Do consumers acculturated to global consumer culture buy more impulsively? The moderating role of attitudes towards, and beliefs about advertising

This research advances knowledge that can foster understanding of how global consumer culture (GCC) and its elements relate to impulsive buying and sheds light on how advertising attitudes and beliefs interact with this main relationship. Specifically, this study examines the moderating effects of attitudes towards and beliefs about advertising on the relations between consumers’ level of acculturation to global consumer culture (AGCC) and impulsive buying. Consumers who are the most acculturated to GCC, and who have positive attitudes towards and beliefs about advertising buy the most impulsively. AGCC is related to increased impulsive buying even when attitudes towards and beliefs about advertising are negative. The paper contributes to the development of theoretical explanation of these understudied relations by employing acculturation theory and congruity theory. Social and practical implications are discussed

Consumption Style Among Young Adults Toward Their Shopping Behavior (An Empirical Study in Pakistan)

The purpose of this study was to substantiate the consumption styles of adolescents as customer. The study was executed in Karachi, Pakistan by applying consumption style inventory scale. The data covered of 1,048 respondents who are young and educated mostly students, which belong to the different universities in Karachi. The data was collected through structured and self administered questionnaire. To test objective Independent sample t test was used. The results show that young females are more shopping influenced, fashion conscious, recreational, and confused over-choice as compare to males whereas males are more reliance on media, perfectionist, brand conscious, and impulsive as compare to females for their consumption style toward shopping behavior. This research provides understanding about adolescents’ decision making style of consumers in Karachi which would enable organizations to make more appropriate strategies to cater youth consumers market

Technology transfer offices as boundary spanners in the pre-spin-off process: the case of a hybrid model

Over the past decades, universities have increasingly become ambidextrous organizations reconciling scientific and commercial missions. In order to manage this ambidexterity, technology transfer offices (TTOs) were established in most universities. This paper studies a specific, often implemented, but rather understudied type of TTO, namely a hybrid TTO model uniting centralized and decentralized levels. Employing a qualitative research design, we examine how and why the two TTO levels engage in diverse boundary spanning activities to help nascent spin-off companies move through the pre-spin-off process. Our research identifies differences in the types of boundary spanning activities that centralized and decentralized TTOs perform and in the parties they engage with. We find geographical, technological and organizational proximity to be important antecedents of the TTOs’ engagement in external and internal boundary spanning activities. These results have important implications for both academics and practitioners interested in university technology transfer through spin-off creation

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/