, which was collected from one of the largest Chinese e-commerce websites. Using this data, the parameters of the algorithm are tested and optimized to make the algorithm more effective in terms of large data analysis and to make it more suitable for discovering users’ multiple interests. Using this algorithm, three typical user interest patterns are derived based on a real click-stream dataset. More importantly, further calculations based on different click-stream datasets verify that these three interest patterns are consistent and stable. This study demonstrates that the proposed algorithm and the derived interest patterns can provide significant assistances on webpage optimization and personalized recommendation. | A method for discovering clusters of e-commerce interest patterns using click-stream data |
S156742231400074X | In 1982, Betamax, the world’s first personal recording service was ruled as a fair use in court. Although the copyright holders of TV content claimed that Betamax was an infringement of copyright, the court determined that the benefits of personal recording services were significant and that the copyright holder’s profits could be protected because the original service was of better quality and had a better cost structure. It also ruled that the loss from manual advertisement skip was minimal. However, recent advancements in information technology have allowed new kinds of personal recording services such as a cloud DVR that provides unlimited storage and flawless quality, and an Auto-hop feature that automatically removes embedded advertisements. This paper introduces a microeconomic model for reviewing the copyright holder’s business model and social welfare under the court’s decision in relation to newer personal recording services powered by information technologies. Before cloud DVR existed, applying fair use to personal recording services increased social welfare while protecting the copyright holder’s profits; however, after the introduction of cloud DVR, it may no longer do so. | Analysis of an advertisement based business model under technological advancements in fair use personal recording services |
S1567422314000751 | Group buying is now a popular business mechanism whereby customers are encouraged to bargain together. Today’s group buying usually adopts a fixed group price rather than using a dynamic pricing mechanism and many retailers sell products using both a group buying and a posted price. Therefore, there are usually different shopping channels as the market can be divided into two segments, a group buying segment and an individual segment, according to consumers’ preference. However, sometimes the group buying option can decrease total revenue by influencing the individual market segment. Consequently, the seller has to decide: (1) whether to offer a group buying option and (2) if offering such an option, which combination is the best? In this paper, we classify customers into collectivist customers and individualistic customers depending on their different valuations of the time and energy spent in group buying communication and study the optimal pricing strategy according to actual market information. Although collectivist customers prefer to participate in group buying, our results show that this is true only in certain conditions in an individualistic dominant market where the potential demand of the individualistic customers is greater than that of the collectivist customers or the price coefficient of the individualistic customers is less than that of the collectivist customers. In these circumstances, the seller should provide a posted retail price together with a group buying option which can assist in customer discrimination, but in other conditions, the seller should only provide a posted retail price. Additionally, we have extended the two customer classifications to multiple classifications in this paper. | Optimal decisions on group buying option with a posted retail price and heterogeneous demand |
S1567422314000775 | E-selling is an activity that is distinct from e-commerce, e-marketing and e-retailing. E-selling is conceptualized to be computer–human dialog characterized by the digital spatio-temporal locus, the psychology of online persuasion, and complex perceptions of value. This definition warrants that flow user experience and human immersion are key premises for understanding e-selling. The ability to combine these with the different value drivers is identified as the key to e-selling success. This theoretical and conceptual article opens new avenues of research and design into online service design and user engagement. | E-selling: A new avenue of research for service design and online engagement |
S1567422314000805 | No research has quantitatively investigated knowledge contribution from the perspective of community support and leader support. Drawing on social exchange theory and organizational support theory, this study develops a model of perceived community support and leader support for knowledge contribution in online knowledge communities. The research model was tested using survey data collected from 169 online knowledge community users. The result shows that perceived community support and perceived leader support positively affect users’ knowledge contribution. Additionally, we identified the antecedents of perceived community support and leader support. Theoretical and practical implications are discussed. | Understanding knowledge contribution in online knowledge communities: A model of community support and forum leader support |
S1567422314000878 | While many reports predict huge growth potential for the mobile application (app) market, little is known about user intention to purchase paid apps. This study amends the expectation confirmation model and incorporates app rating, free alternatives to paid apps and habit as belief-related constructs to predict user behavior. The proposed model was empirically evaluated using a survey of 507 respondents about their perceptions of app usage. The results indicated that confirmation was positively related to perceived value and satisfaction. Value-for-money, app rating and free alternatives to paid apps were found to have a direct impact on intention to purchase paid apps. Specifically, there was a significant difference between potential users and actual users. The results may provide further insights into app marketing strategies. | What drives purchase intention for paid mobile apps? – An expectation confirmation model with perceived value |
S156742231400088X | Studies have shown that perceptual maps derived from online consumer-generated data are effective for depicting market structure such as demonstrating positioning of competitive brands. However, most text mining algorithms would require manual reading to merge extracted product features with synonyms. In response, Topic modeling is introduced to group synonyms together under a topic automatically, leading to convenient and accurate evaluation of brands based on consumers’ online reviews. To ensure the feasibility of employing Topic modeling in assessing competitive brands, we developed a unique and novel framework named WVAP (Weights from Valid Posterior Probability) based on Scree plot technique. WVAP can filter the noises in posterior distribution obtained from Topic modeling, and improve accuracy in brand evaluation. A case study exploring online reviews of mobile phones is conducted. We extract topics to reflect the features of the cell phones with a qualified validity. In addition to perceptual maps derived by multi-dimensional scaling (MDS) for product positioning, we also rank these products by TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) so as to visualize the market structure from different perspectives. Our case study of cell phones shows that the proposed framework is effective in mining online reviews and providing insights into the competitive landscape. | Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches |
S1567422314000891 | Reputation is crucial in promoting exchanges in online markets, since it may overcome information inefficiency through successful signals of sellers’ quality to less informed customers. To explore this issue, I study web sellers’ reliability in business-to-consumer online transactions with reference to reputation games. Customers can gather information in online marketplaces like e-Bay through public feedback systems. In contrast, without a centralized reputation system, it is not clear how potential buyers form their beliefs. For the latter case, I provide empirical evidence on perceived reliability and its determinants for some virtual shops operating worldwide in the clothing retail sector. | On the efficacy of imperfect public-monitoring of seller reputation in e-commerce |
S1567422314000908 | Compared with traditional information sharing in virtual communities, the status update in microblogs is more spontaneous and less rational. Users do not deliberately plan or arrange to update their real-time status but act in a sudden and unplanned manner. In this regard, purely rational theories, such as planned behavior and social exchange theories, are inappropriate in understanding status updates in microblogs. Hence, this study investigates status update behavior by integrating impulsive and reflective mechanisms. Specifically, a research model is built to investigate how microblog capabilities fulfill users’ media needs, including impulsive- and rational-oriented needs, and drive their urge to update their status. Online survey data from 523 microblog users in China are utilized to validate the proposed model and hypotheses. Results indicate that the urge of users to update their status is significantly influenced by the capabilities of microblogs to share unique content, strengthen positive emotion, maintain interconnectivity, and enhance unidirectional relationships. However, the capability of microblogs to relieve negative emotion does not significantly affect the urge of users to update their status. | Effect of perceived media capability on status updates in microblogs |
S156742231400091X | This study uses eye-tracking to explore the Elaboration Likelihood Model (ELM) in online shopping. The results show that the peripheral cue did not have moderating effect on purchase intention, but had moderating effect on eye movement. Regarding purchase intention, the high elaboration had higher purchase intention than the low elaboration with a positive peripheral cue, but there was no difference in purchase intention between the high and low elaboration with a negative peripheral cue. Regarding eye movement, with a positive peripheral cue, the high elaboration group was observed to have longer fixation duration than the low elaboration group in two areas of interest (AOIs); however, with a negative peripheral cue, the low elaboration group had longer fixation on the whole page and two AOIs. In addition, the relationship between purchase intention and eye movement of the AOIs is more significant in the high elaboration group when given a negative peripheral cue and in the low elaboration group when given a positive peripheral cue. This study not only examines the postulates of the ELM, but also contributes to a better understanding of the cognitive processes of the ELM. These findings have practical implications for e-sellers to identify characteristics of consumers’ elaboration in eye movement and designing customization and persuasive context for different elaboration groups in e-commerce. | An eye-tracking study of the Elaboration Likelihood Model in online shopping |
S1567422314000921 | We extend the conceptual model developed by Amelinckx et al. (2008) by relating electronic reverse auction (ERA) project outcomes to ERA project satisfaction. We formulate hypotheses about the relationships among organizational and project antecedents, a set of financial, operational, and strategic ERA project outcomes, and ERA project satisfaction. We empirically test the extended model with a sample of 180 buying professionals from ERA project teams at large global companies. Our results show that operational and strategic outcomes are positively related to ERA project satisfaction, while price savings are not. We also find positive relationships between financial outcomes and project team expertise; operational outcomes and organizational commitment, cross-functional project team composition, and procedural fairness; and strategic outcomes and top management support, organizational commitment, and procedural fairness. | An empirical study of electronic reverse auction project outcomes |
S1567422314000933 | Compared with traditional auctions, online auctions (used by, e.g., eBay and Yahoo) have several distinguishing features, including different ending rules (hard-close and soft-close), sequential arrival of customers, and random numbers of customers, all of which make bidding behavior more complex. The phenomenon of late bidding has been reported in the literature and, although the origin of this behavior has been analyzed theoretically, it is still not clear. Here, we study both first- and second-price online auctions with either hard- or soft-close ending rules and assume either private value (PV) or common value (CV). By dividing the auction process into two stages and then using backward induction, we find that late bidding is dominant under CV, but under PV late bidding dominates only in first-price online auctions with hard-close. Moreover, for second-price online auctions the dominant strategy for customers is to report their true value immediately upon arrival under PV but near the end of the auction under CV, irrespective of ending rules. Finally, we find that the timing of customer bidding is the same for hard- and soft-close except for first-price online auctions under PV. | Bidding strategies in online auctions with different ending rules and value assumptions |
S1567422314000945 | We examine the role of parental style versus peer influence on Generation Y’s attitudes towards online unethical activities using a survey of a matched parent–child sample. Results suggest that a protective parental style has the greatest impact on Generation Y’s online ethical attitudes, while a strict discipline style has no significant influence. Peers are more influential, but not as influential as when there is agreement between parents and their children on a specific activity. Methodologically, the research highlights the necessity to measure family dyads and assess whether or not parents and their children’s perceptions are the same. | The influence of parents versus peers on Generation Y Internet ethical attitudes |
S1567422315000022 | Increased internet penetration makes it possible for user generated content (UGC) to reflect people’s insights and expectations on economic activities. As representative and easily accessible UGC data that reflect public opinions on economic issues, Google search data have been used to forecast macroeconomic indicators in existing literatures. However, very little empirical research has directly used Google search data to improve the forecast accuracy. This paper proposes an integrated framework, which constructs keywords base and extracts search data accordingly, and then incorporates the search data into a mixed data sampling (MIDAS) model. Five groups of search data are extracted based on the constructed keywords and are then used in MIDAS model to forecast Chinese consumer price index (CPI) from 2004 to 2012. The empirical results indicate that the search data are strongly correlated with CPI, which is officially released by the Statistic Bureau of China; the MIDAS model including the search data outperforms the benchmark models, with the average reduction of root mean square error (RMSE) being 32.9%. This research provides a rigorous and generalizable framework for macroeconomic trend prediction using Google search data, and would have great potential in supporting business decisions by eliciting relevant information from UGC data in the Internet. | A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data |
S1567422315000046 | The proliferation of wireless technologies means that consumers are increasingly coming into contact with a diverse range of mobile services. Mobile service providers seeking to deliver a superior service must understand how consumers perceive these services. Although many instruments, such as SERVQUAL and E-S-QUAL, are available to measure service quality in general, to date there has been no instrument specifically designed to measure mobile service quality. Given the many types of mobile services available, our aim in this study was to ascertain the essential characteristics of such services by conceptualizing, constructing, refining, and testing a multiple-item scale, M-S-QUAL, designed to measure service quality in the mobile environment. According to Hinkin’s guidelines on scale development, the items in our scale were generated by following a deductive approach based on a theoretical foundation. There are two parts of M-S-QUAL, which assess m-commerce shopping experiences for virtual and physical products respectively. Thus, the scale developed in this study was designed to assess m-commerce shopping experiences for both virtual and physical products. We propose and empirical test a multidimensional model of M-S-QUAL using a sample of 578 Internet respondents. Through a five-step validation, the M-S-QUAL construction concluded with five factors (contact, responsiveness, fulfillment, privacy and efficiency) for the supporting services in the process of virtual product shopping and four factors (contact, responsiveness, fulfillment and efficiency) for the supporting services in the process of physical product shopping. These two aspects of M-S-QUAL demonstrate good psychometric properties, as confirmed by exploratory factor analysis, confirmatory factor analysis, and reliability and validity tests. The findings of this study will help mobile service providers to assess the quality of their services and assist researchers in developing mobile service quality theories. | M-S-QUAL: Mobile service quality measurement |
S1567422315000058 | Social networks proliferate in virtual communities, where interdependence and network convergence among users are key to their development. However, as little is known about the drivers of interdependence and network convergence, this study examines whether individual differences could be one such driver. An online questionnaire was used to collect data and responses from 3086 online gamers, and hierarchical regressions were used for the testing of hypotheses. This study found that the need for affiliation, altruism, and social intelligence are positively related to interdependence and network convergence. Moreover, the need for affiliation interacts with altruism to predict interdependence, and interacts with social intelligence to predict network convergence. This study is the first using the weak/strong tie theory to identify drivers of interdependence and network convergence among users of virtual communities. | Drivers of interdependence and network convergence in social networks in virtual communities |
S1567422315000125 | With keen competition in the online game industry, game developers and publishers are finding new ways to induce players’ to spend money on subscriptions and virtual items. As the online game itself provides a highly engaging environment, this study examines online sales from the perspective of customer engagement. We propose a research model that examines why game players actively engage in playing online games, and how such engagement can contribute to sales of online games, empirically testing the model using 377 online game players. The results support our research hypotheses and illustrate the effect of customer psychological engagement on stimulating game players’ spending in online games. In particular, both psychological and behavioral engagement exerted a positive influence on online sales, and the dimensions and antecedents of psychological engagement were also identified. The findings of this study are expected to provide some suggestions for game developers and publishers on promoting the sales of digital items/goods. This study also adds to the current understanding of customer psychological engagement by identifying its antecedents and consequences in the context of online games. | Promoting sales of online games through customer engagement |
S1567422315000149 | We conduct an incentivized experiment to study the effect of the payment method on spending. We find that the willingness to pay is higher when subjects pay with debit cards compared to cash. The result is robust to controlling for cash-on-hand constraints, spending type, price familiarity and consumption habits of the products. The evidence thus suggests that different representations of money matters for consumer behavior. Such results further tease out the underlying mechanism of how payment methods influence spending behavior, which poses important implications for both consumers and merchants, as well as designing of digitalized payment in the future. | Do consumers pay more using debit cards than cash? |
S1567422315000150 | The past twenty years have been a time of many new technological developments, changing business practices, and interesting innovations in the financial information system (IS) and technology landscape. They have led to the increasing use of prior innovations that have supported e-commerce, and that are now being brought into financial services to support different kinds of improvements to core business processes. This research examines recent changes in the payment sector in financial services, specifically related to mobile payments (m-payments) that enable new channels for consumer payments for goods and services purchases, and other forms of economic exchange. We extend recent research on technology ecosystems and paths of influence analysis for how industry-centered technology innovations arise and evolve. We explore the extent to which they can be understood through the lens of several simple building blocks, including technology components, technology-based services, and the technology-supported infrastructures that provide foundations for the related digital businesses. Our extension of the prior research focuses on two key elements: (1) modeling the impacts of competition and cooperation on different forms of innovations in the aforementioned building blocks; and (2) representing the role that regulatory forces play in driving or delaying innovation in the larger scope of our modeling approach. To assess the efficacy of our approach, we use it to retrospectively analyze the past two decades of innovations in the m-payments space. Our results identify the industry-specific patterns of innovation that have occurred, suggest how they have been affected by competition, cooperation and regulation, and point out some more universal patterns of technology innovations that offer insights into the development of e-commerce. | Competition, cooperation, and regulation: Understanding the evolution of the mobile payments technology ecosystem |
S1567422315000162 | The payment market has been stable for a number of decades with well-defined roles (acquirers and issuers), profitable business models (the card schemes) and a dominant design in which the merchants absorb the costs associated with payments. However, numerous digital payment solutions, which rely on new disruptive technologies, are emerging on the payment market, transforming the payment area from being established into a state of flux. In this article, we investigate the various factors that determine the success of a given solution. To this end, we build a framework to analyze the entry and expansion strategies of the digital payment solutions. We claim that the timing of entry of the first-mover speeds up the timing of entry of the early follower, thus determining the order of entry. We also argue that the timing of expansion is of equal importance as the timing of entry. If the expansion is not executed within the optimal time, the previously gained competitive advantage can be annulled. | The race to dominate the mobile payments platform: Entry and expansion strategies |
S1567422315000228 | The introduction of mobile payments is one of many innovations that are changing the payment market. This change involves new payment service providers entering this lucrative market, and meanwhile, the existing stakeholders are trying to defend their oligopolistic positions. The mobile payment market cooperation (MPMC) framework in this article shows how the digitalization of payments, as a technology innovation, affects the competition and collaboration among traditional and new stakeholders in the payment ecosystem at three levels of analysis. We do this by integrating theories of market cooperation with the literatures on business and technology ecosystems. The MPMC framework depicts technology-based market cooperation strategies in the context of recent battles in the mobile payments ecosystem. In these battles, the competitors can use technology either in defensive build-and-defend strategies to protect market position, or in offensive battering-ram strategies for ecosystem entry or position improvement. Successful strategies can lead to: (1) Ricardian rents, based on operational efficiency advantages traceable to the firm’s position relative to suppliers and monopoly power; and (2) Bainian rents, resulting from the extent the firm is able to resist price competition in the market. We validate the framework that we propose through three case studies of technology-based market cooperation in the mobile payments ecosystem. | The new normal: Market cooperation in the mobile payments ecosystem |
S156742231500023X | Owing to the limited information possessed by patients, there exists significant information asymmetry between patients and physicians. In addition, as services are intangible, inseparable, and heterogeneous, patients are difficult to judge the physicians’ service quality. With the development of online healthcare services, healthcare websites currently provide more information for patients, such as patient-generated and system-generated information. Those kinds of information can reflect the quality of physicians’ service outcome and delivery process to help patients to select physicians. However, there is scant research on the role of patient-generated and system-generated information in patients’ online behavior. Collecting data from a healthcare website, this paper develops a two-equation model to verify the effects of two kinds of information on patients’ search, evaluation and decision-making on healthcare websites. The results of our empirical research show that positive patient-generated and system-generated information on physicians’ service quality positively impact patients’ reactions at different stages. Moreover, we also find that synergies between patient-generated and system-generated information are positively associated with patients’ decisions to consult a physician. These findings provide valuable contributions to the online healthcare research. | Exploring the effects of patient-generated and system-generated information on patients’ online search, evaluation and decision |
S1567422315000241 | Transportation data center has recently become a common practice of modern integrated transportation management in major cities of China. Being the convergence center of large-scale multi-source vehicle tracking data, it caused great challenge on GPS map-matching efficiency and privacy protection. In this paper, we propose a secure parallel map-matching system based on Cloud Computing technology to meet the demand of transportation data center. The main contributions are as follows: (1) we propose a leapfrog method to improve the efficiency of traditional serial map-matching algorithm on the increasingly common high sampling rate GPS data; (2) we adapt the serial leapfrog map-matching algorithm for cloud computing environment by reforming it in the MapReduce paradigm; (3) we propose a privacy-aware map-matching model over hybrid clouds to realize the sensitive GPS data protection. We implemented the proposed map-matching system in the hadoop platform and tested its performance with a large-scale vehicle tracking dataset, which exceeds 100 billion records. The experimental results show that our approach is highly efficient and effective on massive vehicle tracking data processing. | Cloud computing-based map-matching for transportation data center |
S1567422315000253 | With the real-time brand communications prevalent in Twitter, it has emerged as an increasingly important social technology that facilitates brand-focused electronic word-of-mouth (eWOM) activities. The objective of the current research is to examine the factors which discriminate between Twitter brand followers’ decisions to engage in eWOM behaviors on the site. Specifically, this study proposes that peer communication, brand-related factors, and Twitter usage motivate brand followers to share eWOM messages on Twitter. Results from an online survey showed that brand followers who serve as role-models to others, those with positive attitudes toward and relationships with brands on Twitter, those who most heavily use Twitter and follow many brands, were most likely to tweet brands. Similar patterns were found in terms of retweeting the links of brands. This study contributes to the literature by demonstrating that Twitter is a socialization agent that facilitates eWOM and provides useful insights for social media marketers. | Using a consumer socialization framework to understand electronic word-of-mouth (eWOM) group membership among brand followers on Twitter |
S1567422315000332 | Nowadays the demands for wireless Internet shopping are increasing. But credit card fraud has been serious, and SET and SSL have their own problems. To enhance the security of online shopping, in this paper, we propose a secure m-commerce scheme, called the Secure M-Commerce System (SMCS for short), with which users can create a safe credit-card transaction for Internet shopping. Basically, the SMCS coordinates the cash flow of a trading system and its credit card entities to effectively protect the issued transactions against different attacks and avoid information leakage. The proposed system also employs a Data Connection Core (DCC for short) to link the card-issuing bank and consumers before their wireless communication starts so as to significantly improve the security level of our m-commerce environment. Theoretical analysis shows that the SMCS is more secure than SET and SSL. The performance analysis indicates that the SMCS is indeed a feasible m-commerce system. | A Secure M-Commerce System based on credit card transaction |
S1567422315000344 | In the last decade, Internet banking technology has made remarkable progress. However, there is a huge disparity across different nations all over the world in the diffusion of Internet banking services. This leads to the research question of this study: why different countries exhibit different levels of Internet banking adoption? Previous studies provide limited insight as they were mostly conducted at the individual user level with single-country samples. At the country level, this study proposes an Internet banking diffusion model that examines the impact of economical, technological and cultural factors on Internet banking diffusion. The hypothesized relationships in the research model were statistically tested with the secondary data collected from a sample of 33 European countries. The results indicate that the effects of socio-economic and technology-related factors on Internet banking diffusion are fully mediated by Internet access. Furthermore, the findings suggest that national culture is an important moderator as it make differences in Internet banking diffusion as well as Internet access across different country groups. The country-level analysis contributes to the advancement of Internet banking theory and practice, and provides some useful insights to researchers, practitioners and policy makers on how to enhance Internet banking diffusion. | Internet banking diffusion: A country-level analysis |
S1567422315000356 | Although the combinatorial double auction model has been proposed for buyers and sellers to trade goods conveniently for over a decade, it is still not widely adopted. Several factors that hinder the adoption of combinatorial double auction model include the high complexity to determine winning bids and the lack of studies on the schemes to benefit winners in an auction. A relevant challenging research issue is to study how to make this business model acceptable in the real world. Motivated by the deficiency of existing studies on these factors, we will study how to take advantage of the surplus of combinatorial double auctions to benefit the winners based on surplus optimization and schemes to reward winners. The contributions of this study are threefold: (1) we propose a computationally efficient approximate algorithm to tackle the complexity issue in combinatorial double auctions, (2) we propose schemes to reward winners based on the surplus of auctions and (3) our study paves the way for the promotion of combinatorial double auction model. Our main results include (i) a surplus optimization problem formulation that takes transaction cost and supply/demand constraints into account (ii) a divide-and-conquer approach to decomposing the optimization problem into subproblems and a subgradient method to determine shadow price (iii) several schemes to reward winners and (iv) numerical results that indicate that the winners can be better off by applying our schemes. | Schemes to reward winners in combinatorial double auctions based on optimization of surplus |
S1567422315000368 | Electronic government (or e-government) initiatives are widespread across the globe. The increasing interest in e-government raises the issue of how governments can increase citizen adoption and usage of their online services. In this study, the fundamental argument is that citizens can be viewed as customers, and that e-government success can be measured by the extent to which customer net benefits are positively influenced. Hence, the key consequents of e-government success are customer-related, and the antecedents of such success have to be considered from the customer viewpoint. We advocate that government agencies must consider their customers’ perceptions of empowerment as a key causal mechanism in deriving value from e-government systems. However, the literature appears to lack this perspective. This study aims to fill the gap by proposing a theoretical model and an associated evaluation tool that measures the e-government performance from a customer empowerment perspective. The model was validated by a survey method and analyzed using partial least squares. The results support our argument and show that all paths in the proposed model are significant. | Customer empowerment: Does it influence electronic government success? A citizen-centric perspective |
S156742231500037X | Concept drift is a common phenomenon in stock market that can cause the devaluation of the knowledge learned from cross-sectional analysis as the market changes over time in unforeseen ways. The widely used cross-sectional regression analysis based on expert knowledge has obvious limitations in handling problems that involve concept drift and high-dimensional data. To discover causal relations between portfolio selection factors and stock returns, and identify concept drifts of these relations, we apply a novel causal discovery technique called modified Additive Noise Model with Conditional Probability Table (ANMCPT). In evaluation experiments, we compares ANMCPT to the conventional cross-sectional analysis approach (i.e., Fama–French framework) in mining relationships between portfolio selection factors and stock returns. Results indicate that the factors selected by ANMCPT outperform the factors adopted in most previous cross-sectional researches that followed the Fama–French framework. To the best of our knowledge, this paper is the first to compare causal inference technique with Fama–French framework in concept drift mining of stock portfolio selection factors. Our causal inference-based concept drift mining method provides a new approach to accurate knowledge discovery in stock market. | Concept drift mining of portfolio selection factors in stock market |
S1567422315000381 | This study incorporates unexpectedness, delight, and customer citizenship behaviors (CCB) into the cognitive, affective, and behavioral stages of traditional expectancy theories, which, in general, contain confirmation, satisfaction, and continuance intention in each stage, respectively. Data collected from 436 app users shows that, from the cognitive stage to the affective stage, satisfaction is affected more by confirmation, and delight is determined more by unexpectedness. In contrast, from the affective stage to the behavioral stage, satisfaction has a greater effect on continuance intention, and delight is more critical for customer citizenship behavior. This study contributes to traditional expectancy theories by highlighting the importance of unexpectedness in the forming of continuance intention, and by illustrating the relatively critical role that components of each stage play in subsequent stages. | The effect of unexpected features on app users’ continuance intention |
S1567422315000393 | This publication presents new classes of financial management systems. These systems will serve as examples of systems for the cognitive analysis of financial data with particular emphasis on analysing financial ratios. Semantic analysis will be an innovative component in financial management systems which will enhance the traditional solutions by adding elements of cognitive data interpretation and analysis. The systems thus built will constitute a class of intelligent management systems. Intelligent financial management systems will also be used to conceal data of a confidential and strategic nature. For this type of solutions, advanced information sharing schemes will be proposed to enable splitting the information among a defined group of secret trustees in an either equal or privileged way. In addition, a scheme for sharing financial data between groups of secret trustees who can jointly reconstruct the shared financial (strategic) information will be proposed. In addition, a new class of financial management systems will be defined which will be optimal for financial data management processes. The new solution will consist of CFMSiC systems (Cognitive Financial Management Systems in the Cloud) dedicated to cloud-based semantic management of financial data. Motivation of the author’s study and this paper is to propose new aspects of the intelligent techniques dedicated for secure financial management in cloud computing. The most important research question is: which secure techniques are the optimal for secure strategic financial data management? To answer to this question it is important to conduct accurate analysis, which shows the efficiency in guaranteeing the secrecy of cognitive data analysis. Also, is necessary to discuss about the advanced techniques dedicated to secure strategic data in financial management processes. As a results of the author’s research, in this paper will be presented a strategic data sharing protocols in advanced threshold schemes, a sharing data schemes with a division into groups using advanced threshold schemes, and presentation of these solutions in a new class of information systems – in a CFMSiC systems (Cognitive Financial Management Systems in the Cloud) dedicated to managing financial data in the cloud. | Intelligent techniques for secure financial management in cloud computing |
S156742231500040X | Advertisement (abbreviated ad) options are a recent development in online advertising. Simply, an ad option is a first look contract in which a publisher or search engine grants an advertiser a right but not obligation to enter into transactions to purchase impressions or clicks from a specific ad slot at a pre-specified price on a specific delivery date. Such a structure provides advertisers with more flexibility of their guaranteed deliveries. The valuation of ad options is an important topic and previous studies on ad options pricing have been mostly restricted to the situations where the underlying prices follow a geometric Brownian motion (GBM). This assumption is reasonable for sponsored search; however, some studies have also indicated that it is not valid for display advertising. In this paper, we address this issue by employing a stochastic volatility (SV) model and discuss a lattice framework to approximate the proposed SV model in option pricing. Our developments are validated by experiments with real advertising data: (i) we find that the SV model has a better fitness over the GBM model; (ii) we validate the proposed lattice model via two sequential Monte Carlo simulation methods; (iii) we demonstrate that advertisers are able to flexibly manage their guaranteed deliveries by using the proposed options, and publishers can have an increased revenue when some of their inventories are sold via ad options. | A lattice framework for pricing display advertisement options with the stochastic volatility underlying model |
S1567422315000411 | Drawing upon social capital theory and the platform synergy perspective from systems theory, this study develops an explanatory model to explain how social capital, platform synergy and active participation affect consumer benefits in an online group buying (OGB) context. Data gathered from OGB consumers through a large-scale online survey is used to test the hypotheses. The conceptual model was validated using the partial least squares (PLS) technique. The results show that (1) the consumer benefits created by engaging in OGB are contributed collectively by social capital (i.e., social interaction ties, trust, and value of sharing), active participation and platform synergy; (2) the social capital enhances consumer benefits by increasing consumers’ active participation; (3) the OGB platform synergy has a positive impact on social capital, active participation and consumer benefits. Our findings highlight the important role of active participation in mediating the effect of social capital and platform synergy on OGB consumer benefits. The results provide insights into (1) how OGB platform developers can provide synergic functionalities that are compatible to OGB activities to enhance consumers’ capabilities and the efficiency of OGB processes; (2) how OGB managers and initiators can leverage platform synergy to enhance social capital and the mediating role of OGB consumers’ active participation in exchange resources within the OGB community, thereby leading to effective consumer benefit creation. | Consumer benefit creation in online group buying: The social capital and platform synergy effect and the mediating role of participation |
S1567422315000423 | The Internet has transformed traditional patterns of firm-to-customer communication and opened new channels through which enterprises can engage with consumers around the world. Yet ways to measure firms’ visibility in this electronic marketplace have failed to keep pace with these developments. We present a new model – the e-visibility maturity (e-VM) model – that can be used to assess the degree to which a firm or set of firms has the potential to engage customers in the global e-business market. The suggested model is developed based on a literature review, an international survey of online customers, and a comprehensive review of 1868 firm websites representing 27 industries in five countries. After presenting the model, we show how it can be scaled to different levels (e.g., the industry or country level) using three illustrative cases: a set of four countries across industries; a set of four industries across countries; and a set of four individual firms. We found substantial differences in levels of e-visibility and its specific dimensions of interactivity, firm globalization, sociability, and security between the countries and specific firms sampled. The industries sampled all emphasize firm globalization and interactivity. The model offers a simple and reliable way to evaluate a company’s adaptation to the challenges of the social web, and can be used by strategists and policy makers at the industry or government level as well as to help firms establish strategies for improving their position in the online marketplace. | E-visibility maturity model: A tool for assessment and comparison of individual firms and sets of firms in e-business |
S1567422315000538 | The Polish payment card market is important given the current position of Poland as the largest Central European country. The purpose of this research is to determine directions for the development of the payment cards market in Poland. An econometric model describing some important aspects of this market is presented. Short-term forecasts of this market’s development will be estimated using a set of constructed empirical econometric models, with particular attention paid to the intensity measures of payment card use, as well as to the use of payment card devices. The findings from these models are meant to provide answers to the question of what can be expected from observing the current Polish market for electronic payments. At the same time, the methodology that is applied is universal and can be used to study the directions of development for electronic payments market elsewhere in the world. It should also be emphasized that selection of an appropriate method requires the testing and matching of such models, which will describe market development most effectively. | Innovations in the payment card market: The case of Poland |
S156742231500054X | This article aims at assessing the progress of mobile payment research over the last 8years. A previous literature review (Dahlberg et al. 2008b), covering articles published between 1999 and 2006, showed that the majority of research had only focused on a few topics. In order to address this issue, a research agenda was formulated to encourage researchers to explore new topics. Almost a decade later, our review reveals that researchers have continued to focus on the same topics (especially consumer adoption and technology aspects) with a limited accumulation of new knowledge and similar findings. In addition to reviewing the literature, we discuss the possible reasons for the lack of research diversity and propose new recommendations to enhance future mobile payment research. | A critical review of mobile payment research |
S1567422315000551 | When developing pricing strategies, it is highly important for managers to understand brand competition resulting from price promotions within a store. To the knowledge of the authors the present study is the first to examine this topic in the fashion e-commerce space. Using a unique data-set with more than 3.3 million observations which was provided by a leading European e-commerce company, we empirically estimate cross-price elasticities in two independent product categories. Regression results show unexpectedly low levels of cross-brand competition due to the distinctiveness of fashion merchandise prohibiting customers to take advantage of increased market transparency in e-commerce. In addition, patterns of brand competition are very distinct as there is only a small share of significant but highly pronounced effects. Moreover, the results show that asymmetric competition exists between private and national brands. Lastly, we also discuss implications for markdown pricing strategies in the context of fashion e-commerce. | Brand competition in fashion e-commerce |
S1567422315000575 | Cloud computing has rapidly become the most effective computing paradigm for today’s increasingly technology-dependent society. The emerging concepts of federated clouds with support for interoperability between different cloud providers and open standards in cloud middleware have opened up new challenges in cloud service management. One of the emerging research areas in cloud computing is the possibility of live virtual machine migration between different clouds. This is of importance when the quality of a cloud service currently used by a user degrades or a new cloud service is developed which is better in terms of quality, performance and cost than the current service being used. In such scenarios, the user needs to make a decision as to whether to continue with the currently used service or migrate to the newly available service. In our previous work, we presented a decision-making approach that assists a cloud service user in selecting a cloud service provider based on the QoS of its services. In this paper, we extend our previous work in the pre-interaction time phase and discuss the decision-making process involved in the migration from one cloud service to another cloud service through inter-cloud virtual machine migration. | Decision-making framework for user-based inter-cloud service migration |
S1567422315000587 | In the Internet era, through the web, and access to content, products and services has evolved in a spectacular way. At the same time, different business models have been developed for access and consumption. Many of these business models are based on making a payment via the web. The use of electronic payments in the web is a complex issue since it involves the support of multiple payment instruments, the secure exchange of payment information, receipts, and so on. A proposed solution approach to web payments is the development of a web payment framework based on a layered approach. This article analyzes the functionality this framework should provide, what solutions may be used, and what issues still need to be addressed so that a web payment framework can make e-payments more widespread. | Towards a web payment framework: State-of-the-art and challenges |
S1567422315000599 | In order to improve the tourist experience, recommender systems are used to offer personalized information for online users. The hotel industry is a leading stakeholder in the tourism sector, which needs to provide online facilities to their customers. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user’s needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. They use a single rating as input. However, the multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and they can be an appropriate choice for the tourist. In this paper, we propose a new hybrid method for hotel recommendation using dimensionality reduction and prediction techniques. Accordingly, we have developed the multi-criteria CF recommender systems for hotel recommendation to enhance the predictive accuracy by using Gaussian mixture model with Expectation Maximization (EM) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS). We have also used the Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF dataset. Our experiments confirmed that the proposed hybrid method achieved high accuracy for hotel recommendation for the tourism sector. | A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS |
S1567422315000605 | Trust is a crucial concern related to unknown networks. A mechanism that distinguishes trustworthy and untrustworthy nodes is essential. The effectiveness of the mechanism depends on the accuracy of a node’s reputation. The dynamics of trust often occurs in a trusted network and causes intoxication and disguises of the nodes, resulting in abnormal behaviors. This study proposes a semi-distributed reputation mechanism based on a dynamic data-driven application system. This mechanism includes two reputations, local reputation (LRep) and global reputation (GRep). LRep is dynamically and selectively injected into a central controller, and this controller collects the injected data to compute GRep, which contains the neural network prediction method, and returns it to provide reference to the distributed nodes. The proposed mechanism focuses on dynamics of trust and the balance between distributed nodes and the central controller. Experimental results showed that GRep was computable with only 52.21% (average) LReps upload and that GRep increased or reduced by 26.5% (average) in a short period, demonstrating that the proposed mechanism effectively handles the problem of dynamics of trust. | A dynamic data driven-based semi-distributed reputation mechanism in unknown networks |
S1567422315000617 | The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed “selective price disclosure”, which improves the attractiveness of a CSA by removing some of the prices in the outputted list. The underlying idea behind this approach is to affect the buyer’s beliefs regarding the chance of obtaining more attractive prices. The paper presents two methods, which are suitable for fully-rational buyers, for deciding which prices among those known to the CSA should be disclosed. The effectiveness and efficiency of the methods are evaluated using real data collected from five CSAs. The methods are also evaluated with human subjects, showing that selective price disclosure can be highly effective in this case as well; however, the disclosed subset of prices should be extracted in a different (simplistic) manner. | Improving comparison shopping agents’ competence through selective price disclosure |
S1567422315000629 | This study discovers part-of-speech (POS) patterns of sentences that express opinions in Chinese product reviews. The use of these patterns makes it possible to identify opinion sentences, feature words, and opinion/feeling words. Degree words and negation words are used in determining the orientation of opinions as well as the degree of their intensity. In order to identify the subject of opinions, the associations between opinion/feeling words, feature words, and corresponding features were ascertained. An algorithm for feature-based opinion summarization is then proposed based on these patterns and association rules. Both car and movie reviews were collected for discovering patterns and testing of the patterns and algorithm. The experimental results demonstrate that the proposed algorithm and approaches perform well on Chinese product reviews. | Discovering Chinese sentence patterns for feature-based opinion summarization |
S1567422315000630 | A trusted service manager (TSM) mediates between service providers, mobile network operators (MNOs), and the secure element (SE) issuer. As a facilitator to accelerate the development of the Near Field Communication (NFC) application ecosystem, such as mobile payments, e-tickets, etc., the TSM and its issues have received increased attention in recent years. In this study, the TSM development modes are analyzed under the challenges of mobile operating system designers (OSD) by an analytical network process. The research framework for developing a TSM is constructed by four main criteria and twelve sub-criteria through a literature review. Based on the authors’ previous work, five possible developing modes of TSMs are set as alternatives. An analytic network process (ANP) framework is proposed, and expert questionnaires are designed. Eighteen experts in NFC-related domains, including MNO, bank, smart card, and TSM stakeholders, were interviewed, and a proper developing mode for TSM in Taiwan was obtained. All these experts are in businesses related with TSM or are familiar with the TSM ecosystem. The analytical results not only include the ranks of four main criteria and twelve sub-criteria but also reveal that experts expect “Cooperation mode” to be the best alternative for developing a TSM in Taiwan. The explanations for the ranks are discussed with the current NFC ecosystem in Taiwan. The research framework for developing a TSM in this study is also expected to be applied to different countries, as developing modes of a TSM are generally multiple-criteria decision-making issues. Developing modes of a TSM for other countries could be implemented by changing the criteria, sub-criteria and alternatives of this research structure according to a country’s own political, NFC-related business environment or development stage of the NFC industry. In conclusion, this study provides academic and managerial implications of the results showing why “Cooperation mode” could be the best alternative to develop a TSM in Taiwan. This study’s contributions are as follows. First, an ANP network of TSM development is constructed, and it can be applied to other countries with similar developing mode analysis processes. Second, a proper developing mode of a TSM in Taiwan based on the views of different experts is uncovered. | An analysis of trusted service manager development modes by mobile operating system designers in Taiwan |
S1567422315000642 | Information control has been deemed one of the most prominent features of the Internet for online consumers searching product information. This study examined how information control affects the online information seeking processes of consumers and how the effects are moderated by shopping purposes (utilitarian vs. hedonic). This study recruited 292 respondents to participate in our experiment. The empirical results reveal that information control significantly increases consumer involvement in information seeking, enhances attitudes toward products, and elevates the degree of satisfaction toward commercial websites. Furthermore, the effects of information control on consumer involvement and product attitudes are moderated by their shopping purposes. The results support most of the proposed hypotheses, suggesting that information control works more effectively for utilitarian consumers than for hedonic consumers. The findings of this study offer online practitioners useful recommendations regarding personalization strategies of website design. | Controlling information flow in online information seeking: The moderating effects of utilitarian and hedonic consumers |
S1567422315000654 | Word-of-mouth has been recognized as a dominant factor in shaping consumer behavior. However, what drives consumers to post their positive and negative purchasing experiences in the online environment remains an important but largely neglected issue. Building on the theory of planned behavior, the justice theory and the social psychology literature, this study aims to investigate the antecedents of consumers’ intention to engage in eWOM communication. Specifically, we separate negative and positive eWOM into two distinct concepts since the motivations underlying consumers’ decisions to post positive and negative eWOM are likely to be different. Through an experience survey, respondents were required to reflect on recent positive or negative shopping experiences. The findings reveal that intentions to engage in positive and negative eWOM communication are associated with different antecedents. Consumers who intend to post positive eWOM appear to be more driven by underlying attitudinal factors, whereas those who consider posting negative eWOM are more driven by social pressure. In addition, consumers’ feelings of satisfaction are largely driven by their perception of distributive justice for negative shopping experiences. In contrast, satisfaction is significantly influenced by the perceptions of interactional and procedural justice for positive shopping experiences. Our results provide insights and implications for scholars and managers. | Understanding why consumers engage in electronic word-of-mouth communication: Perspectives from theory of planned behavior and justice theory |
S1567422315000745 | Consumers today are using various channels to complete their purchase process. As shoppers pursue different goals at each stage of the process, channel choice may be explained by different drivers for search, purchase and post-sales activities. Our research framework is based on an extension of the TAM Model with the support of the Motivational Model, differentiating two types of motivations for channel usage: intrinsic and extrinsic. Moreover, we rely on transaction costs economics to explain different channel usage at each shopping stages and for different product categories. Lineal regression and cluster analysis are applied to data collected through a survey answered by 1533 multichannel retail shoppers within two product categories (apparel and consumer electronics) in two countries (UK and Spain). Our findings show that segments with different usage patterns and motivations can be identified across the shopping process and that the drivers of channel usage are different depending on the stage of the buying process and the product category considered. | Identifying patterns in channel usage across the search, purchase and post-sales stages of shopping |
S1567422315000757 | The increasing interest in sentiment classification of product reviews is due to its potential application for improving e-commerce services and quality of the products. However, in realistic e-commerce environments, the review-related data are imbalanced, and this leads to a problem in which minority class information tends to be ignored during the training phase of a classification model. To address this problem, we propose a topic sentence-based instance transfer method to process imbalanced Chinese product reviews by using an auxiliary dataset (source dataset). The proposed method incorporates a rule and supervised learning hybrid approach to identify a topic sentence of each product review and adds the feature set of the topic sentence to the feature space of sentiment classification. Next, to measure the transferability of instances in source dataset, a greedy algorithm based on information gain of top-N common features is used to select common features. Then, a common feature-based cosine similarity of instances between source dataset and target dataset is introduced to select the transferable instances. Furthermore, a synthetic minority over-sampling technique (Smote) based method is adopted to overcome feature space inconsistency between the source dataset and target dataset. Finally, we immigrate the instances selected in source dataset into target dataset to form a new dataset for the training of classification model. Two datasets collected from Jingdong and Dangdang are the target dataset and source dataset. The experimental results verify that, considering the ability of generalization, our proposed method helps a support vector machine (SVM) to outperform other classification methods, such as the J48, Naive Bayes, Random Forest and Random Committee methods, when applied to datasets produced by resampling and Smote. | A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews |
S1567422315000769 | E-business engineering involves the study of evolving IT technologies and management science approaches to revolutionise e-business models and behaviors, and the demands from new e-business activities that prompt the development of new technologies and to make progress on management methods. In this essay, the scope of e-business engineering is illustrated and its importance to e-business is highlighted. Service-oriented computing (SOC) was chosen among numerous related topics for further analysis due to its role in the increasing popularity of cloud services and Internet of services (IoS) as they are hot commodities in e-business and important enabling technologies for e-business. The focus of this state-of-the-art review is on two SOC core technologies: service description language and service registries for service discovery and composition. A number of key frameworks and developments in the area are discussed in terms of their pros and cons and their associated challenges in the fast growing e-services marketplace. This essay also points out future developments and research directions to meet the related challenges, such as the standardization of service description and directory modelling, and the automated generation of annotation based on semantics and domain ontology. | E-services in e-business engineering |
S1567422315000770 | Mobile health (mHealth) services have gained increasing attention in recent years; however, few studies have focused on the manner in which customers’ attributes affect acceptance behavior, for instance, personal privacy concerns and personalization concerns; with even fewer studies on the effects on different age groups. To fill this research gap, our research has developed an attribute–perception–intention model, using the privacy–personalization paradox factors as independent variables that affect mHealth acceptance intention, with trust as a mediator. The age differences of participants were then examined. A survey of 650 subjects in China was conducted to test the proposed research model and hypotheses. The results show the following key findings: (1) perceived personalization and privacy concerns are positively and negatively associated with behavior intention; (2) trust mediates the relationships between perceived personalization, privacy concerns and behavior intention; and (3) age differences are examined in the model, which in this respect differ from previous technology acceptance research. Theoretical and practical implications are also discussed. | The privacy–personalization paradox in mHealth services acceptance of different age groups |
S1567422315001027 | Due to the fragmentation of the mobile payment market, vendors have a plurality of mobile payment providers they can choose to execute payment processes in the mobile versions of their shops. Besides differences in transaction fees, mobile payment providers can also differ in respect of their reputation. However, it remains unclear how the reputation of mobile payment providers and online vendors interact and affect consumers’ risk perception and transaction intention. Therefore, our study analyses different combinations of mobile payment provider and online vendor reputations and finds that consumers attribute distinct trusting beliefs towards these two types of market players and that these substantially affect consumers’ intentions to transact. While online vendors with low reputation can profit from embedding reputable mobile payment providers, reputable online vendors do not increase transaction likelihood by integrating reputable mobile payment providers compared with less reputable payment providers. For research, the results provide a novel understanding of the interaction of two market players in the m-commerce value chain subject to varying degrees of reputation. For online vendors, our results give direct guidance in the process of selecting external payment entities to establish consumer trust and facilitate transactions. | Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions |
S1567422315001039 | We studied the relationship between individuals’ group social capital and their lending outcomes in the online peer-to-peer financial credit market, where individual lenders make direct unsecured microloans to other individual borrowers. Despite its ability to facilitate economic exchange, social capital as public goods may also cause free-rider problems, particularly in an online environment. Based on the analyses of transaction data collected from one of the largest online peer-to-peer lending platform in the U.S., we found that the borrower’s general group social capital (i.e., group membership) and relational social capital (i.e., group credibility and verifiability, and group trust) yielded inconsistent effects, and the borrower’s structural social capital (i.e., group inclusiveness) had a negative impact on, his/her funding and repayment performance. We discuss the implications of our findings for reconciling two major but conflicting theoretical views of social capital and for improving institutional mechanism design in a decentralized online financial credit market. | Group social capital and lending outcomes in the financial credit market: An empirical study of online peer-to-peer lending |
S1567422315001040 | We present a new form of online tracking: explicit, yet unnecessary leakage of personal information and detailed shopping habits from online merchants to payment providers. In contrast to the widely debated tracking of Web browsing, online shops make it impossible for their customers to avoid this dissemination of their data. We record and analyse leakage patterns for the 881 most popular US Web shops sampled from actual Web users’ online purchase sessions. More than half of the sites we analysed shared product names and details with PayPal, allowing the payment provider to build up fine-grained and comprehensive consumption profiles about its clients across the sites they buy from, subscribe to, or donate to. In addition, PayPal forwards customers’ shopping details to Omniture, a third-party data aggregator with even larger tracking reach than PayPal itself. Leakage to PayPal is commonplace across product categories and includes details of medication or sex toys. We provide recommendations for merchants. | Shopping for privacy: Purchase details leaked to PayPal |
S1567422315001052 | Social commerce enables companies to promote their brands and products on online social platforms. Companies can, for instance, create brand pages on social networking sites to develop consumer–brand relationships. In such circumstances, how to build consumers’ brand loyalty becomes a critical concern. To address this, we draw upon the relationship quality perspective to suggest that brand loyalty is primarily determined by relationship quality, which is further influenced by self-congruence (i.e., the self factor), social norms (i.e., the social factor), information quality and interactivity (i.e., characteristics of brand pages). To test our model, we conduct an empirical survey on companies’ brand microblogs. We find that all proposed hypotheses are supported. Interestingly, the self factor rather than other factors was found to have the strongest impact in the model. In addition to its noteworthy implications for practitioners, we believe that this study provides important theoretical insights into understanding how to build brand loyalty in social commerce. | Building brand loyalty in social commerce: The case of brand microblogs |
S1567422315001064 | This study investigates the effects of tie strength between the two communicators, recommender experience, and their interactions on electronic word-of-mouth message credibility and purchase intentions, and the mediated moderation on intention. Prior research has rarely addressed the fact that the effect of tie strength moderated by recommender experience is mediated by the effect of message credibility on intention. The final sample comprised 302 students who participated in a 3 (tie strength: strong vs. weak vs. no tie) by 2 (experience: high vs. low) between-subject experiment. Multivariate analysis of covariance and bias-corrected bootstrapping analysis using the PROCESS macro were used to test the hypotheses. The results showed that tie strength and recommender experience positively affect message credibility and intentions. Further, recommender experience moderates the effect of tie strength on intentions, while the moderating effects on intention are mediated by message credibility. While prior research suggests that negative recommendations from people with strong ties affect message effectiveness, the results of the present study deviate from this and show that recommendations from weak ties are as persuasive as those from strong ties when the message is delivered by recommenders with experience. | Impact of tie strength and experience on the effectiveness of online service recommendations |
S1567422316000028 | Unlike actual sales figures, sales ranks are widespread in the field of electronic commerce, which motivates economists and marketing scholars to look for the avenues of converting sales ranks into actual sales or market shares that are needed for demand estimation. In this study the relationship between actual sales and sales ranks is calibrated using a large online store’s unique data on 11 product categories, for which this relationship has never been calibrated before. By allowing the shape parameter of the power law to vary with the sales rank we managed to increase a traditionally used model’s fit for most of the product categories. Our parameter estimates can be used by researchers that would like to get a reasonably good approximation of market shares based on sales ranks. We also validated and modified Garg and Telang’s (2013) approach to inferring market shares using data on product price, sales rank and revenue rank. The approach, especially its modified version, was shown to lead to a reasonably low market shares prediction error, making it possible for researchers to infer the shares of sales based solely on sales and revenue rankings from companies that prefer not to disclose actual sales data. | Rank-sales relationship in electronic commerce: Evidence from publicly available data on 11 product categories |
S156742231600003X | Despite the huge growth potential that has been predicted for in-app purchases and the mobile game market, little is known about what motivates game players to make such purchases. The purpose of this paper is to build a research model based on the loyalty literature and studies of value theory to identify the antecedents of in-app purchase intention in the context of mobile games. The proposed model was empirically evaluated using a web survey of 3309 mobile game players: 813 nonpaying players and 2496 paying players. Structural equation modeling was used to assess the research model. The results reveal that loyalty to the mobile game has significant influence on a player’s intention to make an in-app purchase. The perceived values of the game (playfulness, connectedness, access flexibility, and reward) have direct influence on the loyalty of all players but appear to have relatively little impact on the purchase intentions of nonpaying players. Two values (loyalty and good price) were found to have a direct impact on a player’s intention to make an in-app purchase. Specifically, our study revealed differences between paying users and nonpaying users. This study provides a better understanding of how the values influence loyalty among all players of the game, and the purchase intentions of paying and nonpaying players. Further insights into mobile game app marketing strategies are provided as well. | What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty |
S1567422316000041 | Given our limited and irrational use of medical resources, governments hope that using online health communities can help patients receive necessary treatments. Although abundant research has studied the role of reputation in e-commerce, studies on the role of reputation in online health communities (OHCs) remain scarce. This paper focuses on investigating how the reputation of one physician’s colleagues affects the focal physician’s future review amount, which is an important predictor for physicians’ performance in the future. We examine this question by studying a special service: Online Booking, Service in Hospitals (OBSH). We find that both the focal physician’s reputation and his/her colleagues’ reputation have significant impact on his/her patients’ odds of sharing their treatment experience online. In addition, colleagues’ reputation negatively moderates the relationship between the focal physician’s reputation and his/her patients’ odds of sharing their treatment experience. | How your colleagues’ reputation impact your patients’ odds of posting experiences: Evidence from an online health community |
S1567422316000132 | Selecting a movie often requires users to perform numerous operations when faced with vast resources from online movie platforms. Personalized recommendation services can effectively solve this problem by using annotating information from users. However, such current services are less accurate than expected because of their lack of comprehensive consideration for annotation. Thus, in this study, we propose a hybrid movie recommendation approach using tags and ratings. We built this model through the following processes. First, we constructed social movie networks and a preference-topic model. Then, we extracted, normalized, and reconditioned the social tags according to user preference based on social content annotation. Finally, we enhanced the recommendation model by using supplementary information based on user historical ratings. This model aims to improve fusion ability by applying the potential effect of two aspects generated by users. One aspect is the personalized scoring system and the singular value decomposition algorithm, the other aspect is the tag annotation system and topic model. Experimental results show that the proposed method significantly outperforms three categories of recommendation approaches, namely, user-based collaborative filtering (CF), model-based CF, and topic model based CF. | A hybrid approach for movie recommendation via tags and ratings |
S1567422316000144 | In the real-time bidding (RTB) display advertising ecosystem, when receiving a bid request, the demand-side platform (DSP) needs to predict the click-through rate (CTR) for ads and calculate the bid price according to the CTR estimated. In addition to challenges similar to those encountered in sponsored search advertising, such as data sparsity and cold start problems, more complicated feature interactions involving multi-aspects, such as the user, publisher and advertiser, make CTR estimation in RTB more difficult. We consider CTR estimation in RTB as a tensor complement problem and propose a fully coupled interactions tensor factorization (FCTF) model based on Tucker decomposition (TD) to model three pairwise interactions between the user, publisher and advertiser and ultimately complete the tensor complement task. FCTF is a special case of the Tucker decomposition model; however, it is linear in runtime for both learning and prediction. Different from pairwise interaction tensor factorization (PITF), which is another special case of TD, FCTF is independent from the Bayesian personalized ranking optimization algorithm and is applicable to generic third-order tensor decomposition with popular simple optimizations, such as the least square method or mean square error. In addition, we also incorporate all explicit information obtained from different aspects into the FCTF model to alleviate the impact of cold start and sparse data on the final performance. We compare the performance and runtime complexity of our method with Tucker decomposition, canonical decomposition and other popular methods for CTR prediction over real-world advertising datasets. Our experimental results demonstrate that the improved model not only achieves better prediction quality than the others due to considering fully coupled interactions between three entities, user, publisher and advertiser but also can accomplish training and prediction with linear runtime. | Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization |
S1567422316300011 | Most existing user authentication approaches for detecting fraud in e-commerce applications have focused on Secure Sockets Layer (SSL)-based authentication to inspect a username and a password from a server, rather than the inspection of personal biometric information. Because of the lack of support for mutual authentication or two-way authentication between a consumer and a mercantile agent, one-way SSL authentication cannot prevent man-in-the-middle attacks. In practice, in user authentication systems, machine learning and the generalisation capability of support vector models (SVMs) are used to guarantee a small classification error. This study developed an online face-recognition system by training an SVM classifier based on user facial features associated with wavelet transforms and a spatially enhanced local binary pattern. A cross-validation scheme and SVMs associated with the Olivetti Research Laboratory database of user facial features were used for solving classification precision problems. Experimental results showed that the classification error decreased with an increase in the size of the training samples. By using the aggregation of both the low-resolution and the high-resolution face image samples, the global precision of face recognition was over 97% with tenfold cross-validation scheme for an image data size of 168 and 341, respectively. Overall, the proposed scheme provided a higher precision of face recognition compared with the average precision for low-resolution face image (approximately 89%) of the existing schemes. | Face recognition using support vector model classifier for user authentication |
S1567422316300023 | This study develops a crowdfunding sponsor typology based on sponsors’ motivations for participating in a project. Using a two by two crowdfunding motivation framework, we analyzed six relevant funding motivations—interest, playfulness, philanthropy, reward, relationship, and recognition—and identified four types of crowdfunding sponsors: angelic backer, reward hunter, avid fan, and tasteful hermit. They are profiled in terms of the antecedents and consequences of funding motivations. Angelic backers are similar in some ways to traditional charitable donors while reward hunters are analogous to market investors; thus they differ in their approach to crowdfunding. Avid fans comprise the most passionate sponsor group, and they are similar to members of a brand community. Tasteful hermits support their projects as actively as avid fans, but they have lower extrinsic and others-oriented motivations. The results show that these sponsor types reflect the nature of crowdfunding as a new form of co-creation in the E-commerce context. | A typology of crowdfunding sponsors: Birds of a feather flock together? |
S1567422316300047 | Mobile Internet has developed rapidly, and various types of mobile Internet services have changed people’s lifestyles profoundly. Consequently, there is a broad market for mobile Internet service providers. To provide better service and attract users, service providers must understand their users’ behavior patterns. This study proposes a framework to model users’ mobile online behavior based on a multi-state model and a hidden Markov model; this study also extracts typical sequential behavior patterns through clustering methods. The results of the experiments display several characteristic behavior patterns that can guide service providers in application designing, operating, and marketing. | Exploring the sequential usage patterns of mobile Internet services based on Markov models |
S1567422316300059 | Studies have shown that technology addiction distorts the true intentions of technology users. This is primarily due to the maladaptive perceptions that form as a result of the addiction to technology. Internet is the vehicle for e-commerce; therefore, understanding Internet addiction (IA) is critical to developing a sustainable and healthy environment for e-commerce growth and ignoring it could lead to a myriad of business, societal, ethical and legal ramifications. Internet Addiction Test (IAT) is a well-established instrument for measuring an individual’s addiction level. While IAT has been widely adopted clinically and in research in many countries, the differences in the underlying constructs of IA among various countries have not been sufficiently examined. Using the data collected from 488 US, 453 African, and 209 Chinese college students, this study focuses on discovering the differences in the underlying properties of IA from a cross-cultural perspective. The analysis shows that a sizable percent of the users in each region suffers from IA problems. More importantly, the results indicate that the key underlying IA psychometric constructs are substantially different in different cultural, economic and technological contexts. Further, the implications of the findings and directions for future research are discussed. | Understanding the underlying factors of Internet addiction across cultures: A comparison study |
S1567422316300072 | In this study, we investigate the economic impact of flaming on the Internet using Japanese data. In examining the data on firms that experienced flaming between 2006 and September 2013, we establish the following three main findings. First, large firms and ones with negative net income are more likely to be flamed on the Internet. Second, flaming alone may be too weak to impact the stock prices of target firms in the short-term, although it can lower the stock price of target firms at a later period, or when newspapers report the same event. Third, the negative market reaction grows when the flaming content is serious. | Characteristics and stock prices of firms flamed on the Internet: The evidence from Japan |
S1567422316300151 | The focus of this study is customer behavior during the process of booking a hotel through online travel agencies (OTAs). We developed a stochastic programming model to design the optimal sequence of hotels that enables customers to find hotels at the minimum search cost and maximum utility gained from hotels. The number of available hotels in the sequence was strategically decided to minimize search cost. We considered the multidimensional preferences of customers, including price, star rating, review rating, and reservation price. We collected customer information through a survey and took hotel information from a selected OTA. This information was then used through numerical simulations to test the performance of the modeling approach. The results suggest that hotels with higher utility, review rating, star rating, and price should be ranked in the upper position of the sequence. We highlight an application of the proposed model that can help improve hotel performance in today’s competitive market. | Improving the multidimensional sequencing of hotel rooms on an online travel agency web site |
S1567422316300163 | Popularity information is identified as one of the important factors for businesses in the online marketplace and is normally expected to strengthen sales performance. At the same time, the effects of popularity information are found to vary across product types. In this study, we attempt to identify the effects of popularity information on product sales through an analysis of the subgroups of category and price. Two sequential field experiments in an online apparel store on Alibaba’s Tmall, the most dominant online brand marketplace in China, are conducted to capture the causal effects of the popularity information on sales. After observing the possible existence of popularity information effects through a pilot test of 17 products, we conduct the main experiment with 290 products, recording the daily sales for each by posting selected products on the hit list. The difference-in-differences method and propensity score matching are used to analyze the effects. The results show that once the products are displayed on the hit list, product sales increase by an average of 1.3units per day. One subgroup of the niche product category is found to be influenced more significantly by hit list information than are other subgroups in the broad appeal category. Furthermore, after the hit list information is presented, more units of mid-price products are likely to be sold than units of products with high and low prices. | An analysis of popularity information effects: Field experiments in an online marketplace |
S1567422316300175 | The influence of user generated content on e-commerce websites and social media has been addressed in both practical and theoretical fields. Since most previous studies focus on either electronic word of mouth (eWOM) from e-commerce websites (EC-eWOM) or social media (SM-eWOM), little is known about the adoption process when consumers are presented EC-eWOM and SM-eWOM simultaneously. We focus on this problem by considering their adoption as an interactive process. It clarifies the mechanism of consumer’s adoption for those from the perspective of cognitive cost theory. A conceptual model is proposed about the relationship between the adoptions of the two types of eWOM. The empirical analysis shows that EC-eWOM’s usefulness and credibility positively influence the adoption of EC-eWOM, but negatively influence that of SM-eWOM. EC-eWOM adoption negatively impacts SM-eWOM adoption, and mediates the relationship between usefulness, credibility and SM-eWOM adoption. The moderating effects of consumers’ cognitive level and degree of involvement are also discussed. This paper further explains the adoption of the two types of eWOM based on the cognitive cost theory and enriches the theoretical research about eWOM in the context of social commerce. Implications for practice, as well as suggestions for future research, are also discussed. | E-WOM from e-commerce websites and social media: Which will consumers adopt? |
S1567422316300205 | Quality of experience (QoE) plays a crucial role in attracting and retaining users of interactive Internet applications. In this work, the relation between quality of service (QoS) perceived by users and the satisfaction level of users is carefully studied. In our experiments, users encountered certain latencies while using a photo viewing service on their mobile phone; we used the experience sampling method (ESM) to record the satisfaction level of these users on a scale of one to five. The user opinion data are ordinal; therefore, it is not meaningful to treat the data as metric. To address this issue, we used Bayesian data analysis with a generalized linear model (GLM) to estimate the overall satisfaction of the users in the form of the posterior distribution of opinions. We propose that the quality of experience of users can be represented by opinion score distribution (OSD) instead of the mean opinion score (MOS). | Impact of service quality on user satisfaction: Modeling and estimating distribution of quality of experience using Bayesian data analysis |
S1567422316300217 | We investigate how automated traders strategically select marketplaces and submit offers across multiple double auction marketplaces. We model the problem as a Bayesian game with traders that have continuous private values, and use fictitious play to analyse the traders’ Nash equilibrium market selection and bidding strategies. We do this for different trading environments (isolated, single-home, multi-home and hybrid) and different types of goods (independent, substitutable and complementary). We find that, in an isolated marketplace, the fictitious play algorithm converges to a Bayes–Nash equilibrium. In the single-home setting, all traders eventually converge to the same marketplace and the setting reduces to that of an isolated marketplace. In the multi-home setting with perfectly substitutable goods, buyers with high values only bid in one marketplace, whereas buyers with low values bid in multiple marketplaces. Then, for perfectly complementary goods, only buyers with high values bid in multiple marketplaces and buyers with low values enter no marketplaces. Finally, in the hybrid setting with perfectly complementary goods, traders choose no marketplaces. | An equilibrium analysis of trading across multiple double auction marketplaces using fictitious play |
S1567422316300229 | Customer churn in online firms is difficult to manage because customers are so fickle. The ability to detect churn in the early stage is something every online firm would wish to achieve. It represents both a potential revenue source and a cost-saving benefit. Churn prediction models attempt to organize customer behaviors, transactions and demographics to reduce the possibility of churn within a given time. However, most current methods depend on high-dimensionally static data analysis and the model parameters are estimated based on the massively customers. A dynamic and customized prediction model at the individual level cannot be achieved. This study proposes a novel mechanism based on the gamma CUSUM chart in which only inter-arrival time (IAT) and recency need to be collected, so that the customized parameters can be estimated for the purpose of individual monitoring. The data in this study are from an online dating website in Taiwan. The gamma CUSUM chart is compared with the exponential CUSUM chart of Gan (1994), CQC-v of Xie et al. (2002) and CQC of Chan et al. (2000). The results show that the accuracy rate (ACC) for a gamma CUSUM chart is 5.2% higher and the average time to signal (ATS) is about two days longer than required for the best CQC-v. | The gamma CUSUM chart method for online customer churn prediction |
S1567422316300230 | Gender theories developed from traditional fixed and negotiated price shopping have largely been untested in the context of online auctions. The present study intends to fill this gap. Specifically, this study compares male and female online bidders based on their motivations, psychographics, and purchasing behavior. Our results show that females are more likely than males to be enjoyment seekers, information seekers, bargain hunters, variety seekers, and impulsive buyers. Female online bidders also have a higher level of risk aversion and need for uniqueness, but exhibit a lower level of social interaction than males. This study also finds that males are more likely to purchase electronics and computers, whereas females are more likely to purchase books, clothing, jewelry, and toys through online auctions. Based on these results, theoretical and managerial implications are discussed. | Gender differences in online auctions |
S1567422316300242 | This study examines Gibrat’s law regarding size–growth relationships in the consumer-to-consumer (C2C) online marketplace. Using dynamic panel data models, we analyze 21,948 e-merchants from 14 industries on Taobao.com. The data analysis shows that Gibrat’s law holds for large and mature stores when their size and age exceed certain threshold, but it generally fails to apply to stores whose size and age are below certain threshold. For those small stores, they grow faster than large ones in the C2C e-commerce. Results of the study provide insights into the competitive dynamics and industry structure of the C2C online marketplace. | Size and growth dynamics of online stores: A case of China’s Taobao.com |
S1567422316300254 | Redress is concerned with internal complaint-handling procedures through which consumers seek compensation or to rectify the problems that occur during e-commerce transactions. It also serves as an important determinant of consumer confidence and trust. Studies of redress have received much attention, where the focus is largely in the context of traditional litigation procedures in offline business. This paper focuses on the types of redress procedures consumers expected in response to B2C e-commerce complaints, by analysing the experiences and viewpoint of a select group of online consumers and merchants located in Melbourne, Australia. The research reveals that when problems occur in B2C e-commerce transactions, consumers require and expect an immediate accessible and responsive redress procedure from merchants. This suggests that traditional litigation to seek redress is impractical and not a favourable option for consumers or merchants. | Redress procedures expected by consumers during a business-to-consumer e-commerce dispute |
S1568494613000744 | In the paper, two adaptive fuzzy control schemes including indirect and direct frameworks are developed for suppressing the wing-rock motion that is a highly nonlinear aerodynamic phenomenon in which limit cycle roll oscillations are experienced by aircraft at high angles of attack. In the two control topologies, a dynamic fuzzy system called Extended Sequential Adaptive Fuzzy Inference System (ESAFIS) is constructed to represent the dynamics of the wing-rock system. ESAFIS is an online learning fuzzy system in which the rules are added or deleted based on the input data. In the indirect control scheme, the ESAFIS is used to estimate the nonlinear dynamic function and then a stable indirect fuzzy controller is designed based on the estimator. In the direct control scheme, the ESAFIS controller is directly designed to imitate an ideal stable control law without determining the model of the dynamic function. Different from the original ESAFIS, the adaptive tuning algorithms for the consequent parameters are established in the sense of Lyapunov theorem to ensure the stability of the overall control system. A sliding mode controller is also designed to compensate for the modelling errors of ESAFIS by augmenting the indirect/direct fuzzy controller. Finally, comparisons with a neuron control scheme using the RBF network and a fuzzy control scheme with Takagi–Sugeno (TS) system are presented to depict the effectiveness of the proposed control strategies. Simulation results show that the proposed fuzzy controllers achieve better tracking performance with dynamically allocating the rules online. | Adaptive fuzzy control of aircraft wing-rock motion |
S1568494613000756 | A key challenge to most conventional clustering algorithms in handling many real world problems is that, data points in different clusters are often correlated with different subsets of features. To address this problem, subspace clustering has attracted increasing attention in recent years. In practical data mining applications, data points may arrive in continuous streams with chunks of samples being collected at different time points. In addition, huge amounts of data often cannot be kept in the main memory due to memory restriction. Accordingly, a range of evolving clustering algorithms has been proposed, however, traditional evolving clustering methods cannot be effectively applied to large-scale high dimensional data and data streams. In this study, we extend the online learning strategy and scalable clustering technique to soft subspace clustering to form evolving soft subspace clustering. We propose two online soft subspace clustering algorithms, OFWSC and OEWSC, and two streaming soft subspace clustering algorithms, SSSC_F and SSSC_E. The proposed evolving soft subspace clustering leverages on the effectiveness of online learning scheme and scalable clustering methods for streaming data by revealing the important local subspace characteristics of high dimensional data. Substantial experimental results on both artificial and real-world datasets demonstrate that our proposed methods are generally effective in evolving clustering and achieve superior performance over existing soft subspace clustering techniques. | Evolving soft subspace clustering |
S1568494613000884 | Profit based unit commitment problem (PBUC) from power system domain is a high-dimensional, mixed variables and complex problem due to its combinatorial nature. Many optimization techniques for solving PBUC exist in the literature. However, they are either parameter sensitive or computationally expensive. The quality of PBUC solution is important for a power generating company (GENCO) because this solution would be the basis for a good bidding strategy in the competitive deregulated power market. In this paper, the thermal generators of a GENCO is modeled as a system of intelligent agents in order to generate the best profit solution. A modeling for multi-agents is done by decomposing PBUC problem so that the profit maximization can be distributed among the agents. Six communication and negotiation stages are developed for agents that can explore the possibilities of profit maximization while respecting PBUC problem constraints. The proposed multi-agent modeling is tested for different systems having 10–100 thermal generators considering a day ahead scheduling. The results demonstrate the superiority of proposed multi-agent modeling for PBUC over the benchmark optimization techniques for generating the best profit solutions in substantially smaller computation time. profit of GENCO profit of GENCO in iteration ‘it’ revenue of GENCO total operation cost of GENCO profit generated by generator i at hour t power generation of generator i at hour t reserve generation of generator i at hour t ON/OFF status of generator i at hour t forecast demand at hour t forecast reserve at hour t demand left at hour t reserve left at hour t minimum generation limit of generator i maximum generation limit of generator i number of generator units number of hours forecast spot prices at hour t forecast reserve prices at hour t start-up cost of generator i probability that reserve is called and generated minimum up/down time of generator i duration during which unit i is continuously ON/OFF cost coefficients of generator i | Multi-agent modeling for solving profit based unit commitment problem |
S1568494613001075 | We investigate the optimization of transport routes of barge container ships with the objective to maximize the profit of a shipping company. This problem consists of determining the upstream and downstream calling sequence and the number of loaded and empty containers transported between any two ports. We present a mixed integer linear programming (MILP) formulation for this problem. The problem is tackled by the commercial CPLEX MIP solver and improved variants of the existing MIP heuristics: Local Branching, Variable Neighborhood Branching and Variable Neighborhood Decomposition Search. It appears that our implementation of Variable Neighborhood Branching outperforms CPLEX MIP solver both regarding the solution quality and the computational time. All other studied heuristics provide results competitive with CPLEX MIP solver within a significantly shorter amount of time. Moreover, we present a detailed case study transportation analysis which illustrates how the proposed approach can be used by managers of barge shipping companies to make appropriate decisions and solve real life problems. | Routing of barge container ships by mixed-integer programming heuristics |
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