Regulating global capitalism amid rampant corporate wrongdoing—Reply to “Three frames for innovation policy”

Despite progress in science and technology and the economic prosperity achieved by numerous countries over the past century, contemporary global capitalism has left us with severe grand challenges for the future including rising inequality, global warming, modern slavery, child labor and several other human rights struggles. How can we fix them? For many years, scholars and policy makers alike have believed that economic growth (fueled by innovation) would fix institutional failures and lift people out of human misery. We now know that this story creaks. I would suggest that the current grand challenges are related in a non-trivial way to companies’ wrongful business conduct, especially that of large multinational corporations which have grown to rival governments in size, and have proven to be powerful agents capable of shaping the global governance agenda. I challenge technological determinism and ‘transformative change’ frameworks by arguing that the regulation of global capitalism needs to put powerful private actors at center stage since neglecting them will give rise to yet another generation of dysfunctional development and innovation policies.     

Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models

Since its introduction, television has been the main channel of investment for advertisements in order to influence customers purchase behavior. Many have attributed the mere exposure effect as the source of influence in purchase intention and purchase decision; however, most of the studies of television advertisement effects are not only outdated, but their sample size is questionable and their environments do not reflect reality. With the advent of the internet, social media and new information technologies, many recent studies focus on the effects of online advertisement, meanwhile the investment in television advertisement still has not declined. In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months. If we were able to predict purchase behaviors with models based on exposure time more reliably than with models based on demographic data, the obvious strategy for businesses would be to increase the number of adverts. On the other hand, if models based on exposure time had unreliable predictability in contrast to models based on demographic data, doubts would surface about the effectiveness of the hard investment in television advertising. Based on our results, we found that models based on advert exposure time were consistently low in their predictability in comparison with models based on demographic data only, and with models based on both demographic data and exposure time data. We also found that there was not a statistically significant difference between these last two kinds of models. This suggests that advert exposure time has little to no effect in the short-term in increasing positive actual purchase behavior.     

What makes a student feel vital? Links between teacher-student relatedness and teachers’ engaging messages

Recent studies suggest that teacher messages can affect students’ well-being. Using a multilevel, variable, and person-centred approach, this study aimed to identify profiles of students according to their teachers’ use of engaging messages and analyse the relation among these profiles and teacher-student relatedness and students’ subjective vitality. A total of 1209 students participated in the study. At the student-level, profile analysis indicated the existence of four different profiles: the few messages profile, the autonomous motivational appeals profile, the loss-framed messages profile, and the gain-framed messages profile. At the teacher level, profile analysis indicated the existence of two profiles: the variant and the invariant profiles. Results showed that overall, at both levels of analysis, teachers’ engaging messages related with teacher-student relatedness (either positively or negatively) with clear differences among profiles. Moreover, also at both levels of analysis, teacher-student relatedness related with students’ subjective vitality. Main findings and implications for practice are discussed.