scholarly journals Who sells knowledge online? An exploratory study of knowledge celebrities in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoyu Chen ◽  
Alton Y.K. Chua ◽  
L.G. Pee
Keyword(s):  
Latent Dirichlet Allocation ◽  
Sales Performance ◽  
Online Identity ◽  
Data Set ◽  
Content Type ◽  
Multiple Group ◽  
Identity Signaling ◽  
Important Trend ◽  
Knowledge Platform ◽  
Online Identities

PurposeThis study explores identity signaling used by an emerging class of knowledge celebrities in China – Knowledge Wanghong – who sell knowledge products on online platforms. Because identity signaling may involve constructing unique online identities and controlling over product-related and seller-related characteristics, the purpose of this study is two-fold: (1) to uncover different online identities of knowledge celebrities; and (2) to examine the extent to which the online identity type is associated with their product-related characteristics, seller-related characteristics and sales performance.Design/methodology/approachA unique data set was collected from a Chinese leading pay-for-knowledge platform – Zhihu – which featured the online profiles of tens of thousands of knowledge celebrities. Online identity types were derived from their self-edited content using Latent Dirichlet Allocation (LDA) topic modeling. Thereafter, their product-related characteristics, seller-related characteristics and respective sales performance were analyzed across different identity types using analysis of variance (ANOVA) and multiple-group linear regression.FindingsKnowledge celebrities are clustered into four distinctive online identities: Mentor, Broker, Storyteller and Geek. Product-related characteristics, sell-related characteristics and sales performance varied across four different identities. Additionally, the online identity type moderated the relationships among their product-related characteristics, sell-related characteristics and sales performance.Originality/valueAs emerging-phenomenon-based research, this study extends related literature by using the notion of identity signaling to analyze a peculiar group of online celebrities who are setting an important trend in the pay-for-knowledge model in China.

Mining numerical measure of consumers’ product evaluation expressed in words based on latent Dirichlet allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziang Wang ◽  
Feng Yang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Product Evaluation ◽  
Online Reviews ◽  
Product Evaluations ◽  
Product Attributes ◽  
Data Set ◽  
Content Type ◽  
Face To Face ◽  
Online Retailers ◽  
Dirichlet Allocation

Purpose It has always been a hot topic for online retailers to obtain consumers’ product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers’ attitude and provides a method to calculate the numerical measure of consumers’ product evaluation expressed in each word. Design/methodology/approach First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers’ attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers’ binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model. Findings The experiment’s result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers’ product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expanded model and depicted at the end of the experiment. Originality/value This research provides a new method to calculate consumers’ product evaluation from reviews in the level of words. Words may be used to describe product attributes or consumers’ experiences in reviews. Assigning words with numerical measures can analyze consumers’ products evaluation quantitatively. Besides, words are labeled themselves, they can also be ranked if a numerical measure is given. Online retailers can benefit from the result for label choosing, advertising or product recommendation.

A big data approach to examining social bots on Twitter

2019 ◽  
Vol 33 (4) ◽  
pp. 369-379 ◽  
Author(s):  
Xia Liu
Keyword(s):  
Big Data ◽  
Fixed Effects ◽  
Latent Dirichlet Allocation ◽  
User Generated Content ◽  
Fixed Effects Model ◽  
Data Set ◽  
Information Distortion ◽  
Content Type ◽  
Related Information ◽  
Brand Reputation

Purpose Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended. Design/methodology/approach A Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data. Findings The findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots. Research limitations/implications As brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots. Originality/value This is the first big data examination of social bots in the context of brand-related user-generated content.

Incorporating LDA with LSTM for followee recommendation on Twitter network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brahim Dib ◽  
Fahd Kalloubi ◽  
El Habib Nfaoui ◽  
Abdelhak Boulaalam
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Short Term Memory ◽  
Real Life ◽  
Network Graph ◽  
Data Set ◽  
Content Type ◽  
Modeling Strategy ◽  
Searching Method

Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

The impact of organizational social networks on salespeople’s negative headquarters stereotypes

2020 ◽  
Vol 35 (12) ◽  
pp. 1901-1913
Author(s):  
Babak Hayati ◽  
Sandeep Puri
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sales Performance ◽  
Sales Managers ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Network Patterns ◽  
The Impact ◽  
Sales Forces

Purpose Extant sales management literature shows that holding negative headquarters stereotypes (NHS) by salespeople is harmful to their sales performance. However, there is a lack of research on how managers can leverage organizational structures to minimize NHS in sales forces. This study aims to know how social network patterns influence the flow of NHS among salespeople and sales managers in a large B2B sales organization. Design/methodology/approach The authors hypothesize and test whether patterns of social networks among salespeople and sales managers determine the stereotypical attitudes of salespeople toward corporate directors and, eventually, impact their sales performance. The authors analyzed a multi-level data set from the B2B sales forces of a large US-based media company. Findings The authors found that organizational social network properties including the sales manager’s team centrality, sales team’s network density and sales team’s external connectivity moderate the flow of NHS from sales managers and peer salespeople to a focal salesperson. Research limitations/implications First, the data was cross-sectional and did not allow the authors to examine the dynamics of social network patterns and their impact on NHS. Second, The authors only focused on advice-seeking social networks and did not examine other types of social networks such as friendship and trust networks. Third, the context was limited to one company in the media industry. Practical implications The authors provide recommendations to sales managers on how to leverage and influence social networks to minimize the development and flow of NHS in sales forces. Originality/value The findings advance existing knowledge on how NHS gets shared and transferred in sales organizations. Moreover, this study provides crucial managerial insights with regard to controlling and managing NHS in sales forces.

scholarly journals The formation of a cross-selling initiative climate and its interplay with service climate

2018 ◽  
Vol 52 (7/8) ◽  
pp. 1457-1484 ◽  
Author(s):  
Ting Yu ◽  
Ko de Ruyter ◽  
Paul Patterson ◽  
Ching-Fu Chen
Keyword(s):  
Positive Impact ◽  
Sales Performance ◽  
Service Climate ◽  
Frontline Employees ◽  
Initial Attempt ◽  
Bottom Line ◽  
Data Set ◽  
Frontline Staff ◽  
Content Type ◽  
The Impact

Purpose This study aims to explore the formation and consequences of a cross-selling initiative climate, as well as how a service climate, which provides an important boundary condition, affects both its formation and its ultimate impact on service-sales performance. This article identifies two important predictors of a cross-selling initiative climate: frontline employees’ perceptions of supervisors’ bottom-line mentality and their own sense of accountability. Design/methodology/approach The multilevel data set includes 180 frontline staff and supervisors (team leaders) from 31 teams employed by a spa/beauty salon chain. Hierarchical linear modelling and partial least squares methods serve to analyse the data. Findings Supervisors’ bottom-line mentality disrupts a cross-selling initiative climate. A sense of accountability exerts a positive impact at both individual and team levels. A service climate at the team level weakens the impact of a sense of accountability on a cross-selling initiative climate. A cross-selling initiative climate has a positive effect on team-level service-sales performance, but this effect is weakened by the service climate. Originality/value This study conceptualises an important frontline work unit attribute as a climate. It offers an initial argument that a cross-selling initiative climate is a central factor driving a work unit’s service-sales performance, which can increase firms’ productivity and competitive advantages. With this initial attempt to explore the antecedents and consequences of a cross-selling initiative climate, the study also offers novel insights into the interplay between a service and a cross-selling initiative climate.

Overcoming over-identification

2019 ◽  
Vol 37 (3) ◽  
pp. 258-270
Author(s):  
Valter Afonso Vieira ◽  
Juliano Domingues da Silva ◽  
Colin Gabler
Keyword(s):  
Social Identity Theory ◽  
Sales Performance ◽  
Negative Influence ◽  
Sales Force ◽  
Self Concept ◽  
Sales Manager ◽  
Data Set ◽  
Content Type ◽  
Managerial Support ◽  
The Impact

Purpose The purpose of this paper is threefold: first, to determine the impact of interpersonal identification on sales performance; second, to uncover whether or not that relationship changes direction based on levels organizational prestige; and third, to test the antecedent of managerial support on salesperson interpersonal identification. Ultimately, the authors want to provide sales managers with tangible ways to nurture the self-concept of their sales force while optimizing sales performance. Design/methodology/approach The authors test the hypotheses using a data set of 196 B2C retail salespeople in the shoe industry. Respondents answered a printed questionnaire, which was analyzed using multiple linear regression and response surface analysis. Findings The authors find that managerial support does positively influence interpersonal identification among salespeople which, in turn, increases sales performance. However, the relationship is curvilinear, becoming negative when over-identification occurs. This inverted U-shaped relationship is moderated by organizational prestige such that the negative influence is overcome by employees who have pride and confidence in their organization. Practical implications Managers should balance the level of support that they provide their employees. While this mentorship generally leads to positive results, too much can lead to over-identification, and consequently reduce sales performance. However, this negative effect can be overcome if the salesperson perceives his organization as prestigious. Therefore, a mix of guidance and autonomy may foster the strongest self-concept among the sales team and generate the most positive outcomes. Further, managers should monitor their employees’ perceptions of the company, communicating its strong reputation internally to generate organizational prestige. Originality/value The authors extend social identity theory in a sales context to provide a better understanding of how self-concept can be altered – for better or worse – by the sales manager. The authors also show the importance of communicating your company’s social value to employees. While over-identification in the manager–employee dyad can create a “tipping point” where sales performance begins to decrease, organizational prestige may be able to overcome this effect, demonstrating the power of prestige. Together, the authors present the importance of contextual and external influences on individual sales performance.

scholarly journals An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models

2019 ◽  
Vol 3 (3) ◽  
pp. 333-347
Author(s):  
Xudong Lu ◽  
Shipeng Wang ◽  
Fengjian Kang ◽  
Shijun Liu ◽  
Hui Li ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Probability Model ◽  
Rapid Development ◽  
Monitoring Data ◽  
Distribution Model ◽  
Industrial Equipment ◽  
Data Set ◽  
Content Type ◽  
Multiple Group ◽  
Correlation Probability

Purpose The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge. Design/methodology/approach In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized. Findings The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences. Originality/value The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.

Understanding relationship quality in hospitality services

2019 ◽  
Vol 29 (3) ◽  
pp. 478-503 ◽  
Author(s):  
Manuel J. Sanchez-Franco ◽  
Gabriel Cepeda-Carrion ◽  
José L. Roldán
Keyword(s):  
Relationship Quality ◽  
Structural Equation ◽  
Latent Dirichlet Allocation ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Equation Modeling ◽  
Affective States ◽  
Data Set ◽  
Content Type ◽  
Out Of Sample Prediction

Purpose The purpose of this paper is to analyze the occurrence of terms to identify the relevant topics and then to investigate the area (based on topics) of hospitality services that is highly associated with relationship quality. This research represents an opportunity to fill the gap in the current literature, and clarify the understanding of guests’ affective states by evaluating all aspects of their relationship with a hotel. Design/methodology/approach This research focuses on natural opinions upon which machine-learning algorithms can be executed: text summarization, sentiment analysis and latent Dirichlet allocation (LDA). Our data set contains 47,172 reviews of 33 hotels located in Las Vegas, and registered with Yelp. A component-based structural equation modeling (partial least squares (PLS)) is applied, with a dual – exploratory and predictive – purpose. Findings To maintain a truly loyal relationship and to achieve competitive success, hospitality managers must take into account both tangible and intangible features when allocating their marketing efforts to satisfaction-, trust- and commitment-based cues. On the other hand, the application of the PLS predict algorithm demonstrates the predictive performance (out-of-sample prediction) of our model that supports its ability to predict new and accurate values for individual cases when further samples are added. Originality/value LDA and PLS produce relevant informative summaries of corpora, and confirm and address more specifically the results of the previous literature concerning relationship quality. Our results are more reliable and accurate (providing insights not indicated in guests’ ratings into how hotels can improve their services) than prior statistical results based on limited sample data and on numerical satisfaction ratings alone.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Light touch, heavy hand: principals and data-use PLCs

2017 ◽  
Vol 55 (4) ◽  
pp. 376-389 ◽  
Author(s):  
Alice Huguet ◽  
Caitlin C. Farrell ◽  
Julie A. Marsh
Keyword(s):  
Professional Learning ◽  
Low Income ◽  
Equal Opportunity ◽  
Data Use ◽  
Opportunity To Learn ◽  
School Level ◽  
Comparative Case Study ◽  
Data Set ◽  
Content Type ◽  
Principal's Role

Purpose The use of data for instructional improvement is prevalent in today’s educational landscape, yet policies calling for data use may result in significant variation at the school level. The purpose of this paper is to focus on tools and routines as mechanisms of principal influence on data-use professional learning communities (PLCs). Design/methodology/approach Data were collected through a comparative case study of two low-income, low-performing schools in one district. The data set included interview and focus group transcripts, observation field notes and documents, and was iteratively coded. Findings The two principals in the study employed tools and routines differently to influence ways that teachers interacted with data in their PLCs. Teachers who were given leeway to co-construct data-use tools found them to be more beneficial to their work. Findings also suggest that teachers’ data use may benefit from more flexibility in their day-to-day PLC routines. Research limitations/implications Closer examination of how tools are designed and time is spent in data-use PLCs may help the authors further understand the influence of the principal’s role. Originality/value Previous research has demonstrated that data use can improve teacher instruction, yet the varied implementation of data-use PLCs in this district illustrates that not all students have an equal opportunity to learn from teachers who meaningfully engage with data.

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