Reciprocal Relationships between Leader–Member Exchange (LMX) and Job Satisfaction: A Cross‐Lagged Analysis

While previous research has mainly emphasised the importance of leader–member exchange (LMX) to job satisfaction, there is a lack of research on reciprocal relationships between job satisfaction and LMX. In this study, we not only suggest that good LMX increases job satisfaction, but that job satisfaction can also enhance high‐quality supervisor–employee relationships. A full cross‐lagged panel analysis was used to test reciprocal relationships between LMX and job satisfaction. Employees (N= 279) of a large information technology company filled out questionnaires at two times, with a time lag of 3 months. In line with our predictions, findings revealed a positive relationship between LMX and job satisfaction both at Time 1 and Time 2. Moreover, LMX at Time 1 predicted the increase of job satisfaction at Time 2, and job satisfaction at Time 1 predicted the increase of LMX at Time 2. The results demonstrate the need to consider reciprocal relationships between job satisfaction and LMX when explaining employees' workplace outcomes. Our findings are discussed in terms of positive psychology theory.     

How much self-presentation behavior do applicants from the United Arab Emirates exhibit?

Applicants from different cultures vary in their self-presentation behavior during job interviews. This study investigates self-presentation behavior in the United Arab Emirates (UAE), the second largest economy in the Arab world. Specifically, it examines self-presentation behavior of applicants from the UAE and compares it to the behavior of American, European, and Chinese applicants from previous studies. The randomized response technique was used to gather self-presentation prevalence data of 111 UAE applicants regarding self-presentation behavior in their last job interview. Prevalence rates were lower than those from the United States and from China but higher than those from Iceland and from Switzerland. Results indicate that though UAE culture values modesty, UAE applicants still engage in distinct self-presentation behavior.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.