Work-in-Progress

Impact of Technology on Consumption Experiences

In the past decade, the way we experience and consume things has changed dramatically. Whether it’s something as personal as how we eat or as fundamental as how we learn, technologies like AI, robotics, and video calls have become part of the everyday. I’m interested in how these changes are affecting the way we consume and engage with the world around us.

Project 1: Online Education And Personality

It is commonly believed that personality traits influence the preference for online learning. But, could the influence also flow in the opposite direction? In a randomized field experiment with business school students, we explore how the mode of course delivery (online versus in-person) might be shaping students' personality.

Project 2: Succeeding in Online Education

While online courses are celebrated for expanding educational access, do they benefit all students equally? Our randomized field experiment demonstrates that students with deficiencies in self-regulated learning (SRL) face detrimental outcomes in their academic performance when enrolled in online courses, unlike their counterparts in in-person settings. This suggests that a shift toward online education may disproportionately disadvantage students who require the most support.

Project 3: Video Effects and Online Learning

Online learning has surged over the past decade, with consumers turning to massive open online courses (MOOCs) and short-form tutorials on platforms like TikTok and YouTube. But could the effectiveness of these learning opportunities vary based on the video effects that now dominate digital content? This project investigates these questions by analyzing over 100,000 tutorial videos.

Project 4: Robotics in the Foodservice Industry

Companies are increasingly considering relying on robots to fill roles that are difficult to staff. However, could consumers perceive these uses of robots as unacceptable? If so, under what conditions is this more—or less—likely to occur? This project seeks to address these questions through a series of pre-registered experiments.

Reconsidering How We Study Consumer Behaviour

The past decade has also brought major changes in how we study consumer behavior. New technologies make it easier than ever to collect large datasets, and to engage in open science practices. Technological advances also make it possible for us to rethink how we analyze our findings, given that p-values are no longer the only feasible metric that we can rely on. My goal is to explore how we can do better research in consumer behaviour by making the most of these new tools and opportunities.

Project 1: Bayes Factors and the Field of Marketing

The frequentist paradigm and its p-values dominate marketing research, but they are also heavily criticized. This article makes the case for an alternative option: embracing Bayesian inference and Bayes Factors. While Bayes Factors are gaining traction in related fields and are easier to use than ever, marketing researchers are largely ignoring this trend. We offer a reader friendly overview for why now is the right time for marketing to take the leap. Specifically, we overview recent developments in the field across four stages of research: research motivation, data collection, findings, and communication. We discuss where frequentist methods fall short and how Bayesian inference can offer a remedy. We introduce the logic behind Bayes Factors and share two simulation studies.

Project 2: Sample Sizes in Consumer Research

Large sample sizes are often promoted as a panacea for producing stronger, more reliable evidence. But does bigger always mean better? To answer, we re-analyzed 6,974 statistically significant consumer behavior findings in four leading marketing journals (JCR, JCP, JMR, and JM). Consistent with their presumed benefits, we found that larger samples increase the likelihood that statistically significant results provide strong evidence for the alternative hypothesis (H1). However, we also document an under-appreciated risk: larger samples are more likely to yield statistically significant results that paradoxically offer more evidence for H0 than for H1. We conclude by outlining potential steps forward.

Project 3: Open Science Practices

Open science practices aim to make research more transparent and, in turn, more trustworthy. But because these practices are currently optional, they may do more than just boost reliability—they might also act as signals about the researcher or the research itself. In this article, we explore that possibility. Specifically, we ask whether open science practices like preregistration and data availability are more likely to be used when researchers feel confident in the strength of their evidence, thereby functioning not just as quality enhancers but as confidence signals.