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1 – 10 of 102Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary…
Abstract
Purpose
Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.
Design/methodology/approach
Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.
Findings
Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.
Originality/value
This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.
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Élida Borges Rodrigues Gomes and Tatiana Monteiro Reis
This chapter demonstrates a parallel between the presidential campaigns of Jair Bolsonaro (Brazil, 2018) and Donald Trump (United States, 2016) regarding their use of social…
Abstract
This chapter demonstrates a parallel between the presidential campaigns of Jair Bolsonaro (Brazil, 2018) and Donald Trump (United States, 2016) regarding their use of social media. Specifically, tweets from the former candidates on the social network sites were analyzed during a one-month timeframe before their respective presidential elections. Tweets were selected for analysis based on the fact that Twitter was the main platform used by both former presidential candidates. The analysis of the data reveals considerable similarities between the communication strategies of the two candidates. This research enlists McCombs and Shaw (1972) agenda setting theory based on their study of media during North American presidential campaigns in 1968 and Lippmann’s (2008) concept of public opinion. The methodology employed is based on Bardin (2011).
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Yutaro Inoue and Shinsaku Nakajima
This study aims to investigate the relationship between consumer awareness of Zespri International Limited (Zespri™) and its sales promotion in Japan and the recent expansion of…
Abstract
Purpose
This study aims to investigate the relationship between consumer awareness of Zespri International Limited (Zespri™) and its sales promotion in Japan and the recent expansion of New Zealand (NZ) kiwifruit imported into Japan.
Design/methodology/approach
Tweets mentioning Zespri™ were utilised as a proxy of such awareness. They were first summarised using two text-mining techniques: tf-idf scoring and a co-occurrence network graph. Afterwards, the authors estimated a tri-variate vector autoregression (VAR) model consisting of the net imports of NZ kiwifruit in Japan, unit import price and number of tweets. Additionally, the occurrence frequency of tweets with “Kiwi Brothers”, promotional characters for Zespri™’s sales, was added to the model, and a tetra-variate VAR model was estimated. Finally, Granger-causality tests, an estimation of the impulse response function and forecast error variance decomposition was conducted.
Findings
All these variables were found to Granger-cause each other. Furthermore, a shock in the document frequency of “Kiwi Brothers” significantly affected Japan’s kiwifruit imports from NZ, explaining approximately 20% of future imports. Zespri™’s distinctive sales promotion was, thus, found to contribute in part to the recent increase in NZ’s kiwifruit export to Japan.
Originality/value
This paper is the first to apply text-regression methodology to food consumption research; it contributes to food consumption research by proposing a practical way to combine tweets with outcome variables using a time-series analysis.
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Wondwesen Tafesse and Anders Wien
ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of…
Abstract
Purpose
ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.
Design/methodology/approach
The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.
Findings
The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.
Originality/value
The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.
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Sanjana Arora, Jonas Debesay and Hande Eslen-Ziya
The COVID-19 pandemic has resurfaced challenges to gender equality and gender relations both worldwide and in Norway. There have been massive public discussions on social media…
Abstract
Purpose
The COVID-19 pandemic has resurfaced challenges to gender equality and gender relations both worldwide and in Norway. There have been massive public discussions on social media platforms, highlighting the potential of analysing public discourses in a non-reactive manner (Rauchfleisch et al., 2021). Further, discourses from social media may affect cultural representations and broad discourses in society (Rambukkana, 2015), such as that related to gender. In this article, by studying the Norwegian Twitter users' discussion on gender as related to COVID-19 pandemic, the authors will examine the everyday gendered discourses.
Design/methodology/approach
Data for this project were collected from the social media platform Twitter. The authors conducted the search on 16th November 2020, and that resulted in a total of 485 results, inclusive of both original tweets and replies. The data were analysed qualitatively using thematic analysis.
Findings
The thematic analysis of the tweets revealed three main categories which were mirrored in recognisable and widespread discourses about gender: (1) stereotypical gendered behaviours, (2) construction of masculinities and (3) othering. The authors argued that the stereotypes on gendered behaviour, traits and ideology together attribute to the maintenance of unequal gender structures.
Originality/value
This article explored discourses on gender on Twitter, the networked public sphere of Norway during the COVID-19 pandemic. Given that discourses both reflect and shape social configurations, they have the power to shape gender realities. With the transcendence of social media across geographic boundaries, the authors’ findings are relevant both for Norway and globally.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2022-0482
Viriya Taecharungroj and Ioana S. Stoica
The purpose of this paper is to examine and compare the in situ place experiences of people in Luton and Darlington.
Abstract
Purpose
The purpose of this paper is to examine and compare the in situ place experiences of people in Luton and Darlington.
Design/methodology/approach
The study used 109,998 geotagged tweets from Luton and Darlington between 2020 and 2022 and conducted topic modelling using latent Dirichlet allocation. Lexicons were created using GPT-4 to evaluate the eight dimensions of place experience for each topic.
Findings
The study found that Darlington had higher counts in the sensorial, behavioural, designed and mundane dimensions of place experience than Luton. Conversely, Luton had a higher prevalence of the affective and intellectual dimensions, attributed to political and faith-related tweets.
Originality/value
The study introduces a novel approach that uses AI-generated lexicons for place experience. These lexicons cover four facets, two intentions and two intensities of place experience, enabling detection of words from any domain. This approach can be useful not only for town and destination brand managers but also for researchers in any field.
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R.V. ShabbirHusain, Atul Arun Pathak, Shabana Chandrasekaran and Balamurugan Annamalai
This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.
Abstract
Purpose
This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.
Design/methodology/approach
A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.
Findings
This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.
Research limitations/implications
The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.
Practical implications
The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.
Originality/value
The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.
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Sung Hun Bae, Joonheui Bae and Seonggeun Jo
This research aims to examine some nudges for creating psychological ownership in order to reduce misbehaviors, consequently encouraging subsequent users to demonstrate…
Abstract
Purpose
This research aims to examine some nudges for creating psychological ownership in order to reduce misbehaviors, consequently encouraging subsequent users to demonstrate stewardship behaviors.
Design/methodology/approach
This research examined the sentiment of tweets (Study 1) to explore user experience and conducted two experiments (Studies 2 and 3) to test the hypotheses.
Findings
The misbehavior of the previous user in relation to the subsequent user's stewardship behavior was moderated by nudges based on self-investment and local identity. Perceived responsibility mediated the relationship between misbehavior and stewardship behavior as a result of nudges.
Originality/value
The findings of this study provide a framework for the transition from misbehavior to stewardship behavior in PMVs.
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Rachana Jaiswal, Shashank Gupta and Aviral Kumar Tiwari
Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering…
Abstract
Purpose
Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.
Design/methodology/approach
Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.
Findings
Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.
Research limitations/implications
This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.
Practical implications
Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.
Social implications
By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.
Originality/value
This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.
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Thamaraiselvan Natarajan, P. Pragha, Krantiraditya Dhalmahapatra and Deepak Ramanan Veera Raghavan
The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and…
Abstract
Purpose
The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.
Design/methodology/approach
The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.
Findings
The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.
Research limitations/implications
Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.
Social implications
In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.
Originality/value
The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.
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