This study examines the dual effects of co-creation in live streaming commerce by investigating how synchronous community interaction (danmaku), in the form of scrolling comments, questions, gift-sharing, testimonials and interactions, shape consumer responses. Integrating theories of co-creation, source effects and information overload, we propose and test a model of how community interaction in streaming can (1) co-create social impact such as trust and (2) build commercial impact such as engagement and purchase intention, but can also lead to co-destruction with excessive danmaku content with (3) social impacts that overwhelm viewers' information sensitivity to lower their trust and (4) distract from the streamers' commercial message to reduce conversion. Findings reveal that macro-streamers (>100,000 followers) directly enhance consumer outcomes. Yet when community chat danmaku becomes excessive, positive effects are attenuated due to information overload and disrupted viewer immersion, demonstrating a co-destructive effect. In contrast, micro-streamers build consumer trust gradually, and danmaku does not alter their impact. Their influence operates indirectly via trust-based parasocial bonds, reinforcing co-creative engagement. This research offers actionable implications for brands, platforms and influencers aiming to balance community engagement with message clarity to optimise consumer impact.
The incorporation of Generative Artificial Intelligence (GenAI) technologies into service delivery processes has surfaced as a transformative trend with serious implications for several industries and sectors. In response to the growing need for comprehensive frameworks to examine the impact of AI-driven service delivery systems, we propose the development of a GenAI service delivery scale. Grounded in socio-technical systems theory, the scale aims to measure consumer perceptions of GenAI in service delivery. The development of the scale holds significant implications for researchers, practitioners, and policymakers, providing a standardized measure of consumer perceptions of GenAI in service delivery. Through collaborative efforts and ongoing refinement, the GenAI service delivery scale aims to advance our understanding of consumer perceptions of AI-driven service delivery and contribute to the progress of best practices in the field.
Topic modelling is the algorithm of future to expand the horizons of domain knowledge in marketing due to two reasons: (1) its ability to derive marketing insights from burgeoning wave of text data, and (2) its thoroughness in conducting literature reviews to extract latent meaning out of extant research in marketing. This study carries out a comparative assessment of two cutting-edge unsupervised topic modelling algorithms: BERTopic based on bidirectional encoder representations from transformers (BERT), and latent Dirichlet allocation (LDA). The sample for this study includes text data generated from 200 research papers published in Web of Science (WoS) indexed journals during the 5-year period of 2019 to 2023. This labelled curated sample comprises of 50 published papers belonging to each one of the elements of marketing mix – product, price, place and promotion. The topic modelling outputs are evaluated based on a comparison of topical solutions obtained using BERTopic and LDA. The study presents categorical evidence pertaining to superiority of BERTopic over LDA as an unsupervised topic modelling algorithm.
The deregulation of the Australian dairy industry in 2000 triggered profound structural, economic, and social changes, leaving many farmers struggling to adapt. This study investigates how social capital shapes the resilience of the South East Queensland dairy industry in a challenging post-deregulation context. Adopting a situated resilience approach, we move beyond generic "resilience thinking" to ground our analysis in the lived experiences and perspectives of dairy farmers, recognizing that resilience is shaped by and experienced differently within specific social, political, and economic contexts. This perspective allows us to capture how power relations, industry structures, and local networks influence both the capacity to adapt and the distribution of benefits and burdens. Using a qualitative case study design, we conducted in-depth interviews to map the types and quality of farmers' social capital (i.e. bonding, bridging, and linking) and assess how these networks influence their ability to withstand shocks, innovate, and sustain their livelihoods. Findings reveal that strong bonding capital continues to provide critical peer support, bridging capital fuels innovation and market diversification, while linking capital has eroded due to power imbalances and declining trust in institutions. By integrating social capital into a context-specific resilience framework, this research advances resilience theory and offers actionable strategies for policymakers, industry bodies, and communities to rebuild weakened networks and strengthen the long-term viability of the industry.
