Search results

This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).

This study used Python to obtain data from the LEGO Innovation Community. In total, 285,849 reviews across 4,475 user designs between March 2019 and March 2021 were extracted to test this study’s hypotheses.

The ordinary least square regression analysis results show that review volume, review valence, review variance and review length all positively influence idea popularity. In addition, users’ in-degree centrality positively interacts with review valence, review variance and review length to influence idea popularity, while their out-degree centrality negatively interacts with such effects.

Drawing on the interactive marketing perspective, this study employs a large sample from the LEGO community and examines user design and idea popularity from a community member’s point of view. Moreover, this study is the first to confirm the role of online reviews and user network centrality in influencing idea popularity in OICs from a social network perspective. Furthermore, by integrating social network analysis and persuasion theories, this study confirms the interaction effects of review characteristics and users’ social network centrality on idea popularity.

This study’s results highlight that users should actively interact and share with reviewers their professional product design knowledge and/or the journey of their design to improve the volume of reviews on their user designs. Moreover, users could also draw more attention from other users by actively responding to heterogeneous reviews. In addition, users should be cautious with the number of people they follow and ensure that they improve their in-degree rather than out-degree centrality in their social networks.

This study integrates social network analysis and persuasion theories to explore the effects of online reviews and users’ centrality on idea popularity in OICs, a vital research issue that has been overlooked.

This study investigates how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' purchasing and gift-giving behaviors and how broadcaster popularity moderates social interaction-oriented content's effect on the two different behaviors in live-streaming commerce.

A research model was proposed and empirically tested using a panel data set collected from 537 live streams via Douyin (the Chinese version of TikTok), one of the most popular live broadcast platforms in China. A fixed-effects negative binomial regression model was used to examine the proposed research model.

This study's results show that social interaction-oriented content in broadcasters' live speech has an inverted U-shaped relationship with broadcast viewers' purchasing behavior and shares a positive linear relationship with viewers' gift-giving behavior. Furthermore, broadcaster popularity significantly moderates the effect of social interaction-oriented content on viewers' purchasing and gift-giving behaviors.

This research enriches the literature on live-streaming commerce by investigating how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' product-purchasing and gift-giving behaviors from the perspective of broadcast viewers' attention. Moreover, this study provides some practical guidelines for developing live speech content in the live-streaming commerce context.

This paper studies, based on the theory of service-dominant logic, the effect of value co-creation practices (linking and materializing) on engagement dimensions (popularity, commitment and virality). The main objective is to analyze the influence of value co-creation practices on engagement at international trade shows organizer association on Twitter.

This paper studies the usage of Twitter by the Specialty Food Association, which organizes one of the top five foods and beverage international trade show in the United States. To achieve the research objective, the authors have analyzed 1,608 posts on Twitter from the Twitter account @Specialty_Food. A content analysis was performed using Krippendorff's (2004) recommendations, and the data were analyzed using regression analysis with optimal scaling and Kruskal–Wallis Test.

According to the results, some materializing practices influence popularity, commitment, virality and global engagement on Twitter. While the usage of some linking practices influences respectively commitment and popularity.

These results provide valuable information for business-to-business (B2B) contexts and answer a research gap reported in previous literature, which affirms that more research is needed about the relationship between service systems and engagement. From a general view, to generate more engagement on social media in B2B contexts, it is recommended to prioritize posts that incorporate live and online events based on collaborative and dynamic human interactions, following by business ideas and business cases.

Human brands in the music industry use self-presentation tactics on social media to manage audience impressions. This practice has led to many posts asking followers to adopt behaviors favoring the human brand. However, its effectiveness in leveraging relevant performance metrics for musicians outside social media, such as popularity in specialized media, music sales and number of contracted concerts, needs further exploration. This study aims to reveal the effect of impression management tactics conveyed on social media on the market performance of musicians’ human brands.

Secondary data research classifies 5,940 social media posts from 11 music artists into self-presentation tactics (self-promotion, exemplification, supplication and ingratiation). It shows their predictions on three market performance metrics in an annual balanced panel study.

Impression management tactics via posts on social media are mostly self-promotion, improving the musicians’ market performance by increasing the number of contracted concerts. Conversely, ingratiation generated the most positive effect on the musician’s popularity but reduced music sales. Besides lowering the musicians’ popularity, exemplification also reduced the number of contracted concerts, while the supplication had no significant effect.

To the best of the authors’ knowledge, the research is the first to use social media postings of musicians’ official human brand profiles based on self-presentation typologies as a complete impression management tool. Furthermore, it is the first to test the effects of these posts on market performance metrics (i.e. outside of social media) in a longitudinal study.

The use of social media tools by companies is common, but the links between the use of multiple social media tools by companies and stock price changes are largely unknown. Therefore, this study aims to analyze the value-relevance of social media activities on Facebook (FB), Instagram (IG), LinkedIn (LI), Twitter (TW) and YouTube (YT).

Stock market data and hand-picked social media data in this study were collected from Finland, a small language area with consistent International Financial Reporting Standards (IFRS) reporting practices, in the expectation of better comparability and lower noise in the data.This study uses correlation, regression and factor analyses for a sample of 105 Finnish public limited companies listed on the Nasdaq Helsinki stock exchange.

This paper finds evidence that social media activity is an important area of analysis and that the activity and popularity of a company in social media are value-relevant variables in forecasting stock prices.

Not all social media activities are necessarily equally important for managers and investors. Focus on visual messages in social media is recommended.

The findings of this study highlight the value-relevance of using multiple visual social media channels, particularly IG and YT. This paper suggests avenues for future research and for analyzing social media information.

This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015.

The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology.

The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed.

Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

In 2008, a phenomenon emerged in online retail that attracted the attention of many people, creating a new virtual model of commerce. This phenomenon was called online group buying and arrived in Brazil in 2010. The purpose of this study was to identify the factors that affect satisfaction and loyalty to group buying sites.

Through structural equation modeling, the relationships between the attractiveness of discount rates, service quality, popularity, online brand image, antecedent word of mouth (WOM), creativity and trust in relation to consumer general satisfaction, declared loyalty, repurchase intention and positive WOM were observed. A total of 727 valid questionnaires were collected from online group buyers to test the 11 hypotheses proposed in this study.

The main contribution of this study was that it identified the strong influence of service quality, popularity and online brand image on consumer general satisfaction, and the influence of service quality, trust and general satisfaction on repurchase intention. It also identified the influence of trust, creativity and general satisfaction on declared loyalty, and finally, the influence of trust and general satisfaction on positive WOM.

Because of the extensive and robust model, other statistical interactions among the constructs were gauged. It is possible that new structures and paths for alternative models can be proposed in the future with the inclusion of new relationships not analyzed in the present study. Furthermore, future studies should consider testing the generated model in other countries, as the particular features of collective purchasing in Brazil should be taken into account.

In addition to being concerned with the attractiveness of discounts, marketing managers of group buying sites should concentrate their efforts on strengthening perceived quality, image, trust and the creativity of the site to ensure customer loyalty.

The online group buying business model that was established in Brazil differs significantly from the American and European models. As many online group buying studies have been conducted in these markets, Brazilian consumers will enjoy substantial gains in the quality of the service provided by collective purchasing sites through the development of actions that focus on improving the factors that affect these consumers.

The originality of this study lies in the development of a single model that tests a set of factors gauged separately in other studies related to online purchase behavior in a broader perspective.

The purpose of this paper is to provide comprehensive, theoretical and practical knowledge that will assist decision-makers in making informed decisions when promoting several religious sites in the Kingdom of Saudi Arabia (KSA). Specifically, this study examines the popularity of several religious sites, the personas of prospective visitors and their intentions to visit.

The study uses several methodological approaches to fulfil its main objective, namely, Google Trends analysis, K-means cluster analysis and linear regression analysis.

The results reveal that several religious sites in the KSA are popular and have potential for further consideration by various stakeholders. In addition, four personas were identified which can aid decision-makers and marketing practitioners in designing suitable plans for prospective visitors based on the participants’ motivation and demographics. Furthermore, a significant association was observed among three motivational variables (self-esteem, relationship and physiological needs) and the participants’ intentions to visit.

This study makes an original contribution to the literature, as it examines several religious sites in Saudi Arabia in addition to the sites that are part of the practices of Hajj and Umrah. Furthermore, this study provides comprehensive knowledge in this area to assist both future researchers and practitioners.

Recommender systems collect information about users and businesses and how they are related. Such relation is given in terms of reviews and votes on reviews. User reviews gather opinions, rating scores and review influence. The latter component is crucial for determining which users are more relevant in a recommender system, that is, the users whose reviews are more popular than the average user’s reviews.

A model of measure of user influence is proposed based on review and social attributes of the user. User influence is also used for determining how influenced has been a business being based on popular reviews.

Results indicate there is a connection between social attributes and user influence. Such results are relevant for marketing, credibility estimation and Sybil detections, among others.

The proposed model allows search parameterization based on the social attribute weights of users, reviews and businesses. Such weights defines the relevance on each attribute, which can be adjusted according to the search needs. Popularity results are then a function of weight preferences on user, reviews and businesses data join.

As human beings step into the age of information network, big data technology is constantly improving the intelligence level of various agents such as individuals and enterprises. The crowd decision-making of the intellectual community plays an important role in the active participation of many individuals and schools in giving their wisdom, effectively solve the problems of negative internet communication, single publicity media and unprofessional promotion team in WeChat public account.

This paper aims to optimize the content and improve the effectiveness of network ideological and political education in universities. This study analyzes five highly popular WeChat public accounts at the Central University of Finance and Economics in 2019. It obtains the popularity index of tweets using the WeChat communication index algorithm and finds that the important factors that influence tweet popularity are release time and content value.

To improve the public account tweets, this study highlights the connection between the tweets’ value and students’ emotional needs, which enhances the value of tweet content in students’ life and provides more original and distinctive content.

This study found that the content and interest of college students are tweet time, tweet value and tweet content. Therefore, the public account of college ideological and political education should be improved from the following three aspects: realizing the connection between the value of tweet content and students’ emotional needs; enhancing the value of tweet content in students’ life and learning; and insisting on the original and distinctive original intention of tweet content.