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This paper explores the potential for family businesses (FBs) to play a pivotal role in advancing the United Nations (UN) Sustainable Development Goals (SDGs). It seeks to elucidate how FBs' inherent strengths and values can be harnessed to integrate sustainable practices within their operational paradigms.

The authors employed a literature review to synthesize all the information and identify how FBs' desire to pass on a healthy company to future generations encourages sustainable practices.

FBs have the potential to contribute significantly to not only their own sustainability but also the broader well-being of society by aligning with the SDGs.

This paper provides practical insights for stakeholders, policymakers and business leaders seeking to foster a more inclusive and environmentally responsible economic landscape.

The problem of privacy protection in digital libraries is causing people to have increasingly extensive concerns. This study aims to design an approach to protect the preference privacy behind users’ book browsing behaviors in a digital library.

This paper proposes a client-based approach, whose basic idea is to construct a group of plausible book browsing dummy behaviors, and submit them together with users’ true behaviors to the untrusted server, to cover up users’ sensitive preferences.

Both security analysis and evaluation experiment demonstrate the effectiveness of the approach, which can ensure the privacy security of users’ book browsing preferences on the untrusted digital library server, without compromising the usability, accuracy and efficiency of book services.

To the best of the authors’ knowledge, this paper provides the first attempt to the protection of users’ behavior privacy in digital libraries, which will have a positive influence on the development of privacy-preserving libraries in the new network era.

The purpose of this paper is to investigate the role of trust management on the fundraising performance in reward-based crowdfunding.

A research model was constructed based on elaboration likelihood model (ELM) and literatures with five hypotheses developed. Data were collected from www.demohour.com - the first and one of the largest reward-based crowdfunding platforms in China. In total, 829 reward-based crowdfunding projects were analyzed to test hypotheses. To test the hypotheses, partial least squares was used to analyze data of entrepreneur/sponsor profiles, entrepreneur/sponsor behaviors, and crowdfunding projects.

Results indicated trust management significantly promoted fundraising performance via central (entrepreneur’s creditworthiness) and peripheral (entrepreneur-sponsor interactions) routes. The peripheral route (entrepreneur-sponsor interaction) showed significantly higher effects than the central route (entrepreneur’s creditworthiness). The finding aligns with authors’ assumptions derived from unique characteristics of reward-based crowdfunding – community and collaboration because personal, dynamic message interactions were more effective than static, historical success records on the trust establishment. In addition to the main effects, the results also showed entrepreneur’s prior success crowdfunding records positively moderated the effect of entrepreneur-sponsor interaction on fundraising performance.

This study is the first paper that reveals the value of trust management in reward-based fundraising, especially the effect of dynamic entrepreneur-sponsor message interactions. Entrepreneur-sponsor interactions not only promoted community benefits in crowdfunding, but also cultivated trust relationships between entrepreneurs and sponsors. Previous studies mainly focussed on the entrepreneur’s popularity level on third-party social media (such as Facebook) toward fundraising performance. This study examines the effect of direct entrepreneur-sponsor interactions on the crowdfunding platform. Additionally, this study found one moderating effect from the central route to the peripheral route. It is a rare case in studies based on ELM. Finally, this study demonstrates how to incorporate a theoretical framework guiding the analysis of structured and unstructured data for in-depth analysis, result interpretation, and corresponding intervention strategy development.

The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data.

An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification.

The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap.

This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well.

This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

Customer acquisition and retention methods are the most critical issues for any enterprise. By identifying potential customers and targeting them through marketing activities, enterprises can minimize marketing costs and maximize transaction probability. However, because market surveys are labor- and time-consuming, and data mining is ineffective for obtaining competitor data, enterprises may be unable to understand real-time changes in market trends and consumer preferences. The paper aims to discuss these issues.

This study developed a mechanism that automatically searches for potential customers in virtual communities. In addition, a common product attribute (CPA) model was developed based on the five dimensions of the theory of consumption values and a questionnaire survey was conducted to verify the corresponding relationships. Subsequently, the authors quantified and applied the relationship between the proposed CPA model and consumption values theory.

During the experiment, functional and social values yielded more accurate predictions. Contrary to our expectations, emotional value yielded an inaccurate prediction of potential customers. The overall precision was 0.74, with a threshold of 0.5.

Due to each industry including the distinctive characteristics and attributes regarding its products, the methods and models were only adopted in food industry for testing effectiveness.

Considering the food industry as an example, this study adopted the case study method to screen potential customers based on 400 articles from virtual communities, and combined a latent semantic analysis method with a backpropagation neural network to verify the effectiveness of the proposed method.

By adopting the proposed enterprise-product profile model, enterprises can compile basic information related to their products and industry. The proposed system can be used by enterprises to identify potential customers in areas with potential for market development.

Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues.

Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes.

A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori.

Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.

The purpose of this paper is to offer a theory-driven, evidence-based approach to developing a brand’s messaging strategies on social media encompassing three messaging dimensions, namely, appeal, content, and richness.

Using longitudinal data from three Fortune 200 companies – Delta Airlines, Wal-Mart, and McDonald’s – the authors empirically investigate comprehensive strategic messaging framework. Using ANOVAs and regression analyses, the authors test a set of hypotheses regarding the relations between a brand ' s purchase involvement, its message appeal, message content, and message richness, and engagement.

Findings reveal significant relations between purchase involvement and appeal. Furthermore, the authors find that abstract content categories are best combined with richer media. Finally, both transformation appeal and richer media have a highly significant and positive effect on engagement.

The authors offer a theoretical ground and empirical validation of both a comprehensive typology of content categories and a holistic strategic messaging framework that can fill a significant void in the social media marketing literature that lacks integrative models for assessing, classifying, analyzing, and in turn, informing future social media marketing strategies.

The validated framework can help managers better understand the diversity of messaging components as well as offer an analytical tool for assessing the nature of engagement associated with each appeal and category.

To the best of the author’s knowledge, this paper offers the first comprehensive typology of content categories and validates it in the context of a strategic messages framework using real-world data finding strong support for all hypotheses.

User-generated content (UGC), i.e. the feedback from consumers in the electronic market, including structured and unstructured types, has become increasingly important in improving online businesses. However, the ambiguity and heterogeneity, and even the conflict between the two types of UGC, require a better understanding from the perspective of human cognitive psychology. By using online feedback on hotel services, the purpose of this paper is to explore the effects of satisfaction level, opinion dispersion and cultural context background on the interrelationship between structured and unstructured UGC.

Natural language processing techniques – specifically, topic classification and sentiment analysis on the sentence level – are adopted to retrieve consumer sentiment polarity on five attributes relative to itemized ratings. Canonical correlation analyses are conducted to empirically validate the interplay between structured and unstructured UGC among different populations segmented by the mean-variance approach.

The variety of cognitions displayed by individuals affects the general significant interrelationship between structured and unstructured UGC. Extremely dissatisfied consumers or those with heterogeneous opinions tend to have a closer interconnection, and the interaction between valence and dispersion further strengthens or loosens the relationship. The satisfied or neutral consumers tend to show confounding sentiment signals in relation to the two different UGC. Chinese consumers behave differently from non-Chinese consumers, resulting in a relatively looser interplay.

By identifying consistent opinion providers and promoting more valuable UGC, UGC platforms can raise the quality of information generated. Hotels will then be able to enhance their services through the strategic use of UGC by analyzing reviews with dispersed low-itemized rating and by addressing the differences exhibited by non-Chinese customers. This analytical method can also help to create richly structured sentiment information from unstructured UGC.

This paper investigates the variety of cognitive behaviors in the process when UGC are contributed by online reviewers, focussing on the consistency between structured and unstructured UGC. The study helps researchers understanding emotion recognition and affective computing in social media analytics, which is achieved by exploring the variety of UGC information and its relationship to the contributors’ cognitions. The analytical framework adopted also improves the prior techniques.

The purpose of this paper is to explore how to use social media in e-government to strengthen interactivity between government and the general public.

Categorizing the determinants to interactivity covering depth and breadth into two aspects that are the structural features and the content features, this study employs general linear model and ANOVA method to analyse 14,910 posts belonged to the top list of the 96 most popular government accounts of Sina, one of the largest social media platforms in China.

The main findings of the research are that both variables of the ratio of multimedia elements, and the ratio of external links have positive effects on the breadth of interactivity, while the ratio of multimedia features, and the ratio of originality have significant effects on the depth of interactivity.

The contributions are as follows. First, the authors analyse the properties and the topics of government posts to draw a rich picture of how local governments use the micro-blog as a communications channel to interact with the public. Second, the authors conceptualize the government online interactivity in terms of the breadth and depth. Third, the authors identify factors that will enhance the interactivity from two aspects: structural features and content features. Lastly, the authors offer suggestions to local governments on how to strengthen the e-government interactivity in social media.