Peer influence and homophily are two entangled forces underlying social influences. However, distinguishing homophily from peer influence is difficult, particularly when there is latent homophily caused by unobservable features. This paper proposes a novel data-driven framework that combines the advantages of latent homophily identification and causal inference. Specifically, the approach first utilizes scalable network representation learning algorithms to obtain node embeddings, which are extracted from social network structures. Then, the embeddings are used to control latent homophily in a quasi-experimental design for causal inference. The simulation experiments show that the proposed approach can estimate peer influence more accurately than existing parameterized approaches and data-driven methods. We applied the proposed framework in an empirical study of players’ online gaming behaviors. First, our approach can achieve improved model fitness for estimating peer influence in online games. Second, we discover a heterogeneous effect of peer influence: players with higher tenure and playing levels receive stronger peer influence. Finally, our results suggest that the homophily effect has a stronger influence on players’ behavior than peer influence. Summary of Contribution: The study proposes a novel computational method to separate peer influence from homophily in an online network. Using network embeddings learned from data to control latent homophily, the approach effectively addresses the challenge of correctly identifying peer effects in the absence of randomized experimental conditions. While simplifying the computational process, the method achieves good computational performance, thus effectively helping researchers and practitioners extract useful network information in various online service contexts.

OBJECTIVE: Although artificial intelligence (AI) has achieved high levels of accuracy in the diagnosis of various diseases, its impact on physicians' decision-making performance in clinical practice is uncertain. This study aims to assess the impact of AI on the diagnostic performance of physicians with differing levels of self-efficacy under working conditions involving different time pressures. MATERIALS AND METHODS: A 2 (independent diagnosis vs AI-assisted diagnosis) × 2 (no time pressure vs 2-minute time limit) randomized controlled experiment of multicenter physicians was conducted. Participants diagnosed 10 pulmonary adenocarcinoma cases and their diagnostic accuracy, sensitivity, and specificity were evaluated. Data analysis was performed using multilevel logistic regression. RESULTS: One hundred and four radiologists from 102 hospitals completed the experiment. The results reveal (1) AI greatly increases physicians' diagnostic accuracy, either with or without time pressure; (2) when no time pressure, AI significantly improves physicians' diagnostic sensitivity but no significant change in specificity, while under time pressure, physicians' diagnostic sensitivity and specificity are both improved with the aid of AI; (3) when no time pressure, physicians with low self-efficacy benefit from AI assistance thus improving diagnostic accuracy but those with high self-efficacy do not, whereas physicians with low and high levels of self-efficacy both benefit from AI under time pressure. DISCUSSION: This study is one of the first to provide real-world evidence regarding the impact of AI on physicians' decision-making performance, taking into account 2 boundary factors: clinical time pressure and physicians' self-efficacy. CONCLUSION: AI-assisted diagnosis should be prioritized for physicians working under time pressure or with low self-efficacy.

Scientific collaboration is essential and almost imperative in modern science. However, collaboration may be difficult to achieve because of 2 major barriers: geographic distance and social divides. It is predicted that the advancement of information communication technologies (ICTs) will bring a puzzled conclusion for collaboration in the scientific community: the “Global Village” trend with significantly increased physical distance among collaborated scientists and the “Virtual Balkans” trend with significantly increased social stratification among collaborated scientists. The results of this study reveal that the scientific community evolves towards the Global Village generally on both the geographic and social dimension, but with variations in term of collaboration patterns. The influence of such collaboration patterns on research performance (that is, productivity and impact), however, is asymmetric to each side of collaborators. When researchers from top‐tier and general‐tier institutions collaborate, researchers from top‐tier institutions face a decrease in research productivity and impact, whereas researchers from general‐tier institutions increase in research productivity and impact. Furthermore, the development of ICTs plays an important role in shaping the evolving trends and moderating effects of collaboration patterns. Our findings provide a comprehensive understanding of scientific collaboration in the geographic, social, and technological aspect.

Most inventory models in the literature assume that demands are independent among different time periods. However, a number of recent studies suggest that demands are often correlated over different time periods, which motivates our work here. In this paper, we study a class of periodic review (s, S) inventory systems in which demands are correlated and modeled as a Markov-modulated process. Using a Maclaurin series analysis and a Pade approximation, as well as an infinite system of linear equations, we develop algorithms to calculate the moments of the inventory level based on which various performance measures of the system can also be evaluated. Numerical experiments show that our approach is quite efficient and provides accurate estimates for the moments of the inventory level and other related performance measures.

This research addresses the theoretically neglected question of how the internal diffusion of inter-organizational systems (IOS) into a firm's activities and its external diffusion into the supply chain partners influence performance improvement. Drawing on the resource-based view, our research model posits that organizational and relational resources affect both internal and external diffusion, which in turn, influence performance improvement. Survey results from 187 managers in Korean and Chinese firms showed that while the impact of organizational resources on a firm's performance improvement was fully mediated by IOS diffusion, the diffusion partially mediates the impact of relational resources on performance improvement. This study also revealed a significantly different pattern of diffusion between Korean and Chinese firms, i.e. showing the impact of organizational and relational resources on a firm's performance through external diffusion of IOS are significantly greater in Korean firms, while the impact of internal diffusion was significantly greater in Chinese firms. In other words, Korean firms tend to externally diffuse IOS toward their business partners, while Chinese firms tend to internally diffuse IOS by deploying IOS from their partners. The implications of these findings for both research and practice are discussed.

We develop and test a theoretical model to investigate the adoption of government-to-government (G2G) information systems in public administration organizations. Specifically, this model explains how top management commitment (TMC) mediates the impact of external institutional pressures on internal organizational resource allocation, which finally leads to the adoption decision. The hypotheses were tested using survey data from public administration organizations in China. Results from partial least squares analyses suggest that coercive and normative pressures positively affect TMC, which then positively affects financial and information technology (IT) human resources in the G2G adoption process. In turn, financial and IT human resources are confirmed to positively affect the intention to adopt G2G. Surprisingly, we do not find support for our hypothesis that mimetic pressures directly influence TMC. Rather, a post hoc analysis implies that mimetic pressures indirectly influence TMC via the influence of coercive pressures. Our findings provide important managerial implications for public administration organizations.

In organizations, the amount of attention that user‐generated knowledge receives in knowledge management systems (KMSs) may not imply its potential for benefiting organizational activities in terms of accelerating innovation and product development. To optimize the utilization of knowledge in organizations, it is crucial to identify factors that influence knowledge popularity. From a network perspective, this study proposes a model to evaluate knowledge popularity by investigating 2 attributes of contextual information (i.e., authors and tags) that are embedded in a heterogeneous knowledge network, and how they interact to impact knowledge popularity. Objective data obtained through the interaction history of a KMS in a global telecommunication company was applied to test the hypotheses. This paper contributes to the extant literature on knowledge popularity by identifying contextual attributions of knowledge, and empirically tests the impact of their interactions on knowledge popularity.

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.

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.

The study develops an integrated model to examine factors, particularly government factors, which influence IT adoption in Chinese firms. By analyzing the survey data from 1540 firms across 14 industries and across various ownerships in Shanghai, the study contributes several insights into firms’ IT usage. First of all, this study sheds light on the IT adoption in Chinese firms and validates the general route from IT infrastructure construction to value realization. Second, the findings suggest that government actions influence firms’ IT infrastructure development and IT management decision. However, there is no evidence showing the government impact on firms’ IT usage. The study also provides valuable IT adoption implications to firms in China, particularly to those in the modern cities like Shanghai.