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Past studies suggest that full-time maternal employment may be negatively related to children’s cognitive development. Most studies measure maternal employment at one time point, while mothers’ work hours may not be stable during early childrearing years. Using data from the 2001 Early Childhood Longitudinal Study – Birth Cohort (N ≈ 6,500), the authors examine stability in mothers’ work hours across four waves when children are 9 and 24 months old, in preschool, and in kindergarten, mothers’ background characteristics associated to it, and its link to child cognitive development. Results show that the majority of mothers change work hours across the four waves. Analysis using multinomial logistic regression models suggests that mothers’ older age, fewer children, and higher household income are related to working full time at all four waves compared to varying work hours across the waves; more children and less than high school completion are related to staying home at all four waves; and mothers’ older age, being White, no change in partnership status, and holding a college degree are related to working part time at all four waves. Compared to mothers’ changing work hours, mothers’ stable work hours, full time or part time, at all four waves is related to children’s better reading, math, and cognitive scores in kindergarten, whereas mothers’ staying home at all four waves is negatively related to these scores. These associations disappear when background characteristics are controlled for in ordinary least squares regression models. These findings underscore the role of background characteristics in shaping both mothers’ stable employment and children’s cognitive development.

The idea that a significant portion of what consumers do, feel, and think is driven by automatic (or “implicit”) cognitive processes has sparked a wave of interest in the development of assessment tools that (attempt to) capture cognitive processes under automaticity conditions (also known as “implicit measures”). However, as more and more implicit measures are developed, it is becoming increasingly difficult for consumer scientists and marketing professionals to select the most appropriate tool for a specific research question. We therefore present a systematic overview of the criteria that can be used to evaluate and compare different implicit measures, including their structural characteristics, the extent to which (and the way in which) they qualify as “implicit,” as well as more practical considerations such as ease of implementation and the user experience of the respondents. As an example, we apply these criteria to four implicit measures that are (or have the potential to become) popular in marketing research (i.e., the implicit association test, the evaluative priming task, the affect misattribution procedure, and the propositional evaluation paradigm).

Relying on Social Identity Theory (SIT), Relative Deprivation Theory (RDT) and System Justification Theory (SJT), this study aims at examining identity enhancement strategies of blue-collar workers who might be described as members of low-status groups having negative social identity. In the scope of this study, individual and collective strategies for identity enhancement of blue-collar workers have been proposed with the support of above mentioned theories. First of all, factors determining identity enhancement strategies have been described. Then, individual mobility conditions were explained and informal workplace learning was suggested as an individual mobility strategy for blue-collar workers to enhance social identity. In addition, it was argued that high self-monitoring blue collars are more likely than low self-monitoring blue collars to use individual mobility strategy. It was also emphasized that through union membership, high identifier blue collars will adopt a collective identity enhancement strategy. Finally, acceptance of low-status strategy was proposed for blue-collar workers in case they perceive their group’s low status as stable and legitimate.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

This study investigates the role of grit in a work setting as well as interrelationships among work-related constructs among frontline employees of hotels. Based on the framework of grit and work-related constructs, this study proposes and tests a model that attempts to understand the dynamic relationship among the two dimensions of grit, customer orientation, job satisfaction, and job performance, with an emphasis on the moderating role of organizational tenure. The results indicate that consistency of interest significantly influences customer orientation, whereas perseverance of effort significantly affects job satisfaction. Job performance is significantly influenced by customer orientation and job satisfaction. The paths from perseverance of effort to customer orientation, from perseverance of effort to job satisfaction, and from consistency of interest to job satisfaction are significantly moderated by organizational tenure.