Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions

An online recommendation system (RS) involves using information technology and customer information to tailor electronic commerce interactions between a business and individual customers. Extant information systems (IS) studies on RS have approached the phenomenon from many different perspectives, and our understanding of the nature and impacts of RS is fragmented. The current study reviews and synthesizes extant empirical IS studies to provide a coherent view of research on RS and identify gaps and future directions. Specifically, we review 40 empirical studies of RS published in 31 IS journals and five IS conference proceedings between 1990 and 2013. Using a recommendation process theoretical framework, we categorize these studies in three major areas addressed by RS research: understanding consumers, delivering recommendations, and the impacts of RS. We review and synthesize the extant literature in each area and across areas. Based on the review and synthesis, we surface research gaps and provide suggestions and potential directions for future research on recommendation systems

WEBSITE GLOBALIZATION STRATEGY : A CROSS-CULTURAL ANALYSIS OF WEBSITE STRUCTURE

Master'sMASTER OF SCIENC

Does Pre-login Search Matter? Evidence from a Mobile Commerce Platform

An increasing number of consumers enjoy shopping through mobile devices. When consumers use a mobile app, they can choose whether to log in with their accounts. We argue that pre-login search plays a critical role in affecting consumers’ purchase decisions, although it has largely been overlooked in the literature. Using clickstream data, we adopt different econometric models to examine whether and how pre-login search affects the likelihood of purchase. Our results show that pre-login search behaviors are as important as post-login search to consumers’ purchase decisions. We also demonstrate that consumers’ purchase propensity increases at a diminishing rate with an increasing search effort during both pre- and post-login periods. Based on recommender systems (RSs) and paradox of choice theory, our results contribute to the burgeoning literature on consumer behavior in mobile commerce and provide novel insights to the strategic usage of RSs. Finally, we discuss theoretical and managerial implications

Evaluating recommender systems from the user's perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing method

User decision improvement and trust building in product recommender systems

As online stores are offering an almost unlimited shelf space, users must increasingly rely on product search and recommender systems to find their most preferred products and decide which item is the truly best one to buy. However, much research work has emphasized on developing and improving the underlying algorithms whereas many of the user issues such as preference elicitation and trust formation received little attention. In this thesis, we aim at designing and evaluating various decision technologies, with emphases on how to improve users' decision accuracy with intelligent preference elicitation and revision tools, and how to build their competence-inspired subjective constructs via trustworthy recommender interfaces. Specifically, two primary technologies are proposed: one is called example critiquing agents aimed to stimulate users to conduct tradeoff navigation and freely specify feedback criteria to example products; another termed as preference-based organization interfaces designed to take two roles: explaining to users why and how the recommendations are computed and displayed, and suggesting critique suggestions to guide users to understand existing tradeoff potentials and to make concrete decision navigations from the top candidate for better choices. To evaluate the two technologies' true performance and benefits to real-users, an evaluation framework was first established, that includes important assessment standards such as the objective/subjective accuracy-effort measures and trust-related subjective aspects (e.g., competence perceptions and behavioral intentions). Based on the evaluation framework, a series of nine experiments has been conducted and most of them were participated by real-users. Three user studies focused on the example critiquing (EC) agent, which first identified the significant impact of tradeoff process with the help of EC on users' decision accuracy improvement, and then in depth explored the advantage of multi-item strategy (for critiquing coverage) against single-item display, and higher user-control level reflected by EC in supporting users to freely compose critiquing criteria for both simple and complex tradeoffs. Another three experiments studied the preference-based organization technique. Regarding its explanation role, a carefully conducted user survey and a significant-scale quantitative evaluation both demonstrated that it can be likely to increase users' competence perception and return intention, and reduce their cognitive effort in information searching, relative to the traditional "why" explanation method in ranked list views. In addition, a retrospective simulation revealed its superior algorithm accuracy in predicting critiques and product choices that real-users intended to make, in comparison with other typical critiquing generation approaches. Motivated by the empirically findings in terms of the two technologies' respective strengths, a hybrid system has been developed with the purpose of combining them into a single application. The final three experiments evaluated its two design versions and particularly validated the hybrid system's universal effectiveness among people from different types of cultural backgrounds: oriental culture and western culture. In the end, a set of design guidelines is derived from all of the experimental results. They should be helpful for the development of a preference-based recommender system, making it capable of practically benefiting its users in improving decision accuracy, expending effort they are willing to invest, and even promoting trust in the system with resulting behavioral intentions to purchase chosen products and return to the system for repeated uses

When data changes pre-purchase behavior : the effects of information visualization on online information seeking

Online consumer information search became a crucial initial step in the purchase decision process. The objective of this dissertation is to investigate and measure the effects of different visual representations of information about products on individual’s behavior during pre-purchase online information seeking activities. More specifically, this dissertation analyze what type of information customers considered most important and pays more attention and what is the extent of the visual aspect and its impact in information seeking behavior. To do so, five experiments were conducted, three using online participants via Amazon Mechanical Turk, and two using participants in a laboratory setting, being collected biological measures in one of them. Through two studies, the first article shows how different degrees of evaluability of the same online review can influence on helpfulness, overestimation of information, and purchase intention. It also evidence individual’s involvement while browsing has a moderating role in the relation between evaluability and helpfulness as well as in the relation between evaluability and purchase intention. The second article analyze the relationship between depth-of-field and type of search on several behavioral outcomes, such as intention do revisit the website and visual appeal. It was also investigated whether or not involvement, expertise and attitude toward products moderates these relations. Drawing on the findings of the first and second articles, the third article focus on replicate the finding of the second article via biological measures using an eye-tracking device, including attention measures. The third article aims to contribute to online information seeking literature by investigating participant’s online search and browse behaviors and the resulting processing of information when viewing products presented visually differently in a webpage. These patterns of individual’s visualization studied in both three articles have important practical implications for the website design creating experiences that supports the type of information search undertaken by consumers

Effects of Animation on Attentional Resources of Online Consumers

Websites commonly use animation to capture the attentional resources of online consumers. While prior research has focused on the effects of animation on animated banner ads, limited research has examined the effects of animation on other items on the same webpage. Drawing from psychological theories that the amount of an individual’s attentional resources may vary under different conditions, this study focuses on the effects of animation on how individuals allocate attentional resources to both the animated item and the remaining non-animated items. We conducted an eye-tracking experiment to follow online consumers’ visual attention while they performed two types of online shopping tasks: browsing and searching tasks. The results showed that a product item that used animation led to increased visual attention to all items on a webpage, which suggests that the amount of attentional resources increases when a webpage includes animation. Meanwhile, animation influenced how individuals allocate their attentional resources such that it increased visual attention on the animated item at the expense of attention on nonanimated items on the same webpage. In addition, the type of shopping task moderated animation’s effect on how individuals allocate their attentional resources. Specifically, animation’s effect on attracting attentional resources to the animated item was stronger when online consumers browsed than when they searched for a specific target item. We discuss the theoretical and practical implications of our findings

LAW SEARCH IN THE AGE OF THE ALGORITHM

The process of searching for relevant legal materials is fundamental to legal reasoning. However, despite its enormous practical and theoretical importance, law search has not been given significant attention by scholars. In this Article, we define the problem of law search and examine the consequences of new technologies capable of automating this core lawyerly task. We introduce a theory of law search in which legal relevance is a sociological phenomenon that leads to convergence over a shared set of legal materials and explore the normative stakes of law search. We examine ways in which law scholars can understand empirically the phenomenon of law search, argue that computational modeling is a valuable epistemic tool in this domain, and report the results from a multi-year, interdisciplinary effort to develop an advanced law search algorithm based on human-generated data. Finally, we explore how policymakers can manage the challenges posed by new machine learning-based search technologies

Information, Technology and Information Worker Productivity

We study the fine-grained relationships among information flows, IT use, and individual information-worker productivity, by analyzing work at a midsize executive recruiting firm. We analyze both project-level and individual-level performance using: (1) direct observation of over 125,000 e-mail messages over a period of 10 months by individual workers (2) detailed accounting data on revenues, compensation, project completion rates, and team membership for over 1300 projects spanning 5 years, and (3) survey data on a matched set of the same workers’ IT skills, IT use and information sharing. These detailed data permit us to econometrically evaluate a multistage model of production and interaction activities at the firm, and to analyze the relationships among communications flows, key technologies, work practices, and output. We find that (a) the structure and size of workers’ communication networks are highly correlated with their performance; (b) IT use is strongly correlated with productivity but mainly by allowing multitasking rather than by speeding up work; (c) productivity is greatest for small amounts of multitasking but beyond an optimum, multitasking is associated with declining project completion rates and revenue generation; and (d) asynchronous information seeking such as email and database use promotes multitasking while synchronous information seeking over the phone shows a negative correlation. Overall, these data show statistically significant relationships among social networks, technology use, completed projects, and revenues for project-based information workers. Results are consistent with simple production models of queuing and multitasking and these methods can be replicated in other settings, suggesting new frontiers for bridging the research on social networks and IT value.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc