Investigating narrative involvement,parasocial interactions,and impulse buying behaviours within a second screen social commerce context

Using mobile devices for additional information search before, during and after watching television (either factual news or fictional TV series) - is an increasingly significant information management phenomenon. This activity has been recognised by both TV media executives and academia as ‘second screen’ activity. This paper develops and tests a ‘parasocial interaction-impulse buying’ - model that captures the behaviours of consumers watching a Chinese TV drama series. These audience-consumers were also engaging extensively (most on a daily basis) during the broadcast period with series related influencer social media activities on a second screen. A structural equation model was developed to analyse the data. Findings indicate that TV series audience consumers who are also highly engaged with influencer- consumer ‘second screen’ content are likely to experience positive narrative involvement leading to engagement in parasocial interaction behaviours and ultimately impulse buying behaviour. These findings have implications for managers by providing insights into (1) the effects of influencer second screen content (2) the processes by which consumers’ involvement in a TV show’s narrative leads to impulse buying of products. Thus, influencer related second screen consumer engagement should be considered a significant opportunity for marketers, as such activity makes consumers become more involved in the characters and the narrative of a particular TV show, leading to increased impulse buying.     

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.     

科技资源开放共享平台创新扩散的关键因素研究——基于TOE理论框架

开放共享平台是科技资源领域的一项重要技术创新、知识创新和管理创新,其创新扩散受到各种关键因素的影响。以创新扩散理论和TOE理论框架为基础,深入分析技术、组织和环境等各种关键因素对开放共享平台采纳和扩散的影响。通过建立的关键因素模型,研究结果表明：平台基础、复杂性、兼容性、管理体制、保障体系、市场环境和政策法规是影响科技资源开放共享平台进一步扩散的关键因素,而组织因素的人才体系和环境因素的社会环境目前对开放共享平台扩散并没有显著影响。在此基础上提出了有利于科技资源开放共享平台进一步扩散的相关政策与建议。     

Towards early identification of online rumors based on long short-term memory networks

In the social media environment, rumors are constantly breeding and rapidly spreading, which has become a severe social problem, often leading to serious consequences (e.g., social panic and even chaos). Therefore, how to identify rumors quickly and accurately has become a key prerequisite for taking effective measures to curb the spread of rumors and reduce their influence. However, most existing studies employ machine learning based methods to carry out automatic rumor identification by extracting features of rumor contents, posters, and static spreading processes (e.g., follow-ups, thumb-ups, etc.) or by learning the presentation of forwarding contents. These studies fail to take into account the dynamic differences between the spreaders and diffusion structures of rumors and non-rumors. To fill this gap, this paper proposes Long Short-Term Memory (LSTM) network based models for identifying rumors by capturing the dynamic changes of forwarding contents, spreaders and diffusion structures of the whole (in the afterwards identification mode) or only the beginning part (in the halfway identification mode, i.e., early rumor identification) of the spreading process. Experiments conducted on a rumor and non-rumor dataset from Sina Weibo show that the proposed models perform better than existing baselines.     

重大突发事件政府新媒体舆论场谣言的传播、扩散与消减模型

【目的/意义】重大突发事件政府新媒体舆论场谣言的广泛传播,会造成社会恐慌,激化社会矛盾,影响社会稳定团结。为此,需要对舆论场中的谣言进行研究,促使其尽快消减,使其不利影响最小化。【方法/过程】首先,本文在对政府新媒体舆论场谣言类型与谣言传播原因分析的基础上,将谣言传播划分为产生、扩散、高潮、消减与平息等五个环节。然后,分析政府新媒体舆论场谣言传播过程中居民情绪与媒体素养对于谣言传播的影响,结合谣言本体的重要性与模糊性,构建政府新媒体舆论场谣言的传播、扩散与消减模型。最后,以新冠疫情为例,对所构建的模型进行验证。【结果/结论】实证结果显示：事件的重要性、模糊性与居民情绪是推动谣言传播的主要因素,而媒体素养则对谣言的传播发挥抑制作用。基于此,本文针对重大突发事件政府新媒体舆论场谣言,提出相关治理策略。【创新/局限】本研究为有效控制谣言,确保社会稳定团结提供一定帮助。但由于案例单一,验证结果较局限,日后可进一步深化。     

Identifying underlying influential factors in information diffusion process on social media platform: A hybrid approach of data mining and time series regression

As the prevailing online communications paradigm, social media platforms are considered to be the fastest medium for sharing and diffusing information. But what influences the spread of information through these platforms? The content of the post? The sentiments contained? Or the characteristics of user's behavior? To explore which factors promote the spread of information through social media, we developed a data analytics method that combines data mining with time series regression. We then applied this analytical framework to the L group Double 11 false advertising scandal, which blew up on the Sina microblog – a public hot trend that attracted the attention of millions of people. Our analysis reveals how three factors – user activity, emotional changes, and public attention – interact and the role they play in the spread of information. Among these factors, sentiment polarity and reposting are found to be the two main drivers of information diffusion. Emotional contagion accelerates the spread of information when the event first breaks (known as the accumulation period), while reposting does more to spread information once the event has gained some traction (the diffusion period). Surprisingly, the topic of public concentration in the event has a significant impact on the spread of the event in the accumulation period, but the effect shades away during the diffusion and convergence periods, i.e., the farther relations among topics are tied, the less public interest is abating on the event – a finding that is supported by cognitive load theory. However, although public attention shows little influence in the diffusion process, it does reveal how consumers shift their attention to different subtopics over time. Overall, our analysis sheds some light on how online events evolve and ‘go viral’. Notably, this study not only explores how underlying factors dynamically influence the information diffusion process, but also offers insights into how to manage information diffusion processes in practice.     

Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation

The adoption and diffusion of electronic government is often impeded by many social and individual factors relating to citizens. In this respect, intermediaries have emerged as a new model for delivering e-government services to overcome such obstacles. This study aims to examine the role of intermediaries in facilitating e-government adoption and diffusion using a survey based empirical study of 502 participants in Madinah City in Saudi Arabia. An extended UTAUT model is used as the theoretical basis utilizing trust in the Internet and Intermediaries. The results of this study show that there are significant relationships among the factors that influence intention to use e-government, namely, performance expectancy, effort expectancy, and trust of intermediary. In addition, the findings show that there is a significant relationship between facilitating conditions and usage behavior proving that intermediaries can influence adoption of e-government services.     

在线旅游企业商业模式创新路径比较研究

在线旅游企业的商业模式创新引起业界和学界的广泛关注。本文将在线旅游企业的商业模式分为三类,即:综合超市模式、搜索引擎模式、专业化模式,并以携程旅行网、去哪儿网、途家网为例对三种商业模式的创新路径进行了分析。本文认为,大型在线旅游企业正选择一站式线上旅行平台的商业模式创新方向,并逐渐成为旅游产品供应链的领导者;而中小在线旅游企业则走专业化的道路,成为旅游产品供应链上不可分割的一环。     

基于传染病模型的商业模式扩散机制研究

针对实践中涌现出的众多商业模式模仿现象,采用传染病模型探讨了商业模式扩散机制。首先分析了传染病模型对商业模式扩散问题的适用性;提出了商业模式扩散机制及相关假设,并通过系统仿真和案例研究验证了假设。结果表明:创新源相对规模越大,系统的动态性越强,达到均衡的时间越长;传染率和拒绝率的交互作用影响扩散速度和系统结构,两比例变量的相对差距受到商业模式自身性质的影响。研究结论弥补了商业模式和创新扩散理论的已有盲点,为企业商业模式创新和模仿及国家产业政策制定提供管理启示。     

Identifying influencers on social media

The increased availability of social media big data has created a unique challenge for marketing decision-makers; turning this data into useful information. One of the significant areas of opportunity in digital marketing is influencer marketing, but identifying these influencers from big data sets is a continual challenge. This research illustrates how one type of influencer, the market maven, can be identified using big data. Using a mixed-method combination of both self-report survey data and publicly accessible big data, we gathered 556,150 tweets from 370 active Twitter users. We then proposed and tested a range of social-media-based metrics to identify market mavens. Findings show that market mavens (when compared to non-mavens) have more followers, post more often, have less readable posts, use more uppercase letters, use less distinct words, and use hashtags more often. These metrics are openly available from public Twitter accounts and could integrate into a broad-scale decision support system for marketing and information systems managers. These findings have the potential to improve influencer identification effectiveness and efficiency, and thus improve influencer marketing.     

基于场论的技术扩散速度三阶段模型

本文以技术扩散速度为主要研究对象,根据产品生命周期和技术扩散过程中的场态特征,将技术扩散过程分为三个阶段,即场源形成阶段、场源稳定阶段、场源衰退阶段,根据不同阶段的技术扩散特点构建了三个阶段的技术扩散速度模型,并根据中国移动通信市场的发展的基本状况,应用统计工具软件对模型的拟合程度及模型参数进行估计验证,得出结果比较满意。根据此三阶段模型对技术扩散全过程中的各个阶段的扩散速度有更深入的认识。     

Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness,stress to use and social influence

Potential for the use of mobile wallet is enormous and it is drawing attention as an alternative mode of payment worldwide. The present research aims to provide important insights into the TAM (Technology Acceptance Model) and UTAUT2 (Unified Theory of Acceptance and Use of Technology) models. This study develops a conceptual model to determine the most significant factors influencing user's intention, perceived satisfaction and recommendation to use mobile wallet. The research model included 206 responses from an online and manual survey in India. Our study tested the moderating effect of innovativeness, stress to use and social influence on user's perceived satisfaction and recommendation to use mobile wallet services. We found that ease of use, usefulness, perceived risk, attitude, to have significant effect on user's intention, which further influenced user's perceived satisfaction and recommendation to use mobile wallet services. We also determined the significant moderating effect of stress to use and social influence on user's perceived satisfaction and recommendation to mobile wallet services. This study provides an integrated framework for academicians to measure the moderating effect of psychological, social and risk factors on technology acceptance. It can also help practitioners by identifying important factors affecting user's decision, which further affects user's perceived satisfaction and recommendation to use mobile wallet services.     

数字化时代危机信息传播模式的时段性特征及管理对策

在综述各种信息传播理论模式基础上,结合数字化时代危机信息传播的特点,并参照M.Defleur的"互动过程模式"和F.Duggan及L.Banwell的危机信息传播模式,建立基于数字化时代危机信息传播模式。在此基础上分析信息传播的过程、时段性特征,并针对所建立的模式和4个阶段的特征对政府如何有效应对公共危机管理对策进行探讨。     

Externalities of openness in innovation

Discussion of open innovation has typically stressed the benefits to the individual enterprise from boundary-spanning linkages and improved internal knowledge sharing. In this paper we explore the potential for wider benefits from openness in innovation and argue that openness may itself generate positive externalities by enabling improved knowledge diffusion. The potential for these (positive) externalities suggests a divergence between the private and social returns to openness and the potential for a sub-optimal level of openness where this is determined purely by firms’ private returns. Our analysis is based on Irish plant-level panel data from manufacturing industry over the period 1994–2008. Based on instrumental variables regression models our results suggest that externalities of openness in innovation are significant and that they are positively associated with firms’ innovation performance. We find that these externality effects are unlikely to work through their effect on the spread of open innovation practices. Instead, they appear to positively influence innovation outputs by either increasing knowledge diffusion or strengthening competition. Our evidence on the significance of externalities from openness in innovation provides a rationale for public policy aimed at promoting open innovation practices among firms.     

Explicit time embedding based cascade attention network for information popularity prediction

Predicting information cascade popularity is a fundamental problem in social networks. Capturing temporal attributes and cascade role information (e.g., cascade graphs and cascade sequences) is necessary for understanding the information cascade. Current methods rarely focus on unifying this information for popularity predictions, which prevents them from effectively modeling the full properties of cascades to achieve satisfactory prediction performances. In this paper, we propose an explicit Time embedding based Cascade Attention Network (TCAN) as a novel popularity prediction architecture for large-scale information networks. TCAN integrates temporal attributes (i.e., periodicity, linearity, and non-linear scaling) into node features via a general time embedding approach (TE), and then employs a cascade graph attention encoder (CGAT) and a cascade sequence attention encoder (CSAT) to fully learn the representation of cascade graphs and cascade sequences. We use two real-world datasets (i.e., Weibo and APS) with tens of thousands of cascade samples to validate our methods. Experimental results show that TCAN obtains mean logarithm squared errors of 2.007 and 1.201 and running times of 1.76 h and 0.15 h on both datasets, respectively. Furthermore, TCAN outperforms other representative baselines by 10.4%, 3.8%, and 10.4% in terms of MSLE, MAE, and R-squared on average while maintaining good interpretability.     

基于复杂网络的技术创新扩散控制研究

企业间竞争日趋激烈,所有的企业都希望通过创新来提升竞争力.针对技术创新扩散问题,运用复杂网络方法构建技术创新级联扩散非线性模型;对ER随机网络、BA无标度网络、SW小世界网络分别进行仿真分析,阐述网络结构对技术创新扩散的影响,通过调节感知效用再分配系数可以更好地达到控制扩散的效果.     

Item diversified recommendation based on influence diffusion

Recently, the high popularity of social networks accelerates the development of item recommendation. Integrating the influence diffusion of social networks in recommendation systems is a challenging task since topic distribution over users and items is latent and user topic interest may change over time. In this paper, we propose a dynamic generative model for item recommendation which captures the potential influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of topic interest and topic popularity over communities in social networks. A collapsed Gibbs sampling algorithm is proposed to train the model, and an improved diversification algorithm is proposed to obtain item diversified recommendation list. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show the superiority of our method compared with state-of-the-art diversified recommendation methods.     

山区年降水量的时空分布特征研究

以浙江省仙居县水文站为基本站,对仙居县内17个水文站和括苍山气象站的年降水量进行了时间序列订正,建立了17个水文站和括苍山气象站年降水量的时间序列订正模型。并分析了仙居县各降水量测站历年平均降水量的时间分布特征。将仙居县各降水量测站历年降水量数据与GPS实地调查所得到的各测站的经度、纬度和海拔高度数据结合起来,进行多元逐步回归分析,建立仙居县年降水量空间分布模型。其次,利用1：10000仙居县的地形图建立1：10000高空间分辨率的数字高程模型（DEM）。从中得到经度、纬度和海拔高度栅格数据,并结合仙居县年降水量空间分布模型,利用GIS的空间分析技术得到了仙居县年降水量空间分布特征,并制作了仙居县年降水量的空间分布图。     

Demand forecasting model using hotel clustering findings for hospitality industry

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.     

基于GRD测算模型的中小企业区域经济贡献度研究

中小企业作为区域经济增长中不可忽视的力量，已越来越受到人们的重视。在回顾索洛（Solow）模型的基础上，结合我国工业企业实际情况，提出了GRD经济贡献度测算模型，并将其应用于中部六省中小企业区域经济贡献度的测算中，同时运用时间序列分析法检验了模型的有效性与实用性。