Benefitting from virtual customer environments: An empirical study of customer engagement

Customer engagement has been labeled as a prerequisite for the success of virtual customer environments. A key challenge for organizations serving their customers via these environments is how to stimulate customer engagement. This study is among the first to shed light on this issue by examining customer engagement and its drivers. Using the theory of uses and gratification as theoretical lens, we develop a model that relates characteristics of virtual customer environments, perceived benefits of using these environments and customer engagement intentions. The model is validated using partially least squares structural equation modeling on three samples of real users of different virtual customer environments in the Dutch telecom industry. The results provide clear support for the validity of the hypothesized relationships and show high robustness of the findings across the three datasets. An important finding of this study is that cognitive, social integrative and hedonic benefits appear to be significant in their influence on customer engagement intentions. Overall, the findings add to the underexplored field of customer engagement study and hold implications for research into and the management of virtual customer environments.     

Facebook and political engagement: A study of online political group membership and offline political engagement

In what ways do online groups help to foster political engagement among citizens? We employ a multi-method design incorporating content analysis of online political group pages and original survey research of university undergraduates (n = 455) to assess the relationship between online political group membership and political engagement—measured through political knowledge and political participation surrounding the 2008 election. We find that participation in online political groups is strongly correlated with offline political participation, as a potential function of engaging members online. However, we fail to confirm that there is a corresponding positive relationship between participation in online political groups and political knowledge, likely due to low quality online group discussion.     

The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement

Educators and others are interested in the effects of social media on college students, with a specific focus on the most popular social media website—Facebook. Two previous studies have examined the relationship between Facebook use and student engagement, a construct related to positive college outcomes. However, these studies were limited by their evaluation of Facebook usage and how they measured engagement. This paper fills a gap in the literature by using a large sample (N = 2368) of college students to examine the relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Student engagement was measured in three ways: a 19-item scale based on the National Survey of Student Engagement, time spent preparing for class, and time spent in co-curricular activities. Results indicate that Facebook use was significantly negatively predictive of engagement scale score and positively predictive of time spent in co-curricular activities. Additionally, some Facebook activities were positively predictive of the dependent variables, while others were negatively predictive.     

Facebook and Online Privacy: Attitudes,Behaviors, and Unintended Consequences

This article investigates Facebook users' awareness of privacy issues and perceived benefits and risks of utilizing Facebook. Research found that Facebook is deeply integrated in users' daily lives through specific routines and rituals. Users claimed to understand privacy issues, yet reported uploading large amounts of personal information. Risks to privacy invasion were ascribed more to others than to the self. However, users reporting privacy invasion were more likely to change privacy settings than those merely hearing about others' privacy invasions. Results suggest that this lax attitude may be based on a combination of high gratification, usage patterns, and a psychological mechanism similar to third‐person effect. Safer use of social network services would thus require changes in user attitude.     

Exploring Privacy Management on Facebook: Motivations and Perceived Consequences of Voluntary Disclosure

This study examined the perceived motivations and consequences of voluntary disclosure of Facebook active users using a survey administered to college students in a public‐speaking course. College‐age students who took the survey were motivated to use Facebook because they perceived their relationships improved with friends and family, although using Facebook could become negatively habit forming. The research suggests that users of Facebook use it more for disclosing to distant friends rather than to close friends, which is divergent from most early disclosure research that equates disclosure with intimacy. This research utilizes Communication Privacy Management Theory for the theoretical framework.     

Oscillatory behavior of delay partial difference equations with positive and negative coefficients

We construct an important transform to obtain sufficient conditions for the oscillation of all solutions of delay partial difference equations with positive and negative coefficients of the form A_m+1,n + A_m,n+1 − A_mn + p_mnA_m−k−1 − q_mnA_{m−k′,n−l′} = 0, where m, n = 0, 1, …, and k, k′, l′, l are nonnegative integers, p, q ϵ (0, ∞), the coefficients {q_mn} and {p_mn} are sequences of nonnegative real numbers.     

Linear classifiers are nearly optimal when hidden variables have diverse effects

We analyze classification problems in which data is generated by a two-tiered random process. The class is generated first, then a layer of conditionally independent hidden variables, and finally the observed variables. For sources like this, the Bayes-optimal rule for predicting the class given the values of the observed variables is a two-layer neural network. We show that, if the hidden variables have non-negligible effects on many observed variables, a linear classifier approximates the error rate of the Bayes optimal classifier up to lower order terms. We also show that the hinge loss of a linear classifier is not much more than the Bayes error rate, which implies that an accurate linear classifier can be found efficiently.     

Networked narratives on Humans of New York: A content analysis of social media engagement on Facebook

Humans of New York (HONY) is a popular Facebook page which has more than 13 million fans. The posts on HONY are termed as networked narratives, which are stories told on social media with technology affordances enabling story co-construction between the story tellers and the readers. A content analysis (N = 390) was conducted to examine the popular topics on networked narratives and its impact on social media engagement as represented by the number of likes, the number of shares, and likability of characters featured in the post. Results revealed a set of topics of the networked narratives were associated with social media engagement. Also, the tone and the length of posts were associated with social media engagement.     

The Janus face of Facebook: Positive and negative sides of social networking site use

There is an increasing awareness that social networking site (SNS) use includes a socio-psychologically positive and a negative side. However, research remains largely silent on which side dominates in driving SNS use. To address this gap and to better understand the nature of SNSs we examine the positive and negative drivers of SNS use in parallel. We draw on the uses and gratifications theory and place social enhancement and interpersonal connectivity as the socio-psychologically positive gratifications and exhibitionism and voyeurism as the adverse gratifications predicting SNS use. We further link these gratifications to two key psychological needs, namely self-presentation and the need to belong. We conceptualize our dependent variable, SNS use, as a multi-dimensional second-order construct that consists of content production, content consumption, amount of usage, and comprehensiveness of one's profile information. We use longitudinal data from Facebook users to test our research model. The results show that exhibitionism, voyeurism and interpersonal connectivity predict SNS use. Furthermore, the number of friends in the SNS decreases the effect of exhibitionism and increases the effect of social enhancement. Altogether, the role of exhibitionism and voyeurism in predicting SNS use underscores the need for increased awareness of the socio-psychologically dark side of SNSs.     

Pedagogical lurking: Student engagement in non-posting discussion behavior

This paper presents the results of a study of student non-posting participation behavior in two online classes. Most often active message is assessed and thus implicitly valued in online class discussion, but the act of writing messages is not the only factor that contributes to student learning. However, it is the most visible and easiest to measure. Students may engage in processes of reading and reflection on the discussion board, not leaving their mark; it is these acts that may be referred to as pedagogical lurking. In this study, students were asked to self-report their non-visible course activities, the reasons behind these activities and their perceived usefulness related to learning. Findings show that about half of the students felt that they learned through the online discussion experience, and that they believe both posting and reading messages contributed to their ability to learn. These students were likely to enter the discussion before posting to obtain a model for participation, and to return at a later time to check for replies and reflect. Students who participated solely to meet course requirements and who focused on posting messages more than reading messages had less positive impressions of the discussion activity’s impact on learning.     

基于背景的个性化客户行为模型研究

针对商业客户的背景信息对其消费行为的影响问题,提出了一种基于背景的商业客户行为模型的构建方法。该方法不仅收集了包含三级粒度背景信息的某大学学生客户的网上交易数据并按依赖于交易数据项的统计量对客户进行分组,还利用朴素贝叶斯(NB和TAN)及分组和统计关系数据库(GAC-RDB)分类器学习了各客户分组的背景和非背景预测函数,同时使用各预测变量的受试者运行特征曲线下面积(AUC)值,对客户背景在预测客户购买行为时的作用进行了定量的比较和分析。研究结果表明:背景信息对客户特别是个性化客户(单一客户)的消费决策具有良好的预测效果。     

A comparison study of user behavior on Facebook and Gmail

Email is one of the most important communication media to date, and millions of Internet users around the world spend the bulk of their time writing and reading electronic messages. The growing popularity of Social Network Sites (SNS) as a communication medium motivated us to explore the relationship between user behavior on SNS and email. In this paper, we collected objective, privacy-preserved behavior data from participants that were both active Facebook and Gmail users. We show that a large proportion of social interactions still occur through email messages, while participants tend to be more emotional on Facebook. We found that producing more content on Facebook does not necessarily decrease or increase the production of emails. By utilizing the quantitative analysis of usage data instead of surveys, we show that user behavior is likely consistent across SNS and email in some aspects, for example, users with more Facebook friends seem to email to more people, Facebook and Gmail share similar trend of “happiness”.     

Strategic self-presentation on Facebook: Personal motives and audience response to online behavior

People have always been concerned with their social image and engaged in strategic self-presentation (Goffman, 1959), but the growth of social networking sites (SNS) has had a major influence on such social endeavors. When people choose how to present themselves online, they use strategies and make choices that influence the liking and respect they receive from others. We examine a model that links: (a) motives for self-presentation, (b) actual online behavior on Facebook, and (c) feedback: social network members' responses (“likes,” comments). One hundred and fifty-six undergraduates (37 males, 119 females, mean age = 24.5) reported on their last three Facebook status updates (468 status updates overall). Based on structural equation modeling, we found that performance goals predict increased levels of self-enhancement on Facebook and increased levels of social feedback – “likes” and comments by others. Mastery goals do not predict increased levels of self-derogation (posting negative contents about self), but those who do choose to derogate also receive increased numbers of “likes” and comments from their network friends.     

Learning and self-disclosure behavior on social networking sites: the case of Facebook users

This paper studies Facebook users’ learning-based attitude formation and the relationship between member attitude and self-disclosure. Through the theoretical lens of learning theories, we recognize the key antecedents to member attitude toward a social networking as stemming from classical conditioning, operant conditioning, and social learning-related factors. In addition, we explore the underlying process through which member attitude affects self-disclosure extent and theorize the mediating role of site usage rate on the relationship between attitude and self-disclosure extent. Analysis of 822 survey data results provides strong support for the role of learning theories in explaining Facebook members’ attitude development. The results also confirm a significant, partial mediating effect of site usage rate. A series of post-hoc analyses on gender difference further reveal that attitude formation mechanisms remain constant between male and female Facebook users; gender difference exists on the association between attitude and self-disclosure extent and the association between site usage rate and self-disclosure extent; and the mediating effect of site usage rate exists in male user group only. Our research, therefore, contributes to the literature on social networking sites, as well as providing behavioral analysis useful to the service providers of these sites.     

Modeling user behavior data in systems of engagement

The proliferation of mobile devices has changed the way digital information is consumed and its efficacy measured. These personal devices know a lot about user behavior from embedded sensors along with monitoring the daily activities users perform through various applications on these devices. This data can be used to get a deep understanding of the context of the users and provide personalized services to them. However, there are a lot of challenges in capturing, modeling, storing, and processing such data from these systems of engagement, both in terms of achieving the right balance of redundancy in the captured and stored data, along with ensuring the usefulness of the data for analysis. There are additional challenges in balancing how much of the captured data should be processed through client or server applications. In this article, we present the modeling of user behavior in the context of personalized education which has generated a lot of recent interest. More specifically, we present an architecture and the issues of modeling student behavior data, captured from different activities the student performs during the process of learning. The user behavior data is modeled and sent to the cloud-enabled backend where detailed analytics are performed to understand different aspects of a student, such as engagement, difficulties, and preferences and to also analyze the quality of the data.     

Verification of real-time systems with preemption: negative and positive results

The aim of this article is to explore the problem of verification of preemptible real-time systems, i.e. systems composed of tasks which can be suspended and resumed by an on-line scheduler. The first contribution of the article is to show that this problem is unfortunately undecidable. To overcome this negative result, we restrict the real-time tasks to be periodic and the implementation to be functionally deterministic, meaning that the preemptions do not affect the functional behaviour and preserve some temporal properties satisfied by the specification. We prove that the verification problem of functional determinism is decidable. This outlines a verification strategy: (1) prove that the scheduled real-time system is deterministic, (2) consider a deterministic non preemptible behaviour which is functionally equivalent to the executions and (3) verify the properties on this behaviour.     

Ferguson on Facebook: Political persuasion in a new era of media effects

In 2014, media outlets deemed political satirist John Oliver's show the best of the year. Described as “the online water-cooler,” his show has become a talking point for many on social media. In this study, we tested the effects of such political comedy and the influence of social pressure in a simulated Facebook environment (N = 189). Participants in this experiment were asked to view a video clip from Oliver's show (high or low salience topic) and in two of the four conditions, a series of Facebook comments contradicting Oliver's views (as a form of social pressure) were provided. Results support a message-consistent persuasive effect of political comedy for both high and low salience issues but finds that message-incongruent commentary reduces this persuasive effect. Thus, the current study provides further insight regarding persuasive effects of sharing political information on social media and new opportunities for exposing entertainment seekers to politics.     

Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy

With competitive pressure growing in online markets, many Internet stores provide various customer aid functions such as personalized pages to help customers shop more effectively and efficiently. Evaluating such customer aid functions is usually costly because it requires full or partly-working systems and many human testers. In order to address this problem, this research presents a novel approach to evaluating customer aid functions with agent-based models of customer behavior and evolution strategies. Agent-based modeling is used to imitate users’ rational behavior at Internet stores with regard to browsing and collecting product information. It is assumed that users evolve their browsing skill and strategy over time, to maximize the efficiency and effectiveness of their shopping, and hence, evolution strategy, an optimization method, is combined with the agent-based model to find the rational behavior of each user. The rational behavior is then used to simulate the virtual shopping of users and to evaluate the performances of target customer aid functions. Several experiments were performed to illustrate the use of the approach, where the personalized recommendation page of a virtual online DVD rental store is evaluated in comparison with more general functions such as listing most popular products or sorting categories. The results show that a personalized page might not always be the best customer aid function for all users compared to the simpler ones.     

Concept-based learning of human behavior for customer relationship management

In this paper, we apply concept learning techniques to solve a number of problems in the customer relationship management (CRM) domain. We present a concept learning technique to tackle common scenarios of interaction between conflicting human agents (such as customers and customer support representatives). Scenarios are represented by directed graphs with labeled vertices (for communicative actions) and arcs (for temporal and causal relationships between these actions and their parameters). The classification of a scenario is performed by comparing a partial matching of its graph with graphs of positive and negative examples. We illustrate machine learning of graph structures using the Nearest Neighbor approach and then proceed to JSM-based concept learning, which minimizes the number of false negatives and takes advantage of a more accurate way of matching sequences of communicative actions. Scenario representation and comparative analysis techniques developed herein are applied to the classification of textual customer complaints as a CRM component. In order to estimate complaint validity, we take advantage of the observation [19] that analyzing the structure of communicative actions without context information is frequently sufficient to judge how humans explain their behavior, in a plausible way or not. This paper demonstrates the superiority of concept learning in tackling human attitudes. Therefore, because human attitudes are domain-independent, the proposed concept learning approach is a good compliment to a wide range of CRM technologies where a formal treatment of inter-human interactions is required.