Australasian Marketing Journal

The paper introduces the concept of the Digentity Human Dyad (DHD), a novel paradigm envisioning a future partnership between a human and their digentity—a personalized digital entity that embodies an individual’s values, preferences, and ideals. Unlike digital twins or extended digital selves, digentities are shaped by personal aspirational traits and enriched with the accumulated wisdom of humanity, guiding individuals in their decision-making processes. Enabled by advances in Generative AI, digentities will evolve alongside their human counterparts, provide context-aware and personalized advice, and transform decision-making from a purely human-driven process to a collaborative effort. As digentities align with their human counterpart’s goals, they will influence decisions across various aspects of life, including consumption, personal choices, and societal participation. Beyond individual impact, the DHD has transformative implications influencing how businesses, employers, governments, and civic systems engage with individuals. While the DHD presents significant opportunities, it also introduces challenges, such as risks of bias, data privacy, human over-reliance, and the potential for manipulation. The paper urges organizations and institutions to prepare for these shifts, calling for governance frameworks that ensure responsible AI integration and safeguard human autonomy in an era of human-digentity collaboration.

This study introduces a depth data approach for predicting individual compliance behaviours during the early stages of global crises, using the COVID-19 pandemic as a case study. Unlike large-scale behavioural data approaches that require extensive historical data, which is in most cases proprietary, or public voice data approaches that rely on explicitly posted crisis-related content, this approach leverages small-scale, publicly available digital footprints (such as Facebook public personal pages), to generate timely and actionable insights. Analysing a compact, consent-based dataset of 206 participants, we demonstrate that in-depth analysis of multi-dimensional social media traces can effectively predict compliance with key health measures during the pandemic. Our models outperform null and voice-based benchmarks, and the findings are operationally translatable into real-world targeting tools, such as Facebook’s advertising infrastructure based on users’ interests and exemplary user profiles. This approach offers a scalable and privacy-conscious solution for early-stage intervention, particularly when targeting non-voicing users is critical and large-scale behavioural datasets are unavailable. By prioritising timeliness, inclusivity and practical deployment, the depth data approach contributes a useful toolkit for early-stage crisis management.

As generative artificial intelligence (GenAI) continues to integrate into content marketing, it has significantly transformed the way firms create and distribute marketing content to customers. In this research, we first identify GenAI’s unique characteristics, including usability, flexibility, and productivity. Building on a review of GenAI’s technical basis and its application in content marketing practice, we propose a conceptual framework to explore the applications of GenAI in content marketing. The conceptual framework elaborates the antecedents and consequences of applying GenAI in content creation and distribution processes emphasizing the collaborative dynamics of between GenAI tools and the users, namely employee and customers. Specifically, characteristics of firm, employee, GenAI, task and customer, serve as antecedents of employee-Gen AI collaborated content creation and customer-Gen AI collaborated content distribution, and customer engagement as consequences. Finally, we discuss the potential concerns and challenges firms may face when applying and integrating GenAI into content marketing practices, such as issues related to fabrication, credibility, intellectual property, ethics, and safety. We also discussed potential moderating and mediating factors for future research.

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 increasing use of crowdsourcing platforms for behavioural research rests on the assumption that research participants are exclusively human. This assumption is now under threat. AI agents from browsers such as OpenAI’s Atlas and Perplexity’s Comet can autonomously complete online surveys. These agents can simulate specific personas or demographic profiles and follow survey prompts, select responses and submit data with fluency and internal consistency. Such capabilities threaten data authenticity and integrity, especially as subjective perception, motivation and emotion are central in behavioural research. This research note outlines practical mitigation strategies to detect AI responses. In addition to immediate measures, the emergence of AI-generated survey data requires broader methodological reflection, updated ethical guidelines and transparent reporting practices. We also situate these risks within the emerging literature on synthetic data, distinguishing unauthorised AI-generated responses from the transparent and theory-driven use of synthetic data for research purposes. Finally, we offer a forward-looking research agenda for protecting human data while responsibly engaging with synthetic data in marketing research. Instead of treating AI solely as a threat, researchers can use this as an opportunity to strengthen methodological rigour and protect the authenticity of human data in an increasingly automated research environment.

Partial least squares structural equation modeling (PLS-SEM) has gained prominence across disciplines for evaluating structural relationships among latent variables. Despite its widespread use, the application of conditional mediation (CoMe) modeling remains underutilized. This paper addresses this gap by offering a comprehensive guide on implementing CoMe models using PLS-SEM. We outline the conceptual foundations of CoMe, present a detailed step-by-step analytical procedure to apply CoMe, and provide practical interpretation guidelines. A case study of luxury counterfeit purchases illustrates the application of CoMe and demonstrates its enhanced ability to uncover complex relational dynamics. Additionally, we propose best-practice guidelines for researchers employing CoMe within PLS-SEM. By highlighting the value of CoMe in generating nuanced theoretical insights, this paper aims to encourage its wider adoption in empirical research accross disciplines.

Gambling is a major public policy issue in Australia, with Australians losing more money per capita on gambling than any other country. Given growing concerns over gambling expenditure, understanding how to predict consumer spending on gambling is crucial. This study compares the predictive power of Google search interest and self-reported data in estimating gambling expenditure. Analysing data from a range of secondary sources, we assess gambling behaviour across type (sports betting) and brands (BetEasy, Ladbrokes, Sportsbet) at the national and state level. Our findings indicate that Google search interest is a reliable predictor of gambling expenditure, providing real-time insights that align with actual spending patterns. In contrast, self-reported data show greater variability. Google search interest offers a stable, objective and cost-effective alternative to self-report surveys, enabling real-time monitoring of consumer behaviour and long-term trend analysis. These findings highlight its value for researchers and policymakers in understanding gambling behaviour to monitor the effectiveness of regulatory and harm reduction strategies.

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.

Effective financial decision-making is critical for individuals’ long-term well-being, yet many consumers struggle to save consistently. This paper investigates how personal saving orientation (PSO) interacts with message framing (promotion vs. prevention) of ads of financial products to influence saving intentions. Drawing on the Regulatory Focus Theory, we explore how consumers with varying levels of PSO respond to tailored financial messages across three experimental studies. In Study 1, we demonstrate that individuals with high PSO exhibit stronger saving intentions when exposed to prevention-focused messages, while those with low PSO are more motivated by promotion-focused messages. Study 2 delves into the psychological mechanisms driving these effects, revealing that perceived relevance and goal importance sequentially mediate the interaction between PSO and message framing on saving intentions. Study 3 adopts a first-person perspective by measuring PSO and using real ads, thus re-confirming the robustness of the effects. Our findings contribute to the literature on financial behavior by highlighting the critical role of PSO in shaping responses to financial message framing. Additionally, we offer actionable insights for financial institutions and policymakers, suggesting that aligning message framing with consumers’ PSO can enhance the effectiveness of saving promotions, ultimately encouraging healthier financial habits.

This paper aims to better understand how consumers navigate their attitudes and behaviour towards vice and virtuous products through the Theories of Customer Perceived Value and Consumer Engagement. Recent societal shifts, which have seen the surge of no- or low-alcohol wines and plant-based meat (PBM), among other products, provide an ideal context to test the hypotheses. We collected data from a representative sample of 665 Australian consumers in June 2023, including drinkers, abstainers, meat eaters and vegetarians. First, the results show that no-alcohol wines and PBM are significantly more virtuous than their counterparts. Second, if consumers believe that health benefits are associated with no-alcohol wine, PBM and regular meat, they are more likely to perceive value in those products. When comparing consumer groups – specifically meat eaters and vegetarians – it was found that social norms have a positive influence for meat eaters on the perceived value of PBM compared to vegetarians. Additionally, neophobia negatively impacts the perceived value of regular wine more compared to no-alcohol wine in consumers. Third, perceived value influences various dimensions of consumer engagement and purchase intent, but more for regular wine compared to no-alcohol wine.