Guest editorial

Abstract

In recent years, society, technology, politics, economics and many other factors have affected and changed the way consumers behave. Consumer behavior is a major topic of interest in the social sciences, particularly in economics and marketing, which emerged only about 50 years ago. Indeed, the first issue of the Journal of Consumer Research was published in 1974, and prior to the late 1970s there was no formal coursework in consumer behavior in colleges of business. With the growing complexity of consumer behavior theories, researchers are increasingly moving away from generalized, universalistic models to more individualized, yet multifaceted models (Codini et al., 2018). For example, there is much more interest in unraveling the contingencies that characterize the differences between subgroups of individuals or environments.Universalistic models overlook the fact that customers differ in their responses to marketing strategies and tactics, decision-making approaches and purchase motivations, which can easily lead to erroneous conclusions in consumer behavior studies. Understanding such contingencies and their effect on behavior requires a rigorous assessment of both observed and unobserved heterogeneity. Moreover, the practical utility of most marketing studies is limited to hypotheses testing of theoretical relationships embedded in a nomological net (Hair et al., 2018a). The result is most conclusions are skewed toward the believability of the hypotheses being tested in the model (Rigdon et al., 2020). Similarly, researchers are moving beyond the modeling of linear effects and increasingly examine complex nonlinear relationships among constructs of interest (

Traditional marketing analytics, big data analytics and big data system quality and the success of new product development

“…The composite reliability (CR) shows the extent that the indicator of the construct represents its latent construct. Therefore, the values for CR were found to be greater than 0.70, which is in line with the level suggested by Hair et al (2014) (see Table 2).…”

“…This study developed the framework and model based on Ringle et al (2014). The SEM-PLS technique is a two-step analytical procedure (Hair et al , 2014). The first stage is the measurement model (including the reliability and validity test), and the second stage is the structural model (hypothesis testing).…”

The Mediating Role of Safety Climate in the Relationship between Transformational Safety Leadership and Safe Behavior—The Case of Two Companies in Turkey and Romania

“…The model constructed according to Table 4 is significant (F = 122.43; p = 0.00). Furthermore, since the lower and upper confidence intervals in the established mediation model do not cover zero value [74,77,79,80,82,83], the mediation effect can be considered significant (Lower = 0.0825; Upper = 0.3684; Sobel Z = 5.3335; p = 0.00). Furthermore, in the Preacher and Hayes' mediation model, the explanatory power of the model will decrease due to measurement errors.…”

The Mediating and Moderating Effect on Smes' Performance: Does Religiosity Matter?

“…Table 5 shows that the R 2 of the performance is 0.375 (37,5%), and behaviour is 0.339 (33,9%). This scores are relatively low (Hair Jr et al, 2014) and the Q 2 predicts higher than 0. So the model has predictive relevance but in a low level (Hair et al, 2019).…”

“…To find the predictive power of the structural model R 2 value for each endogenous latent variable should be measure (Hair Jr et al, 2014). The greater the value, the better the predictor model explains variance.…”