Salesperson’s Personality, Motivation and Selling Performance : The Study of New Product Selling

In the highly competitive environment businesses invest big amounts of money into the new product development. New product success potentially depends on different factors among which salespeople play an important role. The aim of this paper is to explore the potential link between salespeople’s personality, motivation to sell new products and performance in selling new products. Based on the theoretical background of the Big Five personality dimensions, motivation and selling performance hypotheses were formulated and tested using statistical methods of correlation and regression analysis. The data was collected within one technologically intensive organization – ABB AB in Sweden using online web questionnaire and self-assessment measurements. Total investigation was conducted among organization’s salesforce. The findings confirm the importance of salesperson’s personality empirically showing that the latter significantly predicts both motivation and performance in selling new products. From all the Big Five Extraversion was confirmed to be the most important predictor of both motivation and performance in selling new products. Extraversion was found positively related with both motivation and performance in selling new products. Salespeople scoring high in Extraversion and especially possessing such characteristics as confident, energetic and sociable tend to be more motivated to sell new products and show higher performance results. Other personality dimensions such as Agreeableness, Conscientiousness, Neuroticism, and Openness to experience complexly approached are not proved to be significantly related neither with motivation nor performance in selling new products. The results are explained by the extreme importance of Extraversion in new product selling situation which analyzing in combination with the other personality dimensions suppresses the others. Finding regarding controlling for certain demographical characteristics of salespeople reveal that performance in selling new products is determined by selling experience. Salespeople’s age is not proved to be significantly related neither with motivation nor performance in selling new products. Findings regarding salespeople’s gender though proposing that males are more motivated to sell new products cannot be generalized due to the study limitations.

Långa toppar - metod för uttag av skogsbränsle i slutavverkningar : Slutrapport (STEM-projekt 21937-1)

SammanfattningHögskolan Dalarna har i samarbete med Skogsägarna Mellanskog, Naturbränsle i Mellan¬sverige AB och GDE-Net genomfört studier på en ny metod för uttag av skogsbränsle från slutavverkningar. Metoden går ut på att timmer tas ut som enda rundvirkessortiment. Resten av trädet, samt klenare träd som inte håller timmerdimension, tas ut som ett bränslesortiment. Metoden har jämförts med en konventionell slutavverkning med uttag av timmer, massaved och GROT-flis.Enligt genomförda försök skulle en avverkning enligt den nya metoden (långa toppar) ge ett högre drivningsnetto och drygt dubbelt så mycket bränsleflis som en konventionell avverk¬ning. En anledning till det högre drivningsnettot är att kostnaden för flisning blir lägre än vid flisning av GROT och att flisen betalas bättre än GROT-flis. Resultaten är beroende av de faktiska beståndsförutsättningarna och gällande prisrelationer mellan massaved och bränsle¬flis.Faktorer som har en positiv inverkan på drivningsnettot vid uttag av ”långa toppar” är t.ex. stora uttagsvolymer och korta terrängtransportavstånd samt bestånd med en hög andel virke av låg kvalitet eller udda sortiment som betalas dåligt på rundvirkesmarknaden.SummaryIn Sweden forest energy from final felling is traditionally harvested as logging residues after harvesting of timber (saw logs) and pulpwood, but depending on the market situation other methods with higher yield of forest energy might be of interest. Dalarna University has study a new method called “Undelimbed long tops” where only saw timber was taken out as an industrial assortment. The rest of the trees and smaller trees that don’t hold timber dimensions was left intact on the clear-felled area and been chipped later on. The study was done in different stands with some different conditions. The results have been compared with the traditional method for final felling. The surplus (forest owners net income) was higher in almost all stands when the method with “undelimbed long tops” was used, compared to the traditional method for taking out forest energy, and the volume of chips was more than doubled. A reason for the higher income from long tops is that the costs for chipping is lower and the prize of chips is higher compared to chips from logging residues. Other reason is that forest owners will not be paid for wasted pulpwood, but will be fully paid for the chips from such pulpwood. Factors that will have a positive influence on the ULT-method are for example large logging volumes and short distance between the logging area and the landing, different kinds of price reductions on pulpwood and large volumes of rotten wood or low paid industrial assortments.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.