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Twitter is the most popular instant messaging platform today. Thanks to the consistency factor typical of it, it offers every company or business the best opportunity to promote their products or services. Likewise, it offers an unprecedented opportunity to public utility or benefit organizations like libraries to promote their services and build a powerful digital information exchange community. However, because of its immense number of users and chaotic nature, the steps toward making the best of it must be precise and systematized, or else the information professional will end up squandering resources to no avail. The purpose of this paper is the optimization of the distribution of the libraries’ resources regarding the promotion methods on Twitter.

Such a model is initially built by recording and analyzing the potential offered by the social network platform in question. Third-party tools that help handle Twitter are also analyzed. The factors’ total is then modeled by using the iThink editor. This editor is able to receive data from the decision-maker with respect to the specific attributes of each product and given conditions and produce specific outcomes by use of the model.

This research paper, includes a decision-making tool (model) that combines a theoretical view of library information science (LIS) and marketing science with a practical tool, simulating the process and producing certain outcomes using specific resources. The model presents him with the outcome that is bound to be produced with regard to the given circumstances and the amount of resources invested, encouraging, or not, certain courses of action.

The decision-maker can accurately anticipate an outcome so as to respectively customize his data and moves.

Proper use of the tools available, alongside anticipation chances offered by dynamic simulation models, enables the model decision-maker use it according to any given context, hence maximizing library business profits.

This research aims to investigate the current patterns of social media adoption for marketing in the restoration industry and analyse the strategies used by those restoration companies that have adopted social media. Social media marketing has been proven as a cost-effective way to engage new customers, and especially useful for small businesses. The disaster “restoration industry” is the special sector of general contracting that serves both commercial and residential property owners in terms of restoring their disaster-affected property. The restoration industry is characterised by small-business domination and constant need of new customers to survive in the market. Many restoration contractors have started adopting social media for marketing. However, goals and social media marketing strategies have not yet been clearly articulated.

Data were collected through a systematic content analysis (CA) of sample restoration companies’ Facebook and Twitter posts over a 30-day study period. After a preliminary investigation of the industry, the researchers selected the Restoration Industry Association (RIA) member companies, as the research population. Sixty companies were randomly selected from the 1,165 RIA member companies, which equals to 5 per cent of the population. Reliability was tested statistically using Cohen’s Kappa.

Three levels of adoption were derived from the data: active users, non-active users and non-adopters. More than half of the sample companies were found to have adopted at least one social media channel for marketing. However, only 26 out of the 60 sample companies were active on the social media that they have adopted. Active adopters showed, on average, relatively frequent usage patterns, with 10 Facebook posts and 37 tweets in a 30-day period. Sampled restoration companies which were active social media users posted mostly announcements and educational information of Facebook and used Twitter for news and direct interaction with customers. The observed companies revealed a mix of business-relevant content and personalised content including personal greetings, sharing of photos about holidays and fun events.

Three levels of adoption were derived from the data: active users, non-active users and non-adopters. More than half of the sample companies were found to have adopted at least one social media channel for marketing. However, only 26 out of the 60 sample companies were active on the social media that they have adopted. Active adopters showed, on average, relatively frequent usage patterns, with 10 Facebook posts and 37 tweets in a 30-day period. Sampled restoration companies that were active social media users posted mostly announcements and educational information of Facebook and used Twitter for news and direct interaction with customers. The observed companies revealed a mix of business-relevant content and personalised content including personal greetings, sharing of photos about holidays and fun events.

The results showed that social media adoption has not yet penetrated the restoration industry – the adoption rate of 65 per cent is significantly lower than the 77 per cent of companies in all industries. In-depth CA of active social media users revealed patterns of engagement on Facebook and Twitter. This paper built on those patterns to emphasise strategies restoration companies can use to build interpersonal relationships and trust, which can lead to increased word-of-mouth recommendations; facilitate customer contact through a cross-linking strategy; and engage in simple methods for search engine optimisation. These strategic recommendations are grounded in existing practices and, therefore, are feasible and accessible for disaster restoration companies.

The research results showed how the construction industry, especially small businesses, has adopted social media for marketing. A snapshot of the industry’s level of social media adoption for marketing strategy can provide a useful reference point about the industry’s practices and potential future directions. This paper provides suggestions about effective social media marketing strategies. The paper also illustrates the use of CA as a promising method for research in construction management.

This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.

First, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.

This study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.

This study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.

The purpose of this research is to investigate the use of Twitter in business as a medium for knowledge sharing and to crowdsource information to support innovation and enhance business relationships in the context of business-to-business (B2B) marketing.

This study uses a combination of methodologies for gathering data in 52 face-to-face interviews across five countries and the downloaded posts from each of the interviewees' Twitter accounts. The tweets were analysed using structural topic modelling (STM), and then compared to the interview data. This method enabled triangulation between stated use of Twitter and respondent's actual tweets.

The research confirmed that individuals used Twitter as a source of information, ideas, promotion and innovation within their industry. Twitter facilitates building relevant business relationships through the exchange of new, expert and high-quality information within like-minded communities in real time, between companies and with their suppliers, customers and also their peers.

As this study covered five countries, further comparative research on the use of Twitter in the B2B context is called for. Further investigation of the formalisation of social media strategies and return on investment for social media marketing efforts is also warranted.

This research highlights the business relationship building capacity of Twitter as it enables customer and peer conversations that eventually support the development of product and service innovations. Twitter has the capacity for marketers to inform and engage customers and peers in their networks on wider topics thereby building the brand of the individual users and their companies simultaneously.

This study focuses on interactions at the individual level illustrating that Twitter is used for both customer and peer interactions that can lead to the sourcing of ideas, knowledge and ultimately innovation. The study is novel in its methodological approach of combining structured interviews and text mining that found the topics of the interviewees' tweets aligned with their interview responses.

This paper aims to create a better understanding of the challenges posed by work from home (WFH) during the ongoing COVID-19 pandemic, to investigate the public sentiment toward this transition, and to develop a conceptual model incorporating the relationships among the factors that influence the effectiveness of WFH.

This paper uses netnography method to collect data from the Twitter platform and uses Python programming language, Natural Language Processing techniques and IBM SPSS 26 to conduct sentiment analysis and directed content analysis on the data. The findings are combined with an extensive review of the remote work literature to develop a conceptual model.

Results show the majority of tweets about WFH during the pandemic are positive and objective with technology and cyber security as the most repeated topics in the tweets. New challenges to WFH during pandemic include future uncertainty, health concerns, home workspaces, self-isolation, lack of recreational activities and support mechanisms. In addition, exhaustion and technostress mediate the relationship between the antecedents and outcomes of WFH during the ongoing COVID-19 pandemic. Finally, the fear of pandemic and coping strategies moderates these relationships.

This paper is one of the first efforts to comprehensively investigate the challenges of WFH during a crisis and to extend the remote work literature by developing a conceptual model incorporating the moderating effects of fear of pandemic and coping strategies. Moreover, it is the first paper to investigate the tweeting behavior of different user types on Twitter who shared posts about WFH during the ongoing pandemic.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

Until now, little research has been conducted to analyse Twitter conversations about the corporate sponsors of football clubs. The conventional and most widely used method has been to use content analysis to assess the sentiment of the tweets that were sent. However, this approach may be inadequate because sports fans may be unlikely to mention a corporate sponsor in the text they tweet. This study demonstrates the use of visual analytics to assess conversations about corporate sponsors by examining the images people tweet.

The purpose of this paper is to investigate young consumers’ perceptions toward Twitter and marketing information on Twitter.

The theoretical perspective guiding the study is interpretative phenomenology analysis, and the essay assignment and in-depth interviews were used to collect data.

Five themes emerged from the current data regarding the participants’ perception of Twitter: trendy and cool; adoption, incubation and action; dependence on smartphone; entertainment and fun; and low involvement and high frequency; and three themes emerged toward their interpretation of marketing information on Twitter: randomness, awareness builder and relevance and fun.

The previous research on the topic of Twitter and marketing is rather sporadic, lacking both breadth and depth. Specifically, two research gaps can be identified from the literature: first, companies and organizations’ strategic use of Twitter is the single focus of the previous research, and second, the dominate research orientation of the previous research has been quantitative, with a positivistic paradigm. The current study is designed to fill the above two research gaps by exploring young consumers’ interpretation of Twitter and marketing information on this particular social media platform from the perspective of consumers.

This paper aims to examine what type of messaging on Twitter is most effective for helping move social marketing beyond focusing on personal changes to find out what messages help turn members of the public into vocal advocates for these organizations’ social changes. Social marketing scholarship has regularly focused on how organizations can effectively influence changes in awareness and behaviors among their targeted audience. Communication scholarship, however, has repeatedly shown that the most influential form of persuasion happens interpersonally. As such, it is imperative that organizations learn how to engage audiences and facilitate the discussion about organizational messages between individuals. Social media provide platforms for such conversations, as organizational messaging can be shared and discussed by individuals with others in their networks.

Through a content analysis of 3,415 Twitter updates from 50 nonprofit organizations, this study identifies specific types of messages that are more likely to get stakeholders retweeting, archiving and discussing the organizations’ messaging through regression analysis.

Messages focusing on calls-to-action and community building generated the most retweets and Twitter conversation; however, they were also the least used strategies by nonprofit organizations.

Research has regularly examined the types of messages sent out by nonprofit organizations on Twitter, but they have not tested those messages against measures of engagement. This study pushes the understanding of social media communication to the next level by analyzing those message categories against metrics provided by Twitter for each tweet in the sample.