Social media competitive analysis and text mining: A case study in the pizza industry

Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy.     

Understanding the effects of a social media service failure apology: A comparative study of customers vs. potential customers

Service failure apologies on social media are a new crisis communication outlet trend used by companies to apologise to affected customers quickly and offer solutions, ultimately to restore customers' trust and brand loyalty. This paper contributes to the nascent literature on companies' social media service failure apologies and fills a gap in the social commerce literature by recognising that due to the open and public nature of social media, these apologies may reach not just affected customers, but also unintended audiences such as potential customers among the general public, which could potentially damage a company's reputation and market share. An online survey administered to 241 customers and 271 non-customers of a famous mobile phone brand, which used YouTube to apologise to its customers for a service failure incident, is used to explore potential behavioural outcomes, after exposure to the apology. Findings confirm that both customers and non-customers of the service provider may become exposed to a social media service failure apology. The hypothesised model predicts behavioural intentions to remain a customer after exposure to the social media service apology better than behavioural intentions to become a customer, even though relationships hold for both groups. Theoretical and managerial implications are discussed.     

An empirical investigation of information sharing behavior on social commerce sites

Social commerce sites (SCSs), a new model of social media, provide fertile ground for customers to communicate their opinions and exchange product- or service- related information. Given the significant opportunities related to the use of social media data for customers’ insight, we explore the factors driving information sharing behavior on SCSs. In this paper, we propose and empirically test a comprehensive theoretical model for customer information sharing behavior through analysis of online survey data as well as network and behavioral usage data of over four months from 1177 customers in a SCS. The research model was empirically validated with the use of both subjective and objective data in a longitudinal setting. Our results show that customer information sharing is influenced by both individual (i.e., reputation and the enjoyment of helping others) and social capital (i.e., out-degrees’ post, in-degrees’ feedback, customer expertise and reciprocity) factors. This study contributes to the existing literature by highlighting the role of directed social network in customer information sharing behavior on SCSs. We believe that the results of our study offer important insights to the IS research and practice.     

面向语义缺失场景的社交媒体中热门新闻识别方法研究

[目的/意义]对社交媒体中热门新闻的及时识别,有助于加速正面资讯的投送或抑制负面资讯的扩散。当前,基于自然语言处理的传统识别方法正面临社交媒体新生态的挑战：大量新闻内容以图片、音视频形式存在,缺乏用于语义及情感分析的文本。[方法/过程]对此,本文首先将社交网络划分为众多社群,并按其层次结构组织为贝叶斯网络。接着,面向社群构建基于卷积神经网络的热门新闻识别模型,模型综合考虑新闻传播的宏观统计规律及微观传播过程,以提取社群内热门新闻传播的特征。最后,利用贝叶斯推理并结合局部性的模型识别结果进行全局性热度预测。[结果/结论]实验表明,本方法在语义缺失场景下可有效识别热门新闻,其准确度强于基于语义信息的机器学习方法,模型具有良好的时效性、可扩展性和适用性。该研究有助于社交媒体的监管机构及时识别出各类不含语义信息且迅速扩散的热点内容。     

轻量化视角下微信小程序用户持续使用意愿研究

以微信小程序为代表的轻量应用一定程度上改变了企业与用户之间的互动方式,如何提高“用户留存”已经成为企业进行社交媒体营销所面临的重要问题。在社交媒体情境下,小程序具有哪些特征、这些特征是否以及如何影响用户的持续使用意愿,并未得到理论界的足够重视。本研究从轻量化视角深入分析,基于信息系统成功模型与情绪理论构建了小程序轻量化特征影响用户持续使用意愿的理论模型。研究表明：信息-任务匹配、系统易用性以及服务响应性正向影响用户持续使用意愿;信息-任务匹配、服务响应性分别对用户积极情绪和流体验有促进作用,系统易用性正向影响用户流体验而对积极情绪的影响不显著;积极情绪分别在信息-任务匹配、服务响应性与用户持续使用意愿之间起到完全中介作用,流体验则完全中介了小程序轻量化特征与持续使用意愿之间的关系。研究构筑了“轻量化特征-情绪反应-行为意愿”完整的传导机制与影响路径,拓展了轻量应用持续使用、社交媒体用户情绪成因相关研究,结论对企业借助轻量应用进行社交媒体营销具有借鉴意义。     

基于虚拟社区体验的四元互惠战略模式

随着虚拟产品社区和品牌社区的发展,如何利用社会化媒体平台给顾客带来积极正向的体验成为各大企业不得不面临的重要问题。通过梳理前人研究,从顾客体验和社会化媒体的视角,选取了虚拟社区体验的26个指标并进行因子分析,归纳出功利体验、享乐体验、社会体验和可用体验四大维度;同样地,对顾客态度的维度进行梳理,共选取29个指标,通过因子分析归结为顾客对产品、对品牌和对企业的态度三大维度。最后以虚拟社区体验维度为自变量,以顾客态度为因变量,建立三个回归模型,阐释了虚拟社区体验对顾客态度的影响机理,丰富了顾客体验理论的研究成果,能为相关学者提供有价值的理论和实践指导,同时提出的四元互惠战略发展模式有助于企业管理客户关系、制定品牌战略和公司发展战略,能为相关学者提供有价值的理论和实践指导。     

A symbolic interactionism perspective of using social media for personal and business communication

Achieving the anticipated business benefits of a social medium is important as organizations diligently invest in different social media platforms. While much previous research assumes that social media helps organizations to communicate with customers, less is known about whether customers embrace using social media to interact with organizations. It is important to understand the role of social media for business communication from the customers’ perspective, as this may significantly deviate from the organizations’ own communicative intentions. In this exploratory case study of the Moon Struck hotel in China, we investigate both how customers interpret the hotel’s use of WeChat official account for business communication and how customers respond to messages received from Moon Struck’s WeChat account. Adopting a symbolic interactionism perspective, we surprisingly find that WeChat personal accounts and Moon Struck’s official account offer radically different meanings to followers. Specifically, WeChat personal account symbolizes a sociality-oriented meaning (e.g., relationship and image building), while Moon Struck’s WeChat official account symbolizes information broadcasting-related meaning (e.g., selling, advertising, and branding). Both technological features and the distance of relationships among users contribute to the constructed symbolic meaning of technology, subsequently affecting users’ WeChat use patterns. The theoretical implications of this study are discussed and recommendations are made for future research and practice.     

Social commerce: Factors affecting customer repurchase and word-of-mouth intentions

Social commerce refers to an extension of e-commerce sites, integrated with social media and Web 2.0 technology to encourage online purchases and interactions with customers before, during, and after the purchase. As the country with the largest e-commerce market in Southeast Asia and many active social media users, Indonesia has many opportunities to implement successful social commerce. Since customers are the primary focus in social commerce, repurchase and word-of-mouth (WOM) intentions have been considered as significant behavioural intentions after a customer completes a purchase. Thus, this study aims to identify factors that affect customers’ repurchase and WOM intentions. A total of 421 sets of survey data were gathered from social commerce customers in Indonesia and analysed using the partial least squares approach. The results indicate that repurchase and WOM intentions are positively affected by trust and satisfaction, where both trust and satisfaction are positively affected by reputation and information quality. The results provide theoretical and practical implications for future social commerce research and practical implications for social commerce firms.     

To be or not to be in social media: How brand loyalty is affected by social media?

There is an ongoing debate over the activities of brands and companies in social media. Some researchers believe social media provide a unique opportunity for brands to foster their relationships with customers, while others believe the contrary. Taking the perspective of the brand community building plus the brand trust and loyalty literatures, our goal is to show how brand communities based on social media influence elements of the customer centric model (i.e., the relationships among focal customer and brand, product, company, and other customers) and brand loyalty. A survey-based empirical study with 441 respondents was conducted. The results of structural equation modeling show that brand communities established on social media have positive effects on customer/product, customer/brand, customer/company and customer/other customers relationships, which in turn have positive effects on brand trust, and trust has positive effects on brand loyalty. We find that brand trust has a fully mediating role in converting the effects of enhanced relationships in brand community to brand loyalty. The implications for marketing practice and future research are discussed.     

授人以鱼不如授人以渔——创新视角下的政府背景客户与企业成长

本文利用2007-2015年间A股上市公司年报中公开披露的客户数据,从研发创新的角度检验了政府大客户的存在对企业成长性的影响。实证结果表明,上市公司的大客户中政府背景订单占比约大,则企业未来的盈利增长能力就越强,与此同时,资本市场对企业的估值水平也越高,进一步地从作用机制来看,政府背景订单之所以能促进企业成长壮大,很大程度上可以归结于它们对企业研发创新的促进作用,尤其是增加公司的创新投入与创新产出,继而助推企业成长。总而言之,本文的研究发现有助于我们客观地了解政府背景客户在企业成长发展中扮演的角色,辩证地看待以政府采购为代表的另类政府干预机制对实体经济产生的影响作用。     

Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models

Since its introduction, television has been the main channel of investment for advertisements in order to influence customers purchase behavior. Many have attributed the mere exposure effect as the source of influence in purchase intention and purchase decision; however, most of the studies of television advertisement effects are not only outdated, but their sample size is questionable and their environments do not reflect reality. With the advent of the internet, social media and new information technologies, many recent studies focus on the effects of online advertisement, meanwhile the investment in television advertisement still has not declined. In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months. If we were able to predict purchase behaviors with models based on exposure time more reliably than with models based on demographic data, the obvious strategy for businesses would be to increase the number of adverts. On the other hand, if models based on exposure time had unreliable predictability in contrast to models based on demographic data, doubts would surface about the effectiveness of the hard investment in television advertising. Based on our results, we found that models based on advert exposure time were consistently low in their predictability in comparison with models based on demographic data only, and with models based on both demographic data and exposure time data. We also found that there was not a statistically significant difference between these last two kinds of models. This suggests that advert exposure time has little to no effect in the short-term in increasing positive actual purchase behavior.     

Sentiment aggregation of targeted features by capturing their dependencies: Making sense from customer reviews

Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers’ acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers’ opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach.     

Investigating consumers’ online social shopping intention: An information processing perspective

Online social media is transforming the way customers communicate and exchange product information with others. Consumers increasingly rely on the opinions and recommendations from social media members when making purchasing decisions. However, information received from social media may have different meanings and social implications for consumers. Based on the theory of informational social influence and heuristic-systematic model (HSM), we develop a model to understand the relative importance of informational social influence, normative social influence, and perceived information quality on the consumer’s social shopping intention under different levels of product involvement. The results of the structural equation modeling (SEM) using a sample of 503 consumers in the Facebook brand fan pages indicate that social influences have a greater impact on the consumer’s social shopping intention than perceived information quality. Three social interactional factors (perceived similarity, familiarity, and expertise) have a positive effect on social shopping intention via the mediation of informational, normative social influence and perceive information quality. The multiple-group analysis suggests that high product-involved consumers are motivated to exert more cognitive effort to evaluate the product information. In contrast, low product-involved consumers are more susceptible to informational social influence. We draw on these findings to offer implications for researchers and practitioners.     

Compatible quality of social media content: Conceptualization,measurement, and affordances

Building and maintaining favorable social media relationships with customers require that organizations produce quality content that fits customers’ needs. So far, little if any research has conceptualized and measured this kind of content quality in the context of social media. Therefore, this study proposes and empirically examines a new construct, compatible quality of social media content, which adds to knowledge by expanding prior online content quality research beyond the traditional focus on non-fit quality beliefs. The results of structural equation modeling of responses collected from a sample of active social media users reveal at least three worth noting findings. First, the study confirmed the multidimensionality of compatible quality of social media content, which encompasses reflective, stimulated, practiced, and advocated components. Second, there was equivalence in the measurements of the studied constructs and the structural weights of the proposed network of relationships across the different gender and experience subgroups. Third, compatible quality of social media content was found to influence continued interest, active confidence, and feedback openness. The practical and research implications of these results are discussed.     

Towards a big data framework for analyzing social media content

Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification.     

Influence of customer engagement with company social networks on stickiness: Mediating effect of customer value creation

Company social networks have become an important means for the socialized marketing of a company, forming a new challenge to companies on how to attract customers. Based on such theories as customer engagement, value co-creation, and relationship marketing, this paper presents a model of the influence of customer engagement on stickiness. Data collected from 260 valid questionnaires from Sina’s enterprise microblog users were analyzed by structural equation modeling. Empirical results show that customer engagement has a direct and positive influence on customer stickiness as well as an indirect influence through customer value creation. This study enriches previous researches on existing theories of customer engagement, value co-creation, and stickiness, and gives practical guidance for companies to encourage customer engagement and enhance the stickiness of company social networks.     

Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors

With the rapid development of information technology, customers not only shop online—they also post reviews on social media. This user-generated content (UGC) can be useful to understand customers’ shopping experiences and influence future customers’ purchase intentions. Therefore, business intelligence and analytics are increasingly being advocated as a way to analyze customers’ UGC in social media and support firms’ marketing activities. However, because of its open structure, UGC such as customer reviews can be difficult to analyze, and firms find it challenging to harness UGC. To fill this gap, this study aims to examine customer satisfaction and dissatisfaction toward attributes of hotel products and services based on online customer textual reviews. Using a text mining approach, latent semantic analysis (LSA), we identify the key attributes driving customer satisfaction and dissatisfaction toward hotel products and service attributes. Additionally, using a regression approach, we examine the effects of travel purposes, hotel types, star level, and editor recommendations on customers’ perceptions of attributes of hotel products and services. This study bridges customer online textual reviews with customers’ perceptions to help business managers better understand customers’ needs through UGC.     

Big data-assisted social media analytics for business model for business decision making system competitive analysis

Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness.     

企业外部社会联系对破坏性创新的影响研究

企业创新活动总是嵌入在特定的社会情境之中。基于合作对象差异,将企业外部社会联系分为供应链联系、竞争对手联系和知识生产服务机构联系三类,并运用180份珠三角企业调查问卷数据实证探讨它们与企业破坏性创新之间的复杂关系。结果发现,供应链联系对企业破坏性创新具有正向线性影响;竞争对手联系与企业破坏性创新之间存在正“U”型关系;而知识生产服务机构联系与企业破坏性创新之间存在倒“U”关系。研究结论为企业在实施和管理破坏性创新活动中如何有效利用外部社会联系提供实证依据和实践启示。     

基于用户信息行为的新媒体社交平台信息茧房现象及其破茧策略研究——以非遗短视频传播为例

【目的/意义】从新媒体社交平台中用户行为角度分析造成“信息茧房”的影响因素,探究突破特定内容领 域“信息茧房”困境、提升信息传播力的策略。【方法/过程】本文构建了新媒体社交平台中“信息茧房”现象影响因素 模型,运用相关分析与回归分析定量化检验新媒体社交平台中“信息茧房”现象产生的多重影响因素,在此基础上 提出突破茧房提高非遗短视频传播力的策略。【结果/结论】选择性接触行为、信息偶遇和主观规范直接正向影响 “信息茧房”感知和“信息茧房”突破意愿;使用时间、使用频率、单次使用时长和关注人数间接正向影响“信息茧房” 感知和“信息茧房”突破意愿。【创新/局限】本文借助于抖音APP为实证平台,融合新媒体社交平台结构属性与资源 内容属性双重特征,针对特定内容领域的信息茧房现象探索影响因素的形成动力与信息传播规律。