Sweat it for sustainability: Impact of physical activity/exercise on sustainable consumption

What would make the “elusive” green consumer actually choose sustainable products? This research draws from “dopamine hypothesis of reward” to demonstrate that physical activity/exercise (regular and immediate) is a vital factor in determining consumers' sustainable behaviors. More specifically, it examines the role of physical activity/exercise on sustainable consumption behaviors as well as the role of emotional intelligence and sustainability claims. Five studies were conducted to test the hypothesized relationships. The findings from Study 1a and 1b establish direct causal link between physical activity/exercise and customers' sustainable consumption behaviors. Studies 2a and 2b show that emotional intelligence (trait-based and ability-based) may explain the effect of physical activity/exercise (regular and immediate) on sustainable consumption behaviors. Finally, the findings from Study 3 demonstrate that sustainability claims moderate the effect of emotional intelligence on sustainable consumption behaviors. These findings offer significant implications for retailers and marketers to use physical activity/exercise as a strategic intervention to positively influence consumers' evaluation and behaviors toward sustainable products

Brand logos versus brand names: A comparison of the memory effects of textual and pictorial brand elements placed in computer games

While a plethora of studies on gamification of advertising exists, little is known about how consumers process different types of brand elements (logos and names) placed in computer games, and whether differences in information processing lead to variations in brand memory. This gap is addressed by conducting three rigorous experiments. In Study 2 we find that, in general, brand logos lead to stronger memory than brand names – something known as the picture superiority effect. Study 3 examines the condition where the picture superiority effect is neutralized. We find that when the speed of a computer game is reduced, names and logos develop similar memory. Finally, in Study 4, we examine whether the picture superiority effect can be neutralized also in the context of high-speed games. We find that in fast games if the physical distinctiveness of the brand elements is increased, both logos and names yield in similar memory

Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

Cellulographics©: A novel smartphone user classification metrics

Despite the worldwide surge in smartphone use, there are no classification metrics based on its use. In this article, a comprehensive concept called ‘Cellulographics’ is introduced for characterization of smartphone users, which includes behavioral classification based on user characteristics like smartphone experience (SE), smartphone use skill (SUS), smartphone internet experience (SIE), smartphone use periods (SUP), smartphone screen time (SST), smartphone use frequency (SUF), smartphone use activities (SUA), and smartphone use location (SUL). This concept can be applied to any field of study without limitations, where smartphone use is involved