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Iyyappan, T.; Santhakumar, R.; Oli, G.A; Padmavathy, P.; Athithan, S.; Subash, P., and Ramkumar, S., 2024. Assessment of knowledge, attitudes, and constraints among ornamental fish entrepreneurs along the Tamil Nadu coast: A market-oriented approach. Journal of Coastal Research, 40(6), 1189–1200. Charlotte (North Carolina), ISSN 0749-0208. This study assessed the current level of knowledge, attitudes, and constraints towards market-oriented approaches among the ornamental fish entrepreneurs (OFEs) in Tamil Nadu, India. A validated questionnaire was given to 290 OFEs, and SPSS was performed for data analysis after pilot-testing. The results revealed a strong understanding of social media for marketing among OFEs, but with a moderate level of attitude toward market-oriented approaches. Respondents (4.33 mean) agreed on avoiding similar fish species production to prevent price crashes and demand slumps, which depicts their focus on consumer-oriented markets (4.31). Entrepreneurs (4.19) agreed that a successful producer prioritizes quality, attractive, and unique fish varieties in the market. They also agreed that social media is a prominent tool for business (4.3). The industry lacks quality brooders and seeds and struggles with a poor marketing system. A significant (P < 0.05) moderate correlation (r = 0.33) existed between knowledge and attitudes, but not with constraints. This study indicates the current state of market approaches and the access patterns of the OFEs in Tamil Nadu, and the results offer clarity for policymakers, planners, and stakeholders to further consider and develop market-oriented strategies for achieving successful business production and marketing in the ornamental fish industry. [ABSTRACT FROM AUTHOR]

The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method’s efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media. [ABSTRACT FROM AUTHOR]

Purpose: This research paper aims to explore how secondary school-aged business owners utilise social media to engage in informal learning. The authors make use of the concept of a self-directed experiential learning cycle to empirically explore adolescents' entrepreneurial learning processes without formal guidance or curriculum. Design/methodology/approach: The study adopts a one-on-one interview approach with a critical incident technique in interviewing to examine the experiences of 10 Grade 9–11 business owners who run social media-based businesses on Instagram. Findings: The results demonstrate that student business owners were able to capitalise on social media for venture creation and informal learning. They effectively engaged in experiential learning cycles with active help-seeking and mentorship in response to challenges in their business journey. A variety of resources within social media, their social circle and the internet were employed by students, highlighting the role the self-directed element plays in their experiential learning process. Practical implications: The research urges institutions to recognise the potential of informal learning on social media and offer more support to strengthen students' learning. Originality/value: This research represents the first exploratory study on the potential of school-age teens' self-initiated informal entrepreneurial learning while testifying the theory of the self-directed experiential learning cycle in the context of social media businesses. The study offers novel insights into the fields of students' informal learning, entrepreneurial learning and social media learning. [ABSTRACT FROM AUTHOR]

Today, social media is not only used for personal interactions, but also for business purposes. The business owner's understanding of using social media to post content as content. The higher the competence of the owner in using social media for his business, the more his business performance will improve. The understanding of social media is measured using the Technology Acceptance Model. This study uses the factor analysis method or uses Confirmatory Factor Analysis (CFA), where this method is used to see the relationship between the indicators expressed by the loading factor, the value of this loading factor then states the greatest contribution by the indicator in the formation of variables. The results of the study show that the main factor forming the competency variable is skill, while in Business Performance the main factor forming it is market growth. It can be interpreted that the maximum use of social media can increase the market because it can reach consumers widely. [ABSTRACT FROM AUTHOR]

The rise in the use of social media for businesses and sales has made it a valuable tool for customer acquisition, retention, and relationship management. However, the diversity it is used and extent to which its function improves performance remains underexplored in the literature. This study examines the effect of social selling on salespersons’ performance. Specifically, the study examines the effects of social media insights, connections, and engagement on sales performance. Data was collected from B2B salespeople in Anambra using a purposive and snowballing technique to collect data both online and offline. A structured questionnaire consisting of 5-items Likert scales questions adapted from the literature was used for data collection. The scales were validated using senior academics from a federal university in Southeast, Nigeria while Cronbach alpha was used to test reliability of the. Three hypotheses were formulated and tested based on dimensions of social selling using multiple regression analysis via SPSS version 22. The result showed that social selling insight and social selling engagement have a positive and significant effect on sales performance. While the effect of social selling connections was not significant. It was therefore recommended that firms should train salespeople on how to use various social media platforms and understand the nuances of social media use. It is also important that salespeople are equipped and encouraged to ‘listen’ to social conversations for mentions about their brands, industry, or competitors. Sales organization should also monitor conversations for opportunities to engage – that is, by identifying relevant conversation or trending topics. [ABSTRACT FROM AUTHOR]

This document provides a brief overview of the biographies of authors who have published articles in the journal "Information Systems Research." The authors come from diverse backgrounds and institutions, and their research interests cover a wide range of topics within the field of information systems. The document includes information about their educational backgrounds, current positions, and areas of expertise. These researchers have published their work in reputable journals and have made significant contributions to the field of information systems and technology. [Extracted from the article]

Purpose: Researchers, with the widespread acceptance of Web-based technologies by companies, have recently discovered a new type of social capital through these mass communication tools, but there is still limited knowledge about its formation. Therefore, this study specifically aims to conceptualize and validate brand social capital (BSC) by analyzing the role of the online brand community's social media capital (OBCSC). Design/methodology/approach: Research data was collected using a questionnaire with 39 closed-ended questions. Participants, among the 220 questionnaires distributed, only returned 140 acceptable questionnaires, indicating a response rate of 64%. The statistical population of the study included managers and employees of e-commerce companies active in social media in the field of B2C who introduce and sell their products and services on various types of social networking websites. This study performed data analysis using structural equation modeling with partial least squares. Findings: The results showed that OBCSC has a positive and significant effect on the integration of brand knowledge, branding co-creation and sense of belonging to the brand community, and in addition, using the mediating role of these three variables, it also has a positive effect on BSC. This study rejects only hypothesis 8 among all the hypotheses formulated, which shows that the sense of belonging to the brand community has no significant effect on branding co-creation. Originality/value: By conceptualizing a new phenomenon called BSC and how its conversion mechanism is, this research defines a specific and formulated path to better identify the results of the organizational use of social media. In addition, it significantly contributes to increasing managers' understanding of the importance of online brand community activities in internalizing customer brand knowledge within the company and turning it into wealth. [ABSTRACT FROM AUTHOR]

In this study we consider whether (a) image- and word-based communication modes and (b) warmth and competence cues vary in their relative influence on different levels of stakeholder engagement on social media. Specifically, we explore social media fitness influencers' abilities to attract followers and get followers to positively interact with them via posts and comments. We theorize that differences in the ways each communication mode is processed, and differences in how competence and warmth cues are perceived, will lead to different relative effects on lower- and higher-engagement behaviors. Using the social media platform Instagram, we followed 488 social media entrepreneurs in the fitness and nutrition industry for six months, and found that images have a positive relationship with less cognitively effortful engagement (following) whereas words do not have a significant relationship, and words have a stronger relationship than images with more cognitively effortful engagement (positive interactions). We also found that competence cues have a stronger positive relationship than warmth cues with the number of followers, and warmth cues have a positive relationship with positive interactions, whereas competence cues do not. Our findings have implications for research on multimodal communication, social judgments, and entrepreneur–stakeholder engagement. [ABSTRACT FROM AUTHOR]

The increased usage of social media in organizational settings has raised questions about its effect on job performance. This study explains the interplay between social media usage, transactive memory capability, employee creativity, and job performance within the Chinese organizational context. The rationale for this study stems from the need to understand how social media usage can enhance job performance through cognitive and creative processes within teams. On the basis of earlier literature, the survey questionnaire was designed to collect data and the survey included validated scales to measure these constructs. The data is collected from 816 employees in China via online resources like social media and emails and then Structural Equation Modeling is used for empirical analysis through AMOS software. The findings show the positive impact of social media on job performance while transactive memory capability and employee creativity mediates this relationship. These findings contribute to a better understanding of how social media can be leveraged to improve job performance. Practical implications highlight the importance of fostering transactive memory capability through social media platforms, facilitating employee creativity, and ultimately, bolstering job performance. Additionally, this study underscores the need for organizations to strategically harness the potential of social media as a catalyst for innovation and knowledge sharing, thus optimizing workforce performance in the digital era. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop and test the impact of a social media-based intervention for improving the business skills and income of young smallholder farmers. Design/methodology/approach: First, the researchers used an exploratory approach to develop a social media-based intervention for acquiring business skills and improving income. Second, the researchers tested the effectiveness of the developed programme on a sample of 506 young smallholder farmers. Finally, the intervention and data collection took place over five years (2017–2021). Findings: The result showed steady improvements in business skills and income from 2017 to 2021 for the treatment group, unlike the control group. Also, improvements in business skills led to a reduction in expenses and an increase in profit from 2017 to 2021. A further evaluation of the result showed that an addition of 5.1 mean scores in business skills led to the addition of $91 income between 2017 and 2018; for 2018–2019, 2.6 improvements in business skills increased income by $123. For 2019–2020, a 2.7 improvement increased income by $209, whilst for 2020–2021, a 1.6 improvement increased income by $320. Originality/value: The results of this study could help explore ways of using social media to change behaviour aimed at improving income amongst young smallholder farmers. [ABSTRACT FROM AUTHOR]

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To address the issues of high storage costs for human resource data, resource management of human resource systems, and employee social interaction in human resource management, this study proposes an adaptive intelligent HR data placement strategy based on genetic algorithms. Additionally, a fitness model and an employee social interaction model are presented. The fitness model utilizes genetic algorithms to optimize data storage strategy and server load balancing, resulting in reduced overall costs and efficient resource utilization. This is achieved by comprehensively considering data transmission latency and storage costs. The employee social interaction model analyzes social media blog post topics and employee ranks, and employs a co-training method to predict employee interactions. The experimental results indicated that the placement strategy can improve the HR data transfer by nearly 50% and effectively reduce the standard deviation of server load by about 10-15%. The accuracy, recall, F1 value, and precision of the employee social interaction model were 77.89%, 63.97%, 70.32%, and 71.78%, respectively. The proposed strategy and model demonstrated higher accuracy and robustness in predicting and analyzing employee social interactions, thus providing more reliable decision support for human resource management. [ABSTRACT FROM AUTHOR]

More and more companies are using social media in their marketing. They're spending a lot of money to make sales and connect with customers quickly, which helps their brands do better and gets more people visiting their websites. This paper wants to look at how people's feelings and thoughts about a brand affect how they act on social media. Knowing how different people think is really important for managers who spend money on marketing. This study looks at how what people think about a brand relates to what they say about it on social media and if they support the brand there too. The research uses special ways to study how brands behave on social media. It focuses on how people feel about brands and how this affects what they do online. This study found that how people feel about a brand is connected to what they do to support it online. It also showed that different kinds of people act differently on social media, which is helpful for companies to know. Understanding how people feel about brands and act on social media is super important for companies. The study's results give useful ideas to make social media strategies better and to get more people involved with brands online. [ABSTRACT FROM AUTHOR]

To examine how different features of corrective messages moderate individuals' attitudes toward misinformation on social media, a 2 (misinformation source credibility: high vs low) × 2 (corrective message source: algorithmic vs peer correction) × 2 (correction type: factual elaboration vs simple rebuttal) between-subjects experiment was conducted. To reduce perceived credibility and respondents' attitudes toward the misinformation, peer corrections were more effective than algorithmic corrections for misinformation from a source with lower credibility; for misinformation from a highly credible source, the superiority effect of peer corrections was still significant on perceived credibility but not on respondents' attitudes toward the misinformation. For the fact-checking tendency, we did not find a robust effect about how different features of corrective messages interacted. Our findings provide important insights into message design in combatting misinformation on social media. [ABSTRACT FROM AUTHOR]

In the digital era, the integration of technology within business processes is pivotal for organizational efficiency. This study investigates the impact of IT characteristics-specifically IT representation, IT reach, and monitoring capability-on the virtualizability of business processes, utilizing the frameworks of Embodied Social Presence Theory (ESPT) and Process Virtualization Theory (PVT). Our objective is to explore how these IT characteristics, through the mediation of embodied co-presence, enhance business process virtualizability in the context of collaborative tools. Addressing a gap in existing literature, we propose that beyond facilitating virtualization, IT characteristics deeply influence virtual processes by integrating human-centric experiences of co-presence. To examine this, we analyzed data from 311 Korean employees with remote work experience during the COVID-19 pandemic using Smart-PLS 4. Our findings indicate that IT representation and IT reach significantly contribute to business process virtualizability, mediated by embodied co-presence. Surprisingly, monitoring capability did not significantly affect either embodied co-presence or process virtualizability, challenging initial assumptions. This study bridges ESPT and PVT to offer new insights into the roles of IT characteristics in process virtualization, highlighting the importance of human-centric IT design. The results provide valuable guidance for businesses and developers of collaborative tools, underscoring the need to enhance virtual work environments through technology that fosters a sense of presence and collaboration. [ABSTRACT FROM AUTHOR]

In the digital age, social media (such as Weibo, Twitter), as a rich source of data, has become a vital platform for businesses to communicate and engage with their audiences. In this paper, we investigate crisis management effectiveness for enterprises using social media. To achieve this goal, a CRITIC‐EDAS (the abbreviation for criteria importance through inter‐criteria correlation‐evaluation based on distance from average solution) evaluation approach is proposed. First, from the perspective of Weibo, an evaluation index system for the effectiveness of enterprise crisis management is constructed based on the analysis of the factors affecting the effectiveness of enterprise crisis management. Then, the CRITIC method is used to determine the weights of the evaluation index, and the EDAS method is utilized to calculate the enterprise crisis management effectiveness value based on the weights of the evaluation index. Finally, a case study about four enterprises' crisis events is applied to verify the feasibility of the CRITIC‐EDAS approach. Furthermore, some existing problems of enterprise crisis management are summarized and some strategies of enterprise crisis management for these problems are provided. [ABSTRACT FROM AUTHOR]

The impact of social media on business performance of women entrepreneurs and their empowerment is examined in this research. Focusing on Sri Lanka, two research questions are addressed: (1) How are social media platforms empowering women entrepreneurs in Sri Lanka today, and (2) How can the use of social media be improved to support women entrepreneurs in Sri Lanka? A mixed-methods approach was used, with a survey (157 responses) and an online focus group (5 respondents) discussion. The statistical findings showed that business performance of women entrepreneurs mediates the relationship between use of social media and women’s empowerment. Qualitative analysis revealed that the use of social media transforms mindsets, facilitates knowledge sharing, provides motivation, supports business operations, promotes gender-neutral branding, and enables networking for women entrepreneurs in Sri Lanka to enhance the performance of their businesses, leading to empowerment. To enhance support, social media platforms should prioritize motivation, targeted networking, professional courses, and robust security measures. [ABSTRACT FROM AUTHOR]

This article establishes a theoretical link between the business model of social media and the resurgence of antiliberal populism. Through a novel set of tactics I term "identity biopolitics," political campaigns and foreign governments alike can identify voters as members of socioculturally differentiated populations, then target them with political messages aimed at cultivating voters' awareness of their particular disadvantage within the prevailing liberal order. Identity biopolitics exploits a positive feedback loop between targeting and content: the sociocultural differentiations liberalism declares politically irrelevant are used to target content that cultivates awareness of subjects' particular depoliticized disadvantage within the prevailing liberal order. The antiliberal populist exploits this condition to drive support for their political program. This article presents case studies of the Internet Research Agency and Cambridge Analytica during the 2016 general election in the United States to demonstrate the symbiosis between social media and antiliberal populism. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors' characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors' COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors' characteristics, examine tweet-related features, and train MLMs for prediction. This paper's main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement. [ABSTRACT FROM AUTHOR]

This article explores the court case Lindke v. Freed, which examines whether a public official's actions on social media can be considered state action. The case involves James Freed, the city manager of Port Huron, Michigan, who deleted comments and blocked users on his personal Facebook account. The court emphasizes that state action requires the exercise of actual authority on behalf of the state and discusses the need for a fact-intensive inquiry to determine if an official's social media activity qualifies as state action. The article also highlights the importance of considering the context and content of the speech and the impact of technology in analyzing state action. The other two documents discuss the time limit for monetary relief in copyright infringement cases and the applicability of the principle ne bis in idem to administrative penalties with a punitive purpose, respectively. [Extracted from the article]

In the transition from television to mobile devices, short videos have emerged as the primary content format, possessing tremendous potential in various fields such as marketing, promotion, education, advertising, and so on. However, from the available literature, there is a lack of studies investigating the elements necessary for the success of short videos, specifically regarding what factors need to be considered during production to increase viewership. Therefore, this study proposed the NLP-QFD model, integrating Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), and Quality Function Deployment (QFD) methods. Real short videos from mainstream Western media (CNN) and regional media (Middle East Eye) will be employed as case studies. In addition to analyzing the content of short videos and audiences' reviews, we will utilize the NLP-QFD model to identify the key success factors (KSFs) of short videos, providing guidance for future short video creators, especially for small-scale businesses, to produce successful short videos and expand their influence through social media. The results indicate that the success factors for short videos include the movie title, promotion, reviews, and social media. For large enterprises, endorsements by famous individuals are crucial, while music and shooting are key elements for the success of short videos for small businesses. [ABSTRACT FROM AUTHOR]

Summary: The study measured the Internet Financial Reporting (IFR) disclosure index and compared the results across three continents of the global e‐commerce business. In addition, it documents various social media platforms used by e‐commerce. We use content analysis with a scoring matrix based on content, timeliness, technology, and support used in websites and a one‐way ANOVA. The findings identified an average IFR e‐commerce disclosure index of 0.735, which is of good quality as it approaches the value of 1. There is no difference in index IFR between the three continental zones overall, but slightly different from non‐e‐commerce companies. The results also prove that websites and blog media still dominate the use of social media, while other social media platforms have not provided financial information. Researchers in accounting have not conducted research topics on social media, so there are still limited references and narrow analytical content. This research will interest the e‐commerce business industry and compile their financial reporting through the website to improve the quality of their IFR and financial access. Since the e‐commerce business is an internet‐based company growing significantly, it can use other social media to reveal its reporting as decent work and economic growth. This subject is relatively innovative because none of the IFR disclosure index studies focuses on e‐commerce businesses on social media. It fills the research gap related to the characteristics of e‐commerce businesses, where almost all activities are internet‐based. [ABSTRACT FROM AUTHOR]

The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based on the attention mechanism, learns the low-rank features of another modality through cross-modality, the eigenvectors are reconstructed for the corresponding modality through weighted averaging. When only the image modality in the dataset is used, the preprocessed data has outstanding performance in reconstructing the output model, with an accuracy rate of 87.6%. When using only the text modality data in the dataset, the reconstructed output model is optimal, with an accuracy rate of 85.2%. To improve feature fusion between modalities for effective classification, a weight adaptive learning algorithm is used. This algorithm uses a neural network combined with an attention mechanism to calculate the attention weight of each modality to achieve weight adaptive learning purposes, with an accuracy rate of 87.9%. Extensive experiments on a benchmark dataset demonstrate the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA), the process of determining the emotional tone behind a piece of text, has gained significant importance in various domains, including marketing, customer feedback analysis, and social media monitoring. Traditional SA models often face challenges handling diverse global datasets due to language variations, cultural nuances, and context differences. This paper proposes an Ensemble Recommendation System (ERS) approach to address these challenges and improve SA accuracy. The ERS combines Gated Attention-Based Recurrent Networks (GARNET) Steps with Transfer Learning. The ERS leverages the power of ensemble learning, combining multiple sentiment analysis models trained on global datasets from diverse linguistic backgrounds. The ERS can provide more robust and accurate sentiment classifications by aggregating predictions from these models. Additionally, the system utilizes a recommendation mechanism that dynamically selects the most suitable model based on the characteristics of the input text, such as language, tone, and context. In this paper, an advanced pre-trained model DistilBERT is used to train the selected datasets and apply transfer learning. Transfer learning is used to send the features extracted from training and sends them to the proposed approach ERS. Second, we design and train multiple state-of-the-art SA models integrated to handle specific linguistic attributes while also considering the cultural biases present in the datasets. Third, we introduce the ERS recommendation mechanism, which enhances the system's performance and optimizes computational resources. For the effectiveness of the proposed ERS, extensive experiments were conducted on benchmark datasets and comparing its performance with individual sentiment analysis models and conventional ensemble techniques. The performance of the proposed approach achieves superior sentiment classification accuracy, especially when dealing with challenging global datasets. Finally, ERS presents a promising solution for deep sentiment analysis over diverse global datasets. Finally, the ERS also focused on finding the popular items or products recommended by the proposed approach. [ABSTRACT FROM AUTHOR]

The development of the café business in Indonesia is currently proliferating, thus business people must be able to take advantage of the use of social media starting from promoting a business on social media such as on Instagram and TikTok applications. Where users can share user experiences on their respective account channels. This study aims to analyze how marketing communication through Content Marketing and Electonic Word Of Mouth on social media can influence purchasing decisions at Kopi Roemah Kesambi. This type of research uses a quantitative methodology approach. The population of this study is an unknown number of Roemah Kesambi Coffee consumers. The primary data sources used is data from the disseminated questionnaire. Samples were taken using incidental sampling methods using the Corchan Formula and obtained as many as 100 respondents. The data analysis techniques used are Validity, Reliability, Multiple Linear Regression Analysis and structural models measured using adjusted R square and hypothesis tests using SPSS statistical tool version 22. The results showed that Content Marketing Positively affected Roemah Kesambi's Coffee Purchase Decision. Elecronic Word Of Mouth has a positive influence on the purchase decision of Roemah Kesambi Coffee. This research can provide in-depth insight into the factors that influence the purchasing decisions of Roemah Kesambi Coffee consumers. These findings can be an important reference for business people and researchers to develop more effective marketing strategies. [ABSTRACT FROM AUTHOR]

Firms increasingly follow an "always on" philosophy, publishing multiple pieces of firm-generated content (FGC) every day. Current methodologies used in marketing are unfit to unbiasedly capture the impact of FGC disseminated intermittently throughout the day on stock markets characterized by ultra-high-frequency trading. They also neither distinguish between the permanent (i.e., long-term) and temporary (i.e., short-term) price impacts nor identify FGC attributes capable of generating these price impacts. In this study, the authors define price impact as the impact on the variance of stock price. Employing a market microstructure approach to exploit the variance of high-frequency changes in stock price, the authors estimate the permanent and temporary price impacts of the firm-generated Twitter content of S&P 500 information technology firms. The authors find that firm-generated tweets induce both permanent and temporary price impacts, which are linked to tweet attributes of valence and subject matter. Tweets reflecting only valence or subject matter concerning consumer or competitor orientation result in temporary price impacts, whereas those embodying both attributes generate permanent price impacts. Negative-valence tweets about competitors generate the largest permanent price impacts. Building on these findings, the authors offer suggestions to marketing managers regarding the design of intraday FGC. [ABSTRACT FROM AUTHOR]

In today's world, recommendation systems play a vital role in customer analysis on social media, online businesses, e-commerce, etc. There are multiple sources of information on the Internet giving people a large set of suggestions and advice. This may create confusion for the accurate decision to the user and he/she may get lost in the competitive and growing market. A recommendation system is an essential part of e-commerce to supply the filtered relevant information asked by the customer. The major pitfalls of the existing recommendation system are flooding unnecessary recommendations and unpredictability about new products. Most of the recommendation systems rely on the purchase history of the customer and give suggestions for new products. Along with the history of the user's purchase, it is crucial to analyze various other activities such as browsing history, wish lists, reviews, ratings, and previously ordered items. An intelligent recommendation system using ensemble learning is presented in this paper to reduce duplicate and irrelevant recommendations for the customer. The experimental results indicate that there has been a significant improvement in the precision and recall of the recommendation system in comparison with the other conventional techniques. [ABSTRACT FROM AUTHOR]

In Tanzania, the One by One: Target COVID-19 campaign was launched nationally in July 2022 to address the prevalent vaccine hesitancy and lack of confidence in COVID-19 vaccines. The campaign mobilized social media influencers and viral content with the ultimate goal of increasing COVID-19 vaccine uptake in the country. The objective of this study was to empirically assess the impact of the campaign on three outcomes: vaccine confidence, vaccine hesitancy, and vaccination status. Using programmatic data collected through an online survey before and after the campaign, we conducted a difference-in-difference (DiD) analysis and performed a crude, adjusted, and propensity score-matched analysis for each study outcome. Lastly, to observe whether there was any differential impact of the campaign across age groups, we repeated the analyses on age-stratified subgroups. Data included 5,804 survey responses, with 3,442 and 2,362 responses collected before and after the campaign, respectively. Although there was only weak evidence of increased COVID-19 vaccine confidence in the campaign-exposed group compared to the control group across all age groups, we observed a differential impact among different age groups. While no significant change was observed among young adults aged 18–24 years, the campaign exposure led to a statistically significant increase in vaccine confidence (weighted/adjusted DiD coefficient = 0.76; 95% CI: 0.06, 1.5; p-value = 0.034) and vaccination uptake (weighted/adjusted DiD coefficient = 1.69.; 95% CI: 1.02, 2.81; p-value = 0.023) among young adults aged 25–34 years. Among adults aged 35 years and above, the campaign exposure led to a significant decrease in vaccine hesitancy (weighted/adjusted DiD coefficient = -15; 95% CI: -21, -8.3; p-value<0.001). The social media campaign successfully improved vaccine hesitancy, confidence, and uptake in the Tanzanian population, albeit to varying degrees across age groups. Our study provides valuable insights for the planning and evaluation of similar social media communication campaigns aiming to bolster vaccination efforts. [ABSTRACT FROM AUTHOR]

Purpose: One of the most important challenges confronting enterprise managers is that of controlling employees' social cyberloafing. The use of enterprise social media entails opportunities for cyberloafing. However, previous research on how enterprise social media use affects cyberloafing is rather limited. Using the job demands-resources (JD-R) model, this paper proposes a research model to investigate the relationship between enterprise social media usage and employees' social cyberloafing behavior. Design/methodology/approach: Structural equation modeling was performed to test the research model and hypotheses. Surveys were conducted in an online platform in China, generating 510 employees' data for analysis. Findings: First, both public social media and private social media used for work-related and social-related purposes have a positive effect on employees' job engagement. Further, job engagement has a negative effect on employees' social cyberloafing. Second, the use of public social media for work-related and social-related purposes has no effect on employees' emotional exhaustion. However, work-related private social media usage has a negative effect on employees' emotional exhaustion, and social-related private social media usage has a positive effect on employees' emotional exhaustion. Further, employees' emotional exhaustion has a positive effect on employees' social cyberloafing. Third, there are significant differences in the effects of enterprise social media on employees' social cyberloafing between male and female employees. Originality/value: First, this paper contributes to the social cyberloafing literature by establishing a relationship between enterprise social media usage and social cyberloafing in relation to the dual influence mechanism. Second, it contributes to the JD-R model by clarifying how the use of enterprise social media with different motivations affects social cyberloafing through a mediation mechanism, namely, an enabling mechanism and a burden mechanism. Third, this paper also contributes to the social cyberloafing literature by revealing the boundary condition, namely gender, between enterprise social media use and employees' social cyberloafing. [ABSTRACT FROM AUTHOR]

Objective: the number of women-owned SMEs in the Maldives has increased as a result of the recent popularity of e-commerce and the Covid-19 pandemic. Thus, the purpose of this study was to describe the entrepreneurial experiences of female entrepreneurs in the Maldives in order to gain an understanding of the various stages of entrepreneurial process. Theoretical approach: the conceptual framework of this research was based theoretically on values derived from entrepreneurial theories and the components and features derived from the existing entrepreneurial process models. Method: this study employed a qualitative methodology, with semi-structured interviews conducted with five established female entrepreneurs. Purposive sampling technique was used to select participants. The data collected were analyzed using thematic analysis method. Results: the study's findings revealed that the conception of the entrepreneurial idea, the first stage of the entrepreneurial process, was primarily inspired by the entrepreneur's passion and hobby. In the second stage, it was discovered that no proper business planning had been completed, and the business was initially self-financed. In the third stage, it was discovered that the majority of entrepreneurs launched their businesses through social media and managed day-to-day operations as a small team. When it came to fourth stage, most of female entrepreneurs preferred both image and financial growth, with the majority of them relying on government funds for further business growth. Conclusion: this research will assist policymakers to gain insight into female entrepreneurs' entrepreneurial experiences and, as a result, in developing effective policies and programs to help them expand their business. [ABSTRACT FROM AUTHOR]

Purpose: By building and examining an integral model, the principal objectives of this research are to systematically explore how indirect and direct network externalities lead to user loyalty toward WeChat through the mediating effect of perceived gratifications. Design/methodology/approach: The data were collected through an online survey of 688 young people in Mainland China. To empirically assess the conceptual model, zero-order correlation analyses and structural equation modeling were carried out utilizing web-based data. Findings: Path analysis results demonstrate that indirect network externalities and direct network externalities exert a significant impact on users' hedonic gratifications and utilitarian gratifications. Moreover, the study discovers the significant mediating influences of utilitarian gratifications on the association between indirect network externalities and user loyalty. Research limitations/implications: Theoretically, this article may extend the scope of diverse studies on the association between network externalities and perceived gratifications and offer fresh insights into how mobile social media could actually improve user loyalty through enhancing perceived values among younger generation. Practically, this research assists mobile social media practitioners in retaining users and gaining competitive advantages over rival applications. Originality/value: Although the extraordinary growth of WeChat has successfully become the dominant media by which individuals develop interpersonal network and contact with others, the roles of perceived gratifications between network externalities and user loyalty toward WeChat have not yet been investigated in depth. These obtained outcomes not only enrich the existing literature regarding the relationship between network externalities and affective response, but also offer fresh insights to mobile social media designers, marketers and users. [ABSTRACT FROM AUTHOR]

With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains. This article is categorized under:Algorithmic Development > Text MiningApplication Areas > Business and Industry [ABSTRACT FROM AUTHOR]

INTRODUCTION: In the digital age, citizens' online political engagement is crucial for the development and stability of society. OBJECTIVES: To gain insights into and address the challenges facing political engagement today, where effectively assessing and promoting citizen participation in the context of the information explosion and the popularization of social media has become a pressing issue. METHODS: Using the binomial search tree algorithm, which was introduced to analyze and predict citizens' political behaviors in the online environment, able to dig deeper into citizens' concerns, opinions, and interaction patterns on political topics by collecting large-scale online data and applying it to the binomial search tree algorithm. RESULTS: The binary search tree algorithm is able to efficiently and accurately reveal the complex features of citizens' online political engagement. CONCLUSION: The binomial search tree algorithm is more advantageous than traditional methods, providing deeper insights for government policymakers and social scientists, and this study is essential for advancing the understanding and enhancement of citizens' online political engagement. [ABSTRACT FROM AUTHOR]

Purpose: We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience. Design/methodology/approach: Online interaction and the usage of social media have dramatically altered both consumers' behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers' behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers' different assessments of newly introduced products. Findings: By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established. Practical implications: From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities. Originality/value: The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products. [ABSTRACT FROM AUTHOR]

Purpose: Today social media capabilities have enabled businesses and enterprises to more collaboration, engagement and co-creation with their customers. So the current paper expands on this notion. The aim of this study is a bibliometric analysis to examine the trends of publications in the field of co-creation based on social media. Design/methodology/approach: To data collection of quantitative analysis, Scopus database was selected and the collected data were analyzed using Bibliometrix-package. The Web of Science also was selected to retrieve highly cited and hot papers for qualitative part of analysis besides top 10 Scopus highest citation per year documents on June 6, 2020. Findings: The results indicate insights into research trends pertaining to social media-based co-creation, as follows: starting jump to the publications occurred in this researches from the year 2008 and the growth trend is progressing in recent years; the stressful points are "co-design," "co-creation" and "value co-creation" and concepts such as "open innovation," "co-innovation" and "co-new product design" are new topics that guide future direction; the USA and UK are leaders in number of multiple and single publications; the most active and top journals that are better suited to achieving a high citation rate per year for a related paper were introduced. In addition, the top documents and highly cited papers were qualitatively analyzed on the basis of times cited per year. Research limitations/implications: The current study is not free of limitations. The database was limited to only Scopus. So the patterns and trends generated in the study may not be generalized to all social media-based co-creation research. Of course, the authors did not intend to ignore other contributions. It is mainly because of the number of documents retrieved from Scopus database and the coverage, Scopus was selected. Moreover, other types of research techniques such as correspondence analysis can be incorporated to generate additional meaningful insight. Originality/value: In this time of social media and user-generated content portals, co-creation through social media has become quite popular. So the main innovation of this study is providing a visual presentation of the trends and patterns in the evaluation of social media-based co-creation from the first document about the research area published till 2020. The results of this paper can shed light on the factors that strengthen the contribution of studies in a research area. Generally, the bibliometric items the authors analyzed essentially show the entire field picture and guide researchers toward understanding future trends to produce impactful studies. [ABSTRACT FROM AUTHOR]

Misinformation on social media platforms has become a pervasive problem in recent years, with the potential to have a significant negative impact on society. One of the most effective countermeasures against misinformation is social correction, which refers to attempts to correct the source of misinformation. Social media platforms' misinformation reporting function (MRF) can be regarded as a form of social correction. However, current research on MRFs is limited, and there is a need to understand better the factors that affect users' intentions to use them. In this study, we aim to address this gap by integrating the expectation confirmation model (ECM) and protection motivation theory (PMT) to develop a research model that explains users' intentions to use MRFs. ECM posits that users' confirmation or disconfirmation of expectations determines their satisfaction with a new product or feature. PMT, on the other hand, emphasizes the role of threat appraisal and response appraisal in motivating behavior. The proposed research model hypothesizes that users' intentions to use MRFs can be affected by their threat appraisal of misinformation (including perceived severity and susceptibility), their response appraisal of MRFs (including self-efficacy, response efficacy, and response costs), and their confirmation or disconfirmation of expectations about MRFs. We conducted a quantitative study using a questionnaire survey to test the proposed research model. The questionnaire measured users' threat appraisal, response appraisal, confirmation or disconfirmation of expectations, and intentions to use MRFs. The data collected from the survey were analyzed using structural equation modeling. The findings of this study will have important implications for both theory and practice. Theoretically, the study will contribute to a better understanding of the factors affecting users' intentions to use MRFs. From a practical perspective, the study will provide valuable insights for social media platforms on designing and promoting MRFs that are effective in reducing the spread of misinformation. We explore the use of social media misinformation reporting capabilities. If the data collected from the MRF can be treated as training data for machine learning models and sensor-based misinformation detection systems, it will be possible to deal more effectively with the phenomenon of misinformation in social media through natural language processing and image analysis. [ABSTRACT FROM AUTHOR]

This research aims to explore the influence of social media on digital business strategy. The research methodology used is literature research which involves exploration, evaluation and systematic analysis of relevant literature from various scientific sources. The choice of this method is based on its ability to provide a comprehensive approach to understanding social media in the context of digital business strategy. Research findings show that social media has a significant influence on digital business strategy. The use of social media can help companies build a strong brand, increase interaction with customers, and expand market reach. Apart from that, innovation also has an important role in digital business strategy. Companies that are able to innovate continuously can face challenges and opportunities in a rapidly changing business environment. This research concludes that the influence of social media, innovation and digital literature on digital business strategy is very significant. To develop adaptive and innovative business strategies in the era of digital transformation, it is important to understand and make good use of social media, continue to encourage innovation, and use digital literature as a source of insight and knowledge. [ABSTRACT FROM AUTHOR]