Abstract Does a query image with much higher resolution than that of the gallery image also affect the pedestrian re‐identification performance? If so, and how does it affect performance? The study proposes a novel framework for performing high‐resolution image reconstruction and pedestrian re‐identification tasks, independent of the query image resolution. More precisely, an end‐to‐end trainable Resolution Independent person Re‐identification network is proposed. It is composed of our designed Cross‐Resolution GAN and Embedding Batch Normalisation layers. The model is then compared with the traditional low‐resolution pedestrian recognition algorithm and the hybrid method of high‐resolution reconstruction and pedestrian re‐identification. The results demonstrate that the proposed method outperforms the state‐of‐the‐art methods in the pedestrian re‐identification task based on our expanded benchmark dataset. It also reaches an equivalent performance to the existing methods in the high‐resolution image reconstruction task.

The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to financial inclusion. It brings non-trivial social benefits of diminishing financial inequality. However, the technical challenges in individual financial risk evaluation exacerbated by the unforeseen user characteristic distribution and limited credit history of new users, as well as the inexperience of newly-entered companies in handling complex data and obtaining accurate labels, impede further promotion of financial inclusion. To tackle these challenges, this paper develops a novel transfer learning algorithm (i.e., TransBoost) that combines the merits of tree-based models and kernel methods. The TransBoost is designed with a parallel tree structure and efficient weights updating mechanism with theoretical guarantee, which enables it to excel in tackling real-world data with high dimensional features and sparsity in O(n) time complexity. We conduct extensive experiments on two public datasets and a unique largescale dataset from Tencent Mobile Payment. The results show that the TransBoost outperforms other state-of-the- art benchmark transfer learning algorithms in terms of prediction accuracy with superior efficiency, demonstrate stronger robustness to data sparsity, and provide meaningful model interpretation. Besides, given a financial risk level, the TransBoost enables financial service providers to serve the largest number of users including those who would otherwise be excluded by other algorithms. That is, the TransBoost improves financial inclusion.

In online sharing economy platforms, users often need to cooperate with strangers with minimal information. When personal photos are available on these platforms, how do users form social perceptions and consequently cooperative intention? According to the stimulus-organism-response (S-O-R) model and the kin selection theory, the authors propose that facial resemblance between two partners affects their perceptions of the cognitive trustworthiness, affective trustworthiness, attractiveness, and sociability of the partner, and that these social perceptions affect their cooperative intention. In the experiment, subjects evaluate the face of five potential partners of various levels of facial resemblance. The results support that the effect of facial resemblance on cooperative intention is mediated by social perceptions. Moreover, the transactional values and social values of the task partially moderate the effect of facial resemblance on social perceptions for female subjects. Sharing economy platforms may leverage the facial resemblance to facilitate cooperation among strangers.

Does patients’ Internet search of disease information affect their trust in physicians during diagnosis? This study proposes a research model from a knowledge power perspective, that is, Internet search affects patients’ perception of their knowledge level. Our empirical study of more than 400 subjects suggests that for patients who searched online for disease information, the inconsistency between their self‐diagnosis expectations and their physician's diagnosis reduces their trust in their physician. The effect is stronger for those who spent more time on Internet search. Patients with chronic conditions are less affected by the inconsistency, as are patients of physicians with a higher professional status. This study also found that physicians’ interaction quality in the diagnosis process—how well they communicate with their patient—still plays a dominant role in gaining patient's trust. This finding suggests that even in the high‐tech age, high‐touch remains an important factor to physician‐patient trust.

In the age of mobile commerce, users receive floods of commercial messages. How do users judge the relevance of such information? Is their relevance judgment affected by contextual factors, such as location and time? How do message content and contextual factors affect users' privacy concerns? With a focus on mobile ads, we propose a research model based on theories of relevance judgment and mobile marketing research. We suggest topicality, reliability, and economic value as key content factors and location and time as key contextual factors. We found mobile relevance judgment is affected mainly by content factors, whereas privacy concerns are affected by both content and contextual factors. Moreover, topicality and economic value have a synergetic effect that makes a message more relevant. Higher topicality and location precision exacerbate privacy concerns, whereas message reliability alleviates privacy concerns caused by location precision. These findings reveal an interesting intricacy in user relevance judgment and privacy concerns and provide nuanced guidance for the design and delivery of mobile commercial information.

To develop a general mathematical model for social networks is one of the fundamental tasks currently on demand within social network research. Ignoring the strength of the relationships, existing social network models simply use a Boolean value to describe the existence of relationships between peers. This shortage can be overcome by importing repeated social interactions into the model and building the model on a path-based link analysis. In doing this, the authors developed a new semi-random graph model, which offers a general description of the evolution of social networks, with substantial power, to the well accepted hypothesis of preferential attachment in social networks. In addition to these theoretical results, the authors created a quantitative description of the bonding role of social relationship in networks, a parameter within the model denoted as V. Empirical results indicate that the presented model has a degree of distribution in line with those of real-world networks, which is superior to those of major existing models, and the parameter V, which essentially represents the cohesiveness of social networks, makes an ideal indicator for the cohesion in social networks.

An important question in information-seeking behavior is where people go for information and why information seekers prefer to use one source type rather than another when faced with an information-seeking task or need for information. Prior studies have paid little attention to contingent variables that could change the cost-benefit calculus in source use. They also defined source use in one way or the other, or considered source use as a monolithic construct. Through an empirical survey of 352 working professionals in Singapore, this study carried out a context-based investigation into source use by information seekers. Different measures of source use have been incorporated, and various contextual variables that could affect the use of source types have been identified. The findings suggest that source quality and access difficulty are important antecedents of source use, regardless of the source type. Moreover, seekers place more weight on source quality when the task is important. Other contextual factors, however, are generally less important to source use. Seekers also demonstrate a strong pecking order in the use of source types, with online information and face-to-face being the two most preferred types.

Abstract An innovative model to measure the influence among scientific journals is developed in this study. This model is based on the path analysis of a journal citation network, and its output is a journal influence matrix that describes the directed influence among all journals. Based on this model, an index of journals' overall influence, the quality‐structure index (QSI), is derived. Journal ranking based on QSI has the advantage of accounting for both intrinsic journal quality and the structural position of a journal in a citation network. The QSI also integrates the characteristics of two prevailing streams of journal‐assessment measures: those based on bibliometric statistics to approximate intrinsic journal quality, such as the Journal Impact Factor, and those using a journal's structural position based on the PageRank‐type of algorithm, such as the Eigenfactor score. Empirical results support our finding that the new index is significantly closer to scholars' subjective perception of journal influence than are the two aforementioned measures. In addition, the journal influence matrix offers a new way to measure two‐way influences between any two academic journals, hence establishing a theoretical basis for future scientometrics studies to investigate the knowledge flow within and across research disciplines.

This paper explores the diffusion of competitive Internet technology products in the context of competition between local and multinational corporations as well as how the diffusive interactions between technologies affect their dominance in electronic markets. Drawing on existing theories of innovation diffusion, and competitive dynamics, the authors adopted a new diffusion model that incorporates the influence of one technology’s adoption on the diffusion of other technology. The authors then validated the model using longitudinal field data of the two pairs of Internet technology products in Chinese electronic markets. The findings of this investigation suggest that Internet product diffusion can be better predicted by a competitive dynamic model than by an independent-diffusion-process model. Further, results indicate that the diffusive interaction between local and multinational corporations’ technologies can be a two-way asymmetric interaction. Such a pattern supports a conclusion of significant second-mover advantage for local online vendors in fast-growing emerging markets. The authors also examine the policy implications of these results, specifically with respect to how asymmetric interaction effects can help domestic online vendors gain second-mover advantage facing the entry of multinational corporations.

One common information display design that appears in nearly all electronic shopping sites is product lists on Web pages. Many electronic shopping sites provide sorting functions for product quality attributes. However, the extant literature has not been particularly insightful on how consumers respond to product lists sorted in different orders (i.e., ascending, descending, and random). Despite the intuitive postulation that products appearing in an early position of a list may draw more attention, it is not evident whether and how different sorting of products in a list affect consumers' purchase decisions. The purpose of this research is to investigate how product sorting influences consumers' decision making. Specifically, the focus is on the effect of sorting by product quality attributes on the importance of quality and price in consumers' product choice and on the formation of their consideration set. Results from the experiment show that, when product quality and price are positively but not perfectly correlated, individuals perceive quality to be more important and they tend to include products with higher quality in their consideration set when they are exposed to a descending list than when they are exposed to a random or ascending list. Therefore, online vendors could apply descending sorting to promote the sales of high-quality products.

Abstract The order effect of relevance judgment refers to the different relevance perceptions of a document when it appears in different positions in a list. Although the order effect of relevance judgment has significant theoretical and practical implications, the extant literature is inconclusive regarding its existence and forming mechanisms. This study proposes a set of order effect forming mechanisms, including the learning effect, the subneed scheduling effect, and the cursoriness effect based on the conceptualization of dynamic relevance and the psychology of cognitive elaboration. Our empirical study indicates that in an interactive information retrieval setting, when a document list is reasonably long, order effects demonstrate a curvilinear pattern that conforms to the combined effect of the three mechanisms. Moreover, the curvilinear pattern of order effect could differ for documents of different relevance levels.

Abstract The information science research community is characterized by a paradigm split, with a system‐centered cluster working on information retrieval (IR) algorithms and a user‐centered cluster working on user behavior. The two clusters rarely leverage each other's insight and strength. One major suggestion from user‐centered studies is to treat the relevance judgment of documents as a subjective, multidimensional, and dynamic concept rather than treating it as objective and based on topicality only. This study explores the possibility to enhance users' topicality‐based relevance judgment with subjective novelty judgment in interactive IR. A set of systems is developed which differs in the way the novelty judgment is incorporated. In particular, this study compares systems which assume that users' novelty judgment is directed to a certain subtopic area and those which assume that users' novelty judgment is undirected. This study also compares systems which assume that users judge a document based on topicality first and then novelty in a stepwise, noncompensatory fashion and those which assume that users consider topicality and novelty simultaneously and as compensatory to each other. The user study shows that systems assuming directed novelty in general have higher relevance precision, but systems assuming a stepwise judgment process and systems assuming a compensatory judgment process are not significantly different.

Abstract Research in information science now regards users' relevance judgment as subjective perception. However, user‐centered studies in the extant literature mainly focus on relevance judgment in problem solving contexts in which the situational relevance of a document is the main concern for users. This study investigates users' relevance judgment in non‐problem‐solving contexts, i.e., when users search information for epistemic value or entertainment. It is posited that informative relevance and affective relevance should be the main concerns for users. Based on H. P. Grice's (1975, 1989) communication theory and Y. Xu and Z. Chen's (2006) framework, this study tests the significance of topicality, novelty, reliability, understandability, and scope to informative relevance and affective relevance in non‐problem‐solving contexts. This empirical study finds novelty, reliability, and topicality to be key aspects of informative relevance.

Abstract Human information‐seeking behavior is a topic of increasing interest in many disciplines. However, the dynamics of this behavior remain elusive. The extant research has taken cognitive and behavioral perspectives to study information‐seeking behavior, and observed its dynamics in multiple sessions. However, the underlying mechanisms that govern the dynamics of information‐seeking behavior are not well understood. With a focus on interactive information retrieval behavior, this study proposes an integrated framework based on activity theory. This framework is not only comprehensive and integrated, but also offers an explanation of the mechanisms governing the interaction between users' cognitive states and their manifested behavior when using an information retrieval system. A set of four propositions are advanced to describe the mechanisms. The implications are discussed.

Abstract Human information‐seeking behavior is complicated. Activity theory is a powerful theoretical instrument to untangle the “complications.” Based on activity theory, a comprehensive framework is proposed in Part I (Y. Xu, 2007) of this report to describe interactive information retrieval (IIR) behavior. A set of propositions is also proposed to describe the mechanisms governing users' cognitive activity and the interaction between users' cognitive states and manifested retrieval behavior. An empirical study is carried out to verify the propositions. The authors' experimental simulation of 81 participants in one search session indicates the propositions are largely supported. Their findings indicate IIR behavior is planned. Users adopt a divide‐and‐conquer strategy in information retrieval. The planning of information retrieval activity is also partially manifested in query revision tactics. Users learn from previously read documents. A user's interaction with a system ultimately changes the user's information need and the resulting relevance judgment, but the dynamics of topicality perception and novelty perception occur at different paces.

Abstract How does an information user perceive a document as relevant? The literature on relevance has identified numerous factors affecting such a judgment. Taking a cognitive approach, this study focuses on the criteria users employ in making relevance judgment beyond topicality. On the basis of Grice's theory of communication, we propose a five‐factor model of relevance: topicality, novelty, reliability, understandability, and scope. Data are collected from a semicontrolled survey and analyzed by following a psychometric procedure. Topicality and novelty are found to be the two essential relevance criteria. Understandability and reliability are also found to be significant, but scope is not. The theoretical and practical implications of this study are discussed.

Abstract Information seeking behavior is an important form of human behavior. Past literature in information science and organizational studies has employed the cost‐benefit framework to analyze Seekers' information‐source choice decision. Conflicting findings have been discovered with regard to the importance of source quality and source accessibility in Seekers' choices. With a focus on interpersonal task information seeking, this study proposes a seeker‐source‐information need framework to understand the source choice decision. In this framework, task importance, as an attribute of information need, is introduced to moderate Seekers' cost‐benefit calculation. Our empirical study finds that in the context of interpersonal task information seeking, first, the least effort principle might not be adequate in explaining personal source choices; rather, a quality‐driven perspective is more adequate, and cost factors are of much less importance. Second, the seeker‐source relationship is not significant to source choices. Third, the nature of information need, especially task importance, can modify Seekers' source choice decisions.