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This paper aims to demonstrate how the design of data visualisations can act as a tool to support social marketing messages in prompting behaviour change to reduce food waste using the Elaboration Likelihood Model (ELM) as a theoretical framework. It also responds to a lack of consumer-led insight to develop campaigns in reducing food waste.

The research uses data collected by the End Food Waste Cooperative Research Centre (EFW CRC) in Australia to determine which text-based campaign messages are most likely to prompt people toward reducing food waste. Behaviour change messages were first identified through workshops with 11 food waste experts, then explored through online focus group discussions with 18 participants from three food-wasting market segments. The messages were further tested via a quantitative survey among 1,000 decision makers in Australian households in their own homes, with the top three performing messages examined using summative content analysis.

The significant findings were that participants want to see 1) evidence of how adopting new behaviours would lead to financial savings and benefit the environment, and 2) concrete steps to reduce food waste. When examined through the ELM, the findings suggest that tools that encourage both cognitive and peripheral processing as a means of persuasion, like data visualisations, may be useful for changing food-wasting behaviours.

Applying principles from the field of communication design to the ELM has uncovered the potential for a cross-disciplinary approach to enhance theoretical frameworks for understanding consumer engagement with messages. This process in turn, may lead to the development of more effective behaviour change marketing strategies.

Six principles for using data visualisations in a social marketing campaign are proposed: personal relevance, ease of use, emotional storytelling, context, prioritising the message itself and long-term usage.

This study proposes that data visualisations could enhance the effectiveness of social marketing campaigns by leveraging consumer-derived insights and the persuasive capacity inherent in their theoretical underpinnings.

This study aims to examine the factors that influence the adoption of public services through the latest smart apps designed to provide maximum services within urban local government.

The research is based on a thorough literature review, expert advice on public service delivery and four complementary theories. An empirical survey used a structured questionnaire to collect primary data from citizens in Indian local governments. The data was analysed using structural equation modelling (SEM) and SmartPLS version 4 to assess the measurement and structural models.

The study confirmed that service quality, trust, effort expectancy and social influence are key factors for adopting smart app services. Trust and intention are mediators, while performance expectations do not impact the decision to use smart app services.

The study includes a moderate sample size and a focus on a single developing country, which restricts its cross-cultural applicability. This research investigated the factors influencing individual adoption and use of local government services delivered through smart apps.

The study has significant implications for policymakers, researchers and implementers. It provides insights for developing strategies to increase the use of smart services, thereby enhancing local governance's efficiency, transparency and accountability.

This study enhances existing knowledge by adding service quality, trust and adoption to the model, which helps examine the factors affecting adoption. With limited research on local smart service app adoption, this study provides valuable insights.