Menlo College

Purpose This study leverages theories of social influence to explore how “likes” for consumption-related content on social media fulfill consumers’ needs for social acceptance, subsequently affecting their repurchase and word-of-mouth (WOM) intentions. It aims to understand the extent to which social media engagements, specifically likes, serve as markers of social validation in the context of consumer behavior. Design/methodology/approach Our mixed-methods approach incorporated two experiments and an analysis of an archival dataset from a popular Chinese social media platform. This comprehensive methodology allowed for empirical validation of our hypotheses, combining experimental control with real-world relevance to scrutinize the impact of likes on consumption behavior. Findings The results of Studies 1 and 2 revealed that the number of likes significantly enhanced behavioral intentions regarding repurchase and positive word-of-mouth by fulfilling consumers’ need for social acceptance. Moreover, this effect was observed only under conditions of high (vs low) ideal social self-congruity. Study 3, conducted in a natural social media context, further validated these findings, demonstrating that likes positively influenced behavioral intention, with ideal social self-congruity acting as a boundary condition. Originality/value This study provides a novel perspective by directly linking social media likes to consumer behavioral intentions, specifically repurchase and WOM, through the mediating role of social acceptance and the moderating effect of ideal social self-congruity. By focusing on user-generated content (UGC) rather than marketer-generated content (MGC), it addresses a critical gap in the literature, emphasizing how receiving likes on consumer-shared content impacts content creators’ post-purchase behaviors, with practical implications for marketers on leveraging aspirational self-congruity to optimize promotional strategies.

The integration of artificial intelligence (AI) and social media has provided numerous benefits to businesses, including improved audience analysis and content optimization. However, AI has facilitated the spread of misinformation, emphasizing the importance of taking a balanced approach that considers both the technology's positive applications and its ethical risks. This paper looks at the intersection of AI and social media. The researchers use a mixed-method approach to analyze 1540 scholarly documents, combining bibliometric and systematic literature review techniques. The goal of this research is to identify the most important topics and trends, as well as potential business values and implications, in the AI Social Media domain. The first stage of the research involved a quantitative keyword co-occurrence analysis, which resulted in the identification of ten dominant themes. These include Conversational Agents & User Experience, Human Emotion and Content Recommendation & Moderation, Collective Intelligence in Emergency Management, Algorithmic Activism on social media, Deep Fakes and Fake News, Generative Artificial Intelligence, Algorithmic Bias in Content Moderation Systems, Deep Sentiment Analysis, Metaverse Technologies, and NLP & Mental Health Detection. Each identified theme is then subjected to a qualitative thematic literature review, which provides a more in-depth, context-specific understanding of the associated findings. Because of this comprehensive approach, the study provides a broad overview of the current state of AI social media, shedding light on the potential applications and far-reaching implications of this interdisciplinary nexus. The study's findings have the potential to shape strategic decision-making, policy development, and future research directions in this rapidly changing field.

Abstract In Lisa Ko’s award-winning novel The Leavers (2017), protagonist Polly Guo is a leaver, sometimes by circumstance, sometimes by choice. From the shores of the Minjiang to the bridges of the Harlem River, from the waters of the Atlantic to those of the Pacific, Polly wanders the globe with and without her son but always through and with the water. As such, Polly becomes a rare and pronounced example in Asian American literature of a mother-in-transition—or what we are calling the routed mother. As a routed mother, Polly attempts to use physical movement to escape the containment of heteropatriarchal, capitalist understandings of what it means to be a successful mother. Bringing oceanic studies into conversation with the socioeconomic context of Asian American motherhood, this paper argues that waterways highlight—albeit messily, muddily, shiftingly, much like water itself—the sources and strategies of Polly’s containment as a mother and her resistance to that containment. Simultaneously, water reveals Polly’s failure to do so but recasts that routing in a paradigm beyond the dichotomy of success and failure. Ultimately, this article argues that Polly’s story as a routed mother offers an important oceanic counternarrative of motherly success.

PurposeThe purpose of this study is to evaluate the global developments in the area of fintech solutions by analyzing Islamic and Conventional banks core accounting and market analysis IFIs and their impact on financial inclusion within its core markets.Design/methodology/approachThe authors collect and analyze annual accounting and market Data of the top ten largest Islamic banks and the top ten US Conventional banks, in terms of Total Asset and Market Capitalization, from Bloomberg Data.FindingsThe analysis of Bloomberg data shows higher risk-return for Islamic banks–except ROE Market measure that we suggest-than US conventional banks. Nonetheless, Islamic banking grew faster than conventional banking over the period 2006–2021. As a business model, we find that Islamic banks take more credit with more than seventy percent of their profit from loans, while US conventional banks struggle to reach seventy percent interest rate ratio. The authors’ research documents that Fintech and digitalization are driving Islamic finance growth during financial and economic downturns.Research limitations/implicationsFinTech data is not available for banks, further insights of analysis on FinTech and Innovations in the banking sectors.Practical implicationsIslamic banks continuously innovate to satisfy the users of their services and Fintech is opportune to innovation. This study could be interesting for both practitioners and academics wishing to understand and compare Islamic and conventional banking futures.Social implicationsThe authors compared two banking systems, the US and Islamic Banks, which could be useful for users to differentiate between those banking operations.Originality/valueThe authors collected accounting and market data from Bloomberg of top 10 Islamic and top 10 US Conventional banks from 2006 to 2021 to examine Risk-Return, Growth and Business Model of those banks. The authors propose a new Risk-Return measure ROE-Market and its volatility.

PurposeResearch and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.Design/methodology/approachThe authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.FindingsThe estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.Practical implicationsThis paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.Originality/valueThis paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.