The effects of distance and individual differences in arousability on nonverbal involvement: A test of discrepancy-arousal theory

Mutual influence in human interaction refers to the tendency for persons to alter their verbal, vocal, and kinesic behaviors in response to the intensity, frequency or duration of those behaviors emitted by their partners. Numerous explanations of these processes have been put forward involving principles of approach-avoidance conflict, arousal, reward, and cognitive processes. These explanations are reviewed with special attention given to an arousal based theory, discrepancy-arousal. This theory holds that mutual influence is primarily the result of arousal changes due to the degree of discrepancy of partner's behavior from the receiver's expectation.The theory predicts that persons who differ in reaction to arousal should also differ in their response to a partner's immediacy. This hypothesis was tested on high and low sensation seekers under conditions of near and normal distances during an interview. Several different behavioral responses were coded, including eye gaze, posture and orientation, smiles and laughter, object- and body-focused gestures, vocalization, pauses, and latencies to respond. The hypothesized interaction did not materialize with a median split on sensation seeking. A measure of state-trait sensation seeking (STSS) was developed. The interaction between distance and a median split on the state-trait measure was significant for eye gaze and posture and orientation, with the high STSS subjects compensating less than the low STSS subjects.     

Structures of connectivity

From the earliest days of research on blockmodeling, one primary goal was to develop a method that would exploit the algebra of the social relations among individuals to describe the social structure of the group. This goal was reluctantly abandoned, however, because the presence of idiosyncratic relational ties in the data obscured the structure and no reliable method for identifying these ties existed. This paper presents such a method. We have found it useful to distinguish emotionally weak ties (Granovetter 1973), those with low intensity, from structurally weak ties, those that fail to conform to the pattern of ties that exist in and are reinforced by the social structure. The latter type of weak ties are the idiosyncratic ties that interfere with attempts to describe a social structure. This paper operationalizes the concept of structurally weak ties and presents an algebraic method for identifying them in sociometric data. This enables us to use the algebra of social relations to identify a blockmodel that describes the social structure of the group.     

Opportunities for minimization of confounding in observational research

Observational epidemiological studies are increasingly used in pharmaceutical research to evaluate the safety and effectiveness of medicines. Such studies can complement findings from randomized clinical trials by involving larger and more generalizable patient populations by accruing greater durations of follow-up and by representing what happens more typically in the clinical setting. However, the interpretation of exposure effects in observational studies is almost always complicated by non-random exposure allocation, which can result in confounding and potentially lead to misleading conclusions. Confounding occurs when an extraneous factor, related to both the exposure and the outcome of interest, partly or entirely explains the relationship observed between the study exposure and the outcome. Although randomization can eliminate confounding by distributing all such extraneous factors equally across the levels of a given exposure, methods for dealing with confounding in observational studies include a careful choice of study design and the possible use of advanced analytical methods. The aim of this paper is to introduce some of the approaches that can be used to help minimize the impact of confounding in observational research to the reader working in the pharmaceutical industry.     

Investigating emergent nested geographic structure in consumer purchases: a Bayesian dynamic multi-scale spatiotemporal modeling approach

Spatial modeling of consumer response data has gained increased interest recently in the marketing literature. In this paper, we extend the (spatial) multi-scale model by incorporating both spatial and temporal dimensions in the dynamic multi-scale spatiotemporal modeling approach. Our empirical application with a US company’s catalog purchase data for the period 1997–2001 reveals a nested geographic market structure that spans geopolitical boundaries such as state borders. This structure identifies spatial clusters of consumers who exhibit similar spatiotemporal behavior, thus pointing to the importance of emergent geographic structure, emergent nested structure and dynamic patterns in multi-resolution methods. The multi-scale model also has better performance in estimation and prediction compared with several spatial and spatiotemporal models and uses a scalable and computationally efficient Markov chain Monte Carlo method that makes it suitable for analyzing large spatiotemporal consumer purchase datasets.KEYWORDS: Clustering, dynamic linear models, empirical Bayes methods, Markov chain Monte Carlo methods, multi-scale modeling, spatial models     

On Extending Russell and Krajewski's Algorithm for Economic Purchase Order Quantities*

Russell and Krajewski presented an optimal purchase order quantity algorithm that considered the effect of the transportation rate structure for less-than-truckload (LTL) shipments. The authors applied the Russell and Krajewski algorithm to a variety of freight classes and lengths-of-haul. Anomalous cases were found in which the freight rate schedule, when used with the suggested algorithm, resulted in incorrect order size decisions. In this comment, the authors consider the impact of these anomalies on the optimal order quantity and associated total costs. A procedure is presented to adjust the Russell and Krajewski algorithm to arrive at the optimal purchase order quantity and the lowest total annual cost.