Being a game changer with happy workers: The Ziraat Bank example

Many companies have been implementing strategies in recent years to increase employee happiness, which has become one of their primary corporate objectives. Research has explored which initiatives are effective in promoting employee happiness, the extent to which happiness affects employee performance, and the importance of numerical evidence in this regard. Studies have found a positive correlation between employee happiness and organizational performance, making employee happiness rate a crucial indicator for measuring the health and sustainability of an organization. The aim of this research was to examine the connection between performance scores and happiness, drawing on Fisher's "happy-super worker" hypothesis proposed in 1980. Data analysis was conducted on seven quarters (21 months) of information from 4,277 employees, and the results confirmed that happiness and performance are positively correlated. The study revealed that happiness had a beneficial impact on performance at both low- and high-performance score levels, albeit with a gradual effect. In contrast, happiness had a more rapid impact on success at medium-performance levels. However, a positive association was found between happiness and success across all performance scores, including low, medium, and high performance. The study revealed a curvilinear relationship between happiness and performance. Furthermore, panel data modeling showed that an increase of 1 unit in individual happiness at any given time led to an average increase of 3.41 units in their performance score.

Driver's adaptive glance behavior to in-vehicle information systems

The purpose of this study was to examine the adaptive behavior of drivers as they engage with in-vehicle devices over time and in varying driving situations. Behavioral adaptation has been shown to occur among drivers after prolonged use of in-vehicle devices, but few studies have examined drivers' risk levels across different driving demands. A multi-day simulator study was conducted with 28 young drivers (under 30 years old) as they engaged in different text entry and reading tasks while driving in two different traffic conditions. Cluster analysis was used to categorize drivers based on their risk levels and random coefficient models were used to assess changes in drivers' eye glance behavior. Glance duration significantly increased over time while drivers were performing text entry tasks but not for text reading tasks. High-risk drivers had longer maximum eyes-off-road when performing long text entry tasks compared to low-risk drivers, and this difference increased over time. The traffic condition also had a significant impact on drivers' glance behavior. This study suggests that drivers may exhibit negative behavioral adaptation as they become more comfortable with using in-vehicle technologies over time. Results of this paper may provide guidance for the design of in-vehicle devices that adapt based on the context of the situation. It also demonstrates that random coefficient models can be used to obtain better estimations of driver behavior when there are large individual differences.

A multilevel approach to examining time-specific effects in accelerometer-assessed physical activity

OBJECTIVES

Popular methods for analyzing accelerometer data often use a single physical activity outcome variable such as average-weekly or total physical activity. These approaches limit the types of research questions that can be answered and fail to utilize the detailed, time-specific information available from accelerometers. This study proposes the use of multilevel modeling, which tested intervention effects at specific time periods.

DESIGN

The motivating example was the Active by Choice Today trial. Simulations were used to test whether the application of time-specific hypotheses about when physical activity intervention treatment effects were expected to occur (e.g., after-school hours) increased power to detect effects compared to traditional methods.

METHODS

Six simulation conditions were tested: (1) no treatment effects (to test the type 1 error rate), (2) time-specific effects, but no traditionally-tested effects, (3) traditionally-tested effects, but no time-specific effects, and (4) combinations of traditional and time-specific effects in 3 proportions.

RESULTS

Results showed the proposed multilevel approach demonstrated appropriate type 1 error rates and increased power to detect treatment effects during hypothesized times by 31-38 percentage points compared to traditional approaches. This was consistent across varying proportions of traditional versus time-specific effects, and there was no loss of power using the multilevel approach when only traditional effects were present.

CONCLUSIONS

The current study showed potential advantages of testing time-specific hypotheses about intervention effects using a multilevel time-specific approach. This approach may show intervention effects when traditional approaches do not. Future research should explore the application of this additional analytic tool for accelerometer physical activity estimates.