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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.

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.

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.

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.

This paper aims to reveal the influence of the presentation of online product information (POPI) on consumer attitudes in the context of online buying digital products.

Two main experimental designs are used to collect data. The ANOVA, t-test and Bootstrap methods are applied to check hypotheses.

Findings of Study 1 indicate that if the POPI is combined with different types of celebrity endorsement (CE) (real vs virtual), the self-brand connection will be changed and further influence consumer attitudes toward digital products. Study 2 verifies the diverse moderating effects of the type of virtual CE. The CRP (central-route presentation) online product information with SVCE (super-realistic-digital virtual CE) can decrease consumer attitudes, while the PRP (peripheral-route presentation) online product information with AVCE (anthropomorphic virtual CE) can enhance consumer attitudes.

E-commerce enterprises should optimize the current layout of POPI by considering diverse matchings between POPI and CE to increase consumer attitudes. Moreover, marketers could make various schemes of POPI considering (virtual) CE and self-brand connection.

Findings contribute to understanding the relationship between POPI and consumer attitudes considering the mediation of self-brand connection and the mediations of virtual/real CE. Additionally, this study bridges the gap between research on virtual CE and business practices.

The goal of this study is to explore how content creators engage in critical data literacies on TikTok, a social media site that encourages the creation and dissemination of user-created, short-form videos. Critical data literacies encompass the ability to reason with, critique, control, and repurpose data for creative uses. Existing work on critical data literacies on social media has focused on understanding of personal data, critique of data use, and strategies to protect privacy. This work focuses on how TikTok content creators repurpose data to construct their own narratives.

Through hashtag search, the authors created a corpus of 410 TikTok videos focused on discussing environmental and climate action, and qualitatively coded the videos for data literacies practices and video features (audio, footage, background images) that may support these practices.

Content creators engaged in multiple practices to attach meanings to data and situate environmental and climate action discourse in lived experiences. While there were instances of no data practices, we found cases where creators compiled different data sources, situated data in personal and local contexts, and positioned their experiences as data points to supplement or counter other statistics. Creators further leveraged the platform’s technical features, particularly the ability to add original audio and background images, to add narratives to the collective discourse.

This study presents a unique focus on examining critical data literacies on social media. Findings highlight how content creators repurpose data and integrate personal experiences. They illustrate platform features to support data practices and inform the design of learning environments.

The purpose of this study is twofold. First, to estimate an optimal inflation rate for Ghana and second, to investigate factors that account for the differences between observed and target inflation.

The paper explored the questions within two econometric frameworks, the Autoregressive Distributed Lag (ARDL) and Threshold Regression Models using data spanning the period 1965–2019.

The study estimated a range of 5–7% optimal inflation for Ghana. While this confirms the single-digit inflation targeting by the Bank of Ghana, the range is lower than the central bank's band of 6–10%. The combined behaviours of the central bank, banks and external outlook influence inflation target misses.

The study urges the central bank to continue pursuing its single-digit inflation targeting. However, it implies that there is still room for the Bank to further lower the current inflation band to achieve an optimal outcome on growth and welfare. Again, the Bank should commit to increased transparency and accountability to enhance its credibility in attaining the targeted inflation.

The study is one of the first attempts in Africa in Ghana to estimate an optimal inflation target and investigate the underlying factors for deviation from the targets.

Understanding how to retain users of augmented reality (AR) shopping apps and to motivate them to purchase is vital to the success of AR apps. This study assessed the chain effect of AR attributes on purchase intention and reuse intention through cognitive and affective factors.

The data were collected from Thai users of the IKEA Place app using an online survey. A link to the survey was posted on Thai furniture groups on social media platforms. The 439 responses were analysed using the partial least squares (PLS) approach.

The results revealed that all four AR attributes, namely interactivity, vividness, novelty and spatial presence, significantly influence perceived enjoyment, perceived diagnosticity and perceived value. Brand attitude, as a key driver of purchase intention, is influenced by perceived value. Attitude towards the app significantly affects reuse intention and is affected by affective and cognitive factors.

The findings enable shopping app designers and marketers to successfully promote the brand, retain users and boost sales by effectively incorporating AR.

The study extends the literature on the impacts of AR apps on customer behaviours by including affective factors in addition to cognitive factors to explain why AR attributes influence customer attitudes and behaviours. Furthermore, the study demonstrates the serial causal paths from AR attributes to customer behaviours.

In the tourism industry, immersive technologies become increasingly vital, amplifying traveler experiences and industry growth. By studying “e-booking” applications prevalent in hotels, this study aims to analyze the impact of integrating an anthropomorphic virtual agent (AVA) on user perceptions of humanness and service usage intent.

Two experiments were conducted to examine the effects of using an AVA and explain the psychological mechanism of how AVA’s attributes increase intention to use “e-booking” application.

The results highlight the positive influence of AVA on the intention to use. They illustrate the psychological mechanism of how AVA’s attributes (agency and emotionality) influence perceived humanness and intention to use. More specifically, the results indicate that perceived humanness mediated the effect of an AVA on intention to use.

Further research should delve into additional capabilities related to humanness.

This study provides useful insights for hotels’ managers about incorporating AVAs in digital services to enhance the perceived humanness of AVAs. The findings suggest that such efforts could yield benefits, especially when they involve conveying that AVAs possess agency and emotionality.

To the best of the author’s knowledge, this study is the first to investigate how AVA impacts hotel human–computer interaction. It examines agency and emotionality features on humanness perception and behavioral intent. It also guides successful digitalized hotel service development and design, expanding existing research on human–virtual agent digital services, which mainly focuses on superficial traits like face and gender.

Social media marketing has become a powerful strategic tool for many brands, but scholarly research in this domain is still in its infancy. This study aims to examine the effects of social media marketing activities on consumer online impulse buying intentions via brand resonance and emotional responses by incorporating the direct and moderating effects of social network proneness toward fashion retail brands.

By using snowball sampling, this study recruited 441 netizens (who were using fashion retail brands) and obtained their responses through an online survey. Structural equation modeling was applied to 394 responses for analysis.

The findings discovered that social media marketing activities significantly influenced brand resonance, consumer emotional responses and online impulse buying intentions. Likewise, brand resonance and emotional responses were positively associated with online impulse buying intentions and acted as decisive mediators. Social network proneness’s direct and moderating effects significantly increased consumer online impulse-buying intentions toward fashion retail brands.

This study provides recommendations to retail managers for creating and executing brand positioning, segmenting and targeting strategies to enhance consumers’ intentions for engaging in online impulsive purchases for fashion brands.

This original research contributes to the branding literature and stimulus–organism–response theory by focusing on social media marketing activities, brand resonance, emotional responses, social network proneness and consumer online impulse buying intentions toward fashion retail brands.

The purpose of this study is to investigate the influence of augmented reality (AR) media characteristics on consumers’ purchase intention (PI) for fashion goods within the fashion industry context.

This study establishes five independent variables of salient AR media characteristics derived from existing studies which includes interactivity, vividness, augmentation, simulated physical control and environmental embedding. A quantitative online survey method is conducted with a sample of 172 respondents.

The findings suggest that all five AR media characteristics have a positive and significant influence on consumers’ PI for fashion goods. Among these five characteristics, interactivity and simulated physical control have the strongest positive impact on PI, followed by vividness, environmental embedding and augmentation.

This study provides valuable insights for fashion brands to better understand the media characteristics that consumers may be looking out for in AR experiences that could have an influence on their PI for fashion goods. This study also contributes to the literature by identifying the most influential AR media characteristics in the context of the fashion industry.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.