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The authors test five theoretically derived hypotheses about what drives video ad sharing across multiple social media platforms. Two independent field studies test these hypotheses using 11 emotions and over 60 ad characteristics. The results are consistent with theory and robust across studies. Information-focused content has a significantly negative effect on sharing, except in risky contexts. Positive emotions of amusement, excitement, inspiration, and warmth positively affect sharing. Various drama elements such as surprise, plot, and characters, including babies, animals, and celebrities arouse emotions. Prominent (early vs. late, long vs. short duration, persistent vs. pulsing) placement of brand names hurts sharing. Emotional ads are shared more on general platforms (Facebook, Google+, Twitter) than on LinkedIn, and the reverse holds for informational ads. Sharing is also greatest when ad length is moderate (1.2 to 1.7 minutes). Contrary to these findings, ads use information more than emotions, celebrities more than babies or animals, prominent brand placement, little surprise, and very short or very long ads. A third study shows that the identified drivers predict sharing accurately in an entirely independent sample.

Agents have been claimed to be universally more prominent than verbal arguments with other thematic roles. Perhaps the strongest claim in this regard is that agents have a privileged role in language processing, specifically that there is a universal bias for the first unmarked argument in an utterance to be interpreted as an agent. Symmetrical voice languages such as many western Austronesian languages challenge claims about agent prominence in various ways. Inter alia, most of these languages allow for both ‘agent-first’ and ‘undergoer-first’ orders in basic transitive constructions. We argue, however, that they still provide evidence for a universal ‘agent-first’ principle. Inasmuch as these languages allow for word-order variation beyond the basic set of default patterns, such variation will always result in an agent-first order. Variation options in which undergoers are in first position are not attested. The fact that not all transitive constructions are agent-first is due to the fact that there are competing ordering biases, such as the principles dictating that word order follows constituency or the person hierarchy, as also illustrated with Austronesian data.

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.

Online communities have suffered from their members' intermittent, dormant, or nonexistent participation. We propose that prominence, which refers to the salience of community members' psychological proximity to their community, differs from the engagement construct, which denotes a psychological dedication to behave prosaically toward other community members. Whereas engagement has been increasingly examined as a driver of online community behavior, the role of prominence has received a minimal amount of attention in the literature. Drawing on self-determination theory, we developed a framework that proposes the prominence construct as a phenomenon distinctive from engagement in its nature, formation, and behavioral outcomes. Our findings based on two studies indicate that the proposed model with prominence performs considerably better than the existing model with only engagement. Our conceptual model contributes to Information Systems research by laying a strong theoretical foundation to differentiate between the behavioral paths of the autonomous prominence construct and its controlled engagement counterpart.

As the complexity and unpredictability of cyber-physical systems (CPSs) such as multi-agent robotic networks increase, having robust predictive models is crucial for ensuring dependable operations. This paper presents a modification of the Strength Prominence (SP) index, which was initially designed for fuzzy social networks, adapted for use in robotic and intelligent automation systems. The SP index has been reformulated for fuzzy interaction graphs, where nodes signify robotic components and edges represent uncertain communications or dependencies. The modified index assesses link probability by considering the strength of connectedness and prominence levels, even in the absence of common neighbors. Theoretical aspects such as symmetry, boundedness, and monotonicity are thoroughly demonstrated. Empirical validation utilizing real-world datasets and ROS-based robotic data shows that the SP index achieves superior predictive accuracy, surpassing traditional fuzzy indices like CN, RSM, and CAR in terms of precision, AUC, and AUP measurements. This method allows for the early identification of interaction failures, improves the prediction of collaboration, and aids in the development of fault-tolerant designs. This proposed approach provides a new interdisciplinary tool for fuzzy link prediction in CPS, with important implications for the design of autonomous systems, real-time robotic collaboration, and resilient network structures.

Small-scale, cyclic, transverse motions of plasma threads are usually seen in solar prominences, which are often interpreted as magnetohydrodynamic (MHD) waves. Here, we observed small-scale decayless transverse oscillations in a quiescent prominence, and they appear to be omnipresent. The oscillatory periods of the emission intensity and a proxy for the line-of-sight Doppler shift are about half period of the displacement oscillations. This feature agrees well with the fast kink-mode waves in a flux tube. All the moving threads oscillate transversally spatially in phase and exhibit no significant damping throughout the visible segments, indicating that the fast kink MHD waves are persistently powered and ongoing dissipating energy is transferred to the ambient plasma in the quiet corona. However, our calculations suggest that the energy taken by the fast kink MHD waves alone can not support the coronal heating on the quiet Sun.

When the amplitude of the laser Doppler signal is less than that of the noise amplitude, the speed information cannot be identified. In this paper, the recognition of weak Doppler signals is accomplished by superimposing multiple-frame Doppler signals in the frequency domain and eliminating the base noise. Meanwhile, the relative prominence is defined to assess the quality of Doppler signals. The theoretical analysis and experimental results demonstrate that the relative prominence can be enhanced by more than two orders of magnitude through the stacking of 10,000 frames of Doppler signals. Moreover, this paper establishes a foundation for determining the threshold of relative prominence in practical measurements. When the relative prominence at a specific point on the spectrum is 3, there is at least a 99.08% probability that this point corresponds to the target signal.

Research summary: Emerging markets are characterized by underdeveloped institutions and frequent environmental shifts. Yet, they also contain many firms that have survived over generations. How are firms in weak institutional environments able to persist over time? Motivated by 69 interviews with leaders of emerging market firms with histories spanning generations, we combine induction and deduction to propose reputation as a meta‐resource that allows firms to activate their conventional resources. We conceptualize reputation as consisting of prominence, perceived quality, and resilience, and develop a process model that illustrates the mechanisms that allow reputation to facilitate survival in ways that persist over time. Building on research in strategy and business history, we thus shed light on an underappreciated strategic construct (reputation) in an undertheorized setting (emerging markets) over an unusual period (the historical long run). Managerial summary: Why are some firms able to persistently survive in challenging, uncertain, and underdeveloped business environments? To explore this question, we analyze in‐depth interviews with leaders of emerging market firms that have survived over decades and even centuries. We find that firm reputation is a key strategic driver, and propose new ideas about the ways through which reputation facilitates survival. We elaborate how a favorable reputation allows a firm to more fully utilize its existing resources by decreasing uncertainty. We also propose that reputation has offensive and defensive properties that make it valuable to firms during both positive and negative economic cycles. Finally, we discuss why a reputation‐based source of competitive advantage is hard to imitate, and outline three general approaches for building reputation. Copyright © 2017 John Wiley & Sons, Ltd. Video

In pursuit of notable reputations, organizations commonly engage their stakeholders using various online mechanisms. While \"reputation signaling\" via online user engagement is commonplace, its implications in the context of user-generated content (UGC) remain largely unaddressed. This study empirically explores the impact of UGC on hospital reputational dynamics. A unique panel dataset was analyzed using a lagged model approach to determine the effects of signals of quality, awareness, and content variance on hospital prominence and quality as well as the impact of reputation on financial performance. The findings suggest that quality signals significantly impact patient-perceived quality ratings, and signals of awareness and content variance significantly impact prominence rating. Also, in contrast to prominence, perceived quality significantly impacts financial performance. Given the ready availability of user-generated feedback, these findings can inform patient-provider online engagement strategies and related budget allocations. Furthermore, they highlight the unique role of Facebook ratings as an antecedent to quality and prominence dimensions of reputation.

Information-sharing social networks support the adaptive capacity of small-scale fishers in the face of social and environmental change by allowing them to increase access to unique knowledge critical to their fishing success. The facilitation of information exchange may be supported and influenced by persons in key positions. Within these networks, centralized actors often control the flow and access to information. We take a descriptive approach to explore the relationship between fishing role and actor prominence within information-sharing networks in Jamaica. We hypothesized that fishing captains – given their perceived legitimacy and formal and informal authority – would be more prominent in information-sharing networks, and the information they shared would be perceived as more trustworthy and influential than that of non-captains. We collected personal social networks of fishers (n = 353) on 20 fishing beaches across four parishes in Jamaica using structured questionnaires. We found low centralization and density scores across the parishes, suggesting an even distribution of actor centrality. Our results show that non-captains play a more prominent role in information sharing than fishing captains in one parish suggesting that captains and non-captains play similar roles in facilitating information, and that differences lie in whether fishers perceive the shared information as trustworthy and influential in their fishing decisions and not the prominence of the actor. These findings contribute to understanding the various adaptive strategies fishers develop to meet growing social-ecological changes in small-scale fisheries. Identifying key informants in prominent positions can also support the development of more effective strategies to communicate and share information across communities.