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Despite the increasing relevance of online social interactions on platforms, there is still little research on the temporal interaction dynamics between electronic word-of-mouth (eWOM, a form of opinion-based social interaction), popularity information (a form of action-based social interaction), and consumer decision making. Drawing on a panel data set of more than 23,300 crowdfunding campaigns from Indiegogo, we investigate the dynamic effects of these social interactions on consumers' funding decisions using the panel vector autoregressive methodology. Our analysis shows that both eWOM and popularity information are critical influencing mechanisms in crowdfunding. However, our overarching finding is that eWOM surrounding crowdfunding campaigns on Indiegogo or Facebook has a significant yet substantially weaker predictive power than popularity information. We also find that whereas popularity information has a more immediate effect on consumers' funding behavior, its effectiveness decays rather quickly, while the impact of eWOM recedes more slowly. This study contributes to the extant literature by (1) providing a more nuanced understanding of the dynamic effects of opinion-based and action-based social interactions, (2) unraveling both within-platform and cross-platform dynamics, and (3) showing that social interactions are perceived as quality indicators on crowdfunding platforms that help consumers reduce risks associated with their investment decisions. These results can help platform providers and complementors to stimulate contribution behavior and increase the prosperity of a platform.

Popularity information is usually thought to reinforce existing sales trends by encouraging customers to flock to mainstream products with broad appeal. We suggest a countervailing market force: popularity information may benefit niche products with narrow appeal disproportionately, because the same level of popularity implies higher quality for narrow-appeal products than for broad-appeal products. We examine this hypothesis empirically using field experiment data from a website that lists wedding service vendors. Our findings are consistent with this hypothesis: narrow-appeal vendors receive more visits than equally popular broad-appeal vendors after the introduction of popularity information. This paper was accepted by Pradeep Chintagunta, marketing.

Information popularity prediction is a critical problem in social network analysis. With the increasing prevalence of social platforms, accurate prediction of the diffusion process has become increasingly important. Existing methods mainly rely on graph neural networks to model structural relationships, but they are often insufficient in capturing the complex interplay between temporal evolution and local cascade structures, especially in real-world scenarios involving sparse or rapidly changing cascades. To address this issue, we propose the Cascading Dynamic attention-calibrated Graph Convolutional Network, named CasDacGCN. It enhances prediction performance through spatiotemporal feature fusion and adaptive representation learning. The model integrates snapshot-level local encoding, global temporal modeling, cross-attention mechanisms, and a hypernetwork-based sample-wise calibration strategy, enabling flexible modeling of multi-scale diffusion patterns. Results from experiments demonstrate that the proposed model consistently surpasses existing approaches on two real-world datasets, validating its effectiveness in popularity prediction tasks.

Purpose With the widespread use of online communications, users are extremely vulnerable to a myriad of deception attempts. This study aims to extend the literature on deception in computer-mediated communication by investigating whether the manner in which popularity information (PI) is presented and media richness affects users’ judgments. Design/methodology/approach This study developed a randomized, within and 2 × 3 between-subject experimental design. This study analyzed the main effects of PI and media richness on the imitation magnitude of veracity judges and the effect of the interaction between PI and media richness on the imitation magnitude of veracity judges. Findings The manner in which PI is presented to people affects their tendency to imitate others. Media richness also has a main effect; text-only messages resulted in greater imitation magnitude than those viewed in full audiovisual format. The findings showed an interaction effect between PI and media richness. Originality/value The findings of this study contribute to the information systems literature by introducing the notion of herd behavior to judgments of truthfulness and deception. Also, the medium over which PI was presented significantly impacted the magnitude of imitation tendency: PI delivered through text-only medium led to a greater extent of imitation than when delivered in full audiovisual format. This suggests that media richness alters the degree of imitating others’ decisions such that the leaner the medium, the greater the expected extent of imitation.

According to historical retweet relationships that reveal public attention, information popularity prediction aims to forecast the incremental size of the given information cascades. Existing work independently models user dynamic preference with discrete cascade snapshots technology, they ignore the global structure of information cascades and inefficient tendency semantic representation, leading to suboptimal performance. To overcome the those issues, we introduce a novel T endency-Enhanced E volution G raph K AN T ransformer framework ( TEGKT ) , which is specifically tailored for information popularity prediction. To enhance the ability to express tendency semantics, we construct a tendency encoding learning module, which can effectively exhume the potential high-level dependency relationship among tendency semantics. To capture the global structure of cascade snapshots during the observation period, we design the evolution Graph KAN Transformer architecture to improve the expressive ability of information cascade representation, and its weighted parameter is optimized by gated recurrent units (GRU). Bi-directional gate recurrent units (Bi-GRU) are used to explore the dynamic evolution between cascade snapshots. Extensive experiments conducted on three public datasets show that the proposed model significantly outperforms the advanced methods, which achieves by 3.34% and 4.09% on the Weibo 0.5h dataset in terms of MSLE and MAPE evaluation metrics, respectively, validating its effectiveness. The research provides a better understanding of the laws of information diffusion.

As a kind of the most significantly popular information in markets, the sales ranking has great impacts on consumer choice. However, there are few discussions on how sales ranking should be provided to consumers in the literature. This paper aims to answer the following two questions: 1) To what extent does the sales ranking influence consumer choices; 2) When the sales ranking should be provided to consumers. To do so, this paper first constructs a sales ranking model and then provides detailed simulation experiments to demonstrate the model. The experimental results show that for markets where consumer preferences are dramatically different, such as music and movie markets, sales rankings do not have significant influences on consumer choices and should not be provided to consumers until a large number of early independent consumer choices have been accumulated. But for markets in which consumer preferences are similar, such as markets for official supplies, sales rankings have more influences on consumer choices and should be provided to consumers earlier. Furthermore, an evolution strategy is proposed to ascertain the most suitable sales rankings (characterised by suitable influence strength and suitable release time) for some specified online markets. The comparison results show that the optimized sales rankings not only can help consumers discover higher-quality products but also can improve overall sales.

Popularity information of products, frequently observed by consumers for making purchase decisions, has become an even more common point of reference in E-commerce where such information is readily updated. This study investigates how consumers interpret breadth of appeal and sales volume, which are two common kinds of popularity information that often co-exist on the Internet, with different inferences (quality evaluation or social comparison) under conditions when the two kinds of popularity information are congruent or incongruent. It is hypothesized that when a product’s breadth of appeal and sales volume are incongruent, the probability of purchasing a narrow-appeal product significantly increases compared to the condition of congruence; moreover, the magnitude of increase between the two conditions of congruency is higher for consumers with the inference of quality evaluation compared to that of social comparison. The method of laboratory experiment was adopted, with 200 participants. The empirical results strongly supported the proposed hypotheses and provided practical implications for e-commerce.

Popularity information of products, frequently observed by consumers for making purchase decisions, has become an even more common point of reference in E-commerce where such information is readily updated. This study investigates how consumers interpret breadth of appeal and sales volume, which are two common kinds of popularity information that often co-exist on the Internet, with different inferences (quality evaluation or social comparison) under conditions when the two kinds of popularity information are congruent or incongruent. It is hypothesized that when a product’s breadth of appeal and sales volume are incongruent, the probability of purchasing a narrow-appeal product significantly increases compared to the condition of congruence; moreover, the magnitude of increase between the two conditions of congruency is higher for consumers with the inference of quality evaluation compared to that of social comparison. The method of laboratory experiment was adopted, with 200 participants. The empirical resu

Since the content delivery unit over Information-Centric Networking (ICN) has shifted from files to the segments of a file named chunks, solely either file-level or chunk-level request probability is insufficient for ICN cache management. In this paper, a Request Expectation Index (RXI) based cache replacement algorithm for streaming content delivery is proposed. In this algorithm, RXI is introduced to serve as a fine-grained and unified estimation criteria of possible future request probability for cached chunks. RXI is customized for streaming content delivery by adopting both file-level and chunk-level request probability and considering the dynamically varied request status at each route as well. Compared to prior work, the proposed algorithm evicts the chunk with the minimum expectation of future request to maintain a high cache utilization. Additionally, simulation results demonstrate that the RXI-based algorithm can remarkably enhance the streaming content delivery performance and can be deployed in complex network scenarios. The proposed results validate that, by taking fine-grained request probability and request status into consideration, the customized in-network caching algorithm can improve the ICN streaming content delivery performance by high cache utilization, fast content delivery, and lower network traffic.