Digital or Innovative: understanding “Digital Literacy – Practice – Innovative Work Behavior” Chain

Digital transformation has put tremendous pressure on employees to innovate with the use of information technology (IT). This paper explores the extent to which digital literacy and personal innovativeness contribute to individual’s innovative work behavior (IWB). To test our hypotheses, we apply double bootstrapping chained mediation analyses paired with relative importance analysis on a dataset collected from employees (N = 167) in a pharmaceutical company. The results showed the existence of a double-mediation chain whereby digital practices and attitude toward digitalized innovation mediate the positive relationship between digital literacy and IWB. Surprisingly, said chain was not supported for personally innovative individuals, indicating that digital literacy plays a relatively more important role in stimulating attitudes toward digitalized innovation and IWB. Our findings add further specificity to research on digital natives and may help organizations understand the role of digital literacy and personal innovativeness in organizationally-relevant outcomes, such as IWB.

Understanding Drivers’ Steering Behavior: Chain And One-Time Corrections

Drivers’ steering adjustments can be categorized into one-time and chain corrections. One-time corrections lead to no further steering corrections for a minimum of one second, while chain corrections have at least two consecutive steering actions. Chain corrections represent a novel indicator of steering instability. Evolving vehicle dynamics along with drivers’ state and situational factors can cause these different correction types. In a driving simulator study, drivers’ experienced different roadway widths with and without distraction. The results show that higher steering wheel angle values at the beginning or end of a correction lead to chain corrections and the duration of these corrections tends to be shorter than adjustments not leading to chain corrections. Exploring the underlying causes of different corrections can guide efforts to model drivers’ control actions in recovering from distractions and in taking over control during automation failures.