A Study to Investigate the Consumer Behavior and Cultural Dimensions of Engineering Students in Pakistan

The current study compares consumer behavior and Cultural Orientations between engineering and non-engineering students in Pakistan. Engineering students by virtue of their academic background are considered to have more technical know-how, more cognitive skills and can easily learn and adopt a new technology as compared to students from a non-engineering background. Furthermore the researchers were interested to find out that how the thinking skills and choice making of engineering students differ from other students and ultimately effects their consumer behavior and Cultural Dimensions. For this purpose three consumer behavior variables have been selected that are Customer Satisfaction, Customer Loyalty and Customer Switching. Cultural Dimensions are measured using the model proposed by Geert Hofstede. Two technologically sophisticated services are used in this study that is Mobile Phone and Debit Cards. The target population of the study consisted of 5000 students of which approximately 500 respondents were from various engineering universities in Pakistan. The comparison of consumer behavior and Cultural Dimensions differences was made through two group’s Discriminant Analysis. Differences in behavior and Cultural Dimensions have been reported among the engineering versus non-engineering students. Mobile Phone services satisfaction and loyalty were high among nonengineering students whereas engineering student’s registered higher satisfaction and loyalty in Debit Card services. Another interesting finding is difference in switching behavior. In case of both the services engineering students reported a higher mean score for switching. Score for Cultural Dimensions were also different among the two students type; whereby mean score for Masculinity was higher for engineering students as compared to other professions.

Culture plays a key role in shaping consumer behavior and henceforth a nexus is created between these two conceptual frameworks to study the impact these two have on the behavior of engineering students as consumers. The two services selected for this study are Debit Card services and Mobile Phone Services.
Services sector has been selected on the premise that it has made the most substantial contribution in overall economic growth of Pakistan. The share of services sector has augmented to 58.12% over last two years [2].Another major reason behind selecting services is the profile of target market which is the youth who not only forms 60% of population but is highly tech savvy and fond of using innovative products. Engineering students especially have more technical know-how, more cognitive skills and can easily learn and adopt a new technology and henceforth we base our proposition on the fact that there is significant difference in consumer behavior and Cultural Orientation between engineering and non-engineering students.
Researchers have found that engineering profession is highly masculine, individualistic and function oriented [3]. These traits may set them apart from students of other discipline. Based on this premise the current study seeks to understand that how the thinking skills and choice making of engineering students differ from other students and ultimately effects their consumer behavior and cultural dimensions. In addition to this the purpose of this research was to check validity and reliability of Hofstede Cultural Dimensions scale on various academic disciplines and report how cultural mapping is done on different engineering majors. Based on the mean scores it was found that engineering profession was more masculine, individualistic and highly function oriented [3].

LITERATURE REVIEW
This section reflects on the theoretical definitions and explanations for the key variables of the study.
Furthermore it also discusses the linkage between Cultural Dimensions and consumer behavior. The term 'Customer Satisfaction' has gained utmost importance for marketers and business practitioners as it not only promises higher economic returns but also customer revisit, repeat purchase and a long term loyal relationship. In case of services sector satisfaction has been defined as a "short term emotional reaction" [4]. Satisfaction has been defined as a feeling of acceptance, contentment, delight, respite and enthusiasm culminating in fulfilment of a particular desire or a need [5]. It normally results in when a products or a services' perceived performance matches buyer's expectations and excel leads to customer delight [6].
Satisfaction is considered as an antecedent of loyalty and marketing literature highlights the facts that satisfaction inevitably leads to loyalty [7]. The literal meaning of loyalty is fidelity, commitment or devotion [7]. In business context Customer Loyalty has been defined as customers' long term patronage, re-buy and favourable comments and recommendations about the product or service to friends or acquaintance [4]. The authors also assert that customer loyalties are not just about behaviour, but determine affinity, predilections and future intentions [4]. Customer Switching and Loyalty lowers Switching which is normally known as customer defection [7]. Understanding these concepts from perspective of engineering students is important because of their more rational approach and logical reasoning in decision-making [8]. A study conducted on system engineer's decision-making ability identified that mostly their decisions and choices are based on objectivity, rationality and facts [8]. In another study conducted on individuals with high technical skills identified that rational consumers have varied cognitive abilities and skills can envisage normatively loftier, more logical and consistent judgments and decisions [9].This is more applicable in case of risky decisions. Researchers have found that individuals that have high cognitive and numeracy skills are more thorough in their decisionmaking processes, which ultimately affects their buying behavior [11]. These findings may be applicable to engineers as their number processing ability is considered better as compared to individuals with other academic background.
As suggested by the literature cultural orientation of engineering students are compared with other education type to measure the differences [1,12,21]. considerably inspire consumer behavior whereby individuals belonging to a similar culture may share common thought process, language and choice of criteria [13]. Scholars assert that cultural values impact individual traits that affect their purchase behavior; product choice and decision making [14]. Cultural Orientations of engineering students was examined in a study conducted earlier [3].

STRATEGY OF RESEARCH
The current study is based on survey methodology that provides quantitative data. This approach aids in the depiction of trends, beliefs and attitudes of the target population of the study; in this case engineering students.
Discriminant Analysis which is multivariate tool is used to draw inferences and generalization about the population. Nomothetic explanations are attained that helps in accretion and summarization [15].

DATA COLLECTION & ANALYSIS TECHNIQUE
Primary data has been collected from different universities located in provincial regions of Pakistan through a personally administered questionnaire. Over all sample size of the study was 5000 which was 5% of the total population of students enrolled in HEC recognized universities all across Pakistan. The target population consisted of both engineering and non-engineering students. Of the total students sample 500 belonged to engineering discipline from HEC recognized universities. Details of data collection points for sample engineering students are mentioned in Table 1. Of the distributed forms 4202 were returned, thus the response rate was 84%. However, the actual number of questionnaires that were deemed useful after replacing for the missing values was 3663. The target population represents regional subcultures.
In order to achieve generalizability it was important that the data should be close to the overall trend [16].
Henceforth the pattern and relationship among missing values was identified and a procedural test was run to identify duplicate cases and unusual data. The missing data was replaced with median values. As noted by eminent scholar's blank response to the interval scaled data can be replaced by midpoint [17].  [16]. Pretesting of questionnaire was done before the actual field work.
Cronbach alpha was computed to check the reliability of the scales and all the constructs had an alpha score of above 0.6 which is deemed acceptable by researchers [18]. Validity checks were also conducted ensuring content and criterion validity. In order to validate the discriminant function authors have suggested the use of split sample validation through a hold out sample [16]. The analyzed sample size was 2566 which was 70% of the total sample size.
Hold out sample was 1097 which was 30% of total sample size.

DATA ANALYSIS
Data was analyzed using two group discriminant analysis.
In order to achiever discrimination variate weights of independent variables were calculated. This also helps in maximizing between group variance relative to within group variance. A weighted combination of all the scales of consumer behavior and Cultural Dimensions are used to predict differences in students according to their type of education. The results are shown in Table 2. The first table illustrates group statistics. The mean scores are calculated for all independent variables between nonengineering and engineering students. Mobile Phone Satisfaction is high in non-engineering students with a mean score of 3.29 as compared to engineering students that have a mean score of 3.11. Debit Card Satisfaction was high for engineering students with mean score of 2.97 as compared to non-engineering students score of 2.81. As far as Mobile Phone Loyalty was concerned nonengineering students were more loyal with a score of 3.25 versus engineering student's score of 3.23. Engineering students had a high mean score Debit Card Loyalty 3.03 as compared to non-engineering students score of 2.82. Mean score for Mobile Phone Switching was 3.25 for engineering students versus a score of 3.16 for nonengineering students. Debit Card Switching score was also high for engineering students having a mean score of 3.02 as compared to non-engineering students score of 2.83. Different mean scores were calculated for Cultural Orientations for both education types. Power distance score for non-engineering students was 3.14 and was 3.19 for engineering students. Collectivism score was high for engineering students with a value of 3.60 as compared to non-engineering students score of 3.51. Mean score for Uncertainty Avoidance for both education types was not very different; 3.28 for engendering students and 3.25 for non-engineering students. Same was the case with Masculinity, and Femininity scores between engineering and non-engineering students.
After calculating the group statistics next was reported the test of equality of group means. Loyalty was second significant variable for engineering students with F value of 10.50 and significant value less than 0.05. Followed by this was Debit Card Switching score for engineering students. The F value was 7.63 and significant value less than 0.05. Debit Card Satisfaction was significant for engineering student with an F value of 6.89 and significant value less than 0.05.Mobile Phone Switching was also significant at 90% confidence interval.
Amongst the Cultural Dimensions only Collectivism was significant for engineering students having an F value of 3.67 and significant value less than 0.05. Table 4 illustrates structure matrix which reports discriminant loadings and it is ordered from highest to lowest according to the size of loadings. These scores are useful for interpretation as they less affected by multicollinearity. Structure matrix also shows correlation between discriminate score and predictors. In the matrix below loadings of Mobile Phone Satisfaction is highest followed by Debit Card Loyalty, Debit Card Switching, Debit Card Satisfaction and Collectivism in order of loadings. An important point to note is that there is no difference in the scores of Structure matrix and Wilks Lambda. This does not violate the assumption of multicollinearity.     and Collectivism were significant for engineering students.

Mehran
Engineering students have a more rational approach in decision-making and this is reflected in their Debit Card usage behavior. Furthermore, Debit Card usage involves financial and numerical literacy, which is high among engineers as compared to students from other education type. Collectivism score was high in engineering students due to the nature of sample as the data has been collected from regions of concentrated culture where collectivistic values prevail. Overall it is concluded that most evident difference occurs in Debit Card services. One reason may be the inherent risk involved in financial services and usage of cognitive skills by engineers to select the right service provider. Debit Card services involve more risk as compared to Mobile Phone services. Engineering student's differences in financial services reflects on the fact that such individual have a better understanding of probabilities and 'what ifs' due to variation in their knowledge regarding risky decisions [19]. Moreover, the numerical literacy of engineers enables them to have a better understanding of potential risks and tradeoffs in financial offerings such as Debit Cards. Debit Card usage behavior is purely an economic choice and as noted by researcher's cognitive skills such as numeracy has greater bearing on decisions in economic context [10].
The distinctions between student's belonging to two distinct education types may have both theoretical and managerial implications. Theoretically the results of this study highlights the fact that discipline of education may lead to a discrimination between consumers groups especially for those who possess higher cognitive and numeracy skills. Marketers need to understand that cognitive reflections and numeracy skills affects consumers choice and decision-making. Such consumers are more logical and quantitative in their decision-making and as noted in an earlier study these types of consumers have a more profound and detailed information processing behavior which produces higher number of optimal choice [10]. The discernment between consumers of two education types calls for careful marketing strategies as consumers with high cognitive skills have a better selection and product choice mechanism [10,19,20].
Customer Loyalty and retention programs should be more objective, rendering them meaningful for such consumer groups. Moreover, such consumers are likely to make a

CONCLUSION
The key aim and objective of this paper was to understand consumer behavior and Cultural Orientations differences among engineering and non-engineering students belonging to university sector. This paper uses a quantitative technique and point of differentiation between the two students type was understood through Discriminant Analysis which is a popular technique in multivariate analysis. The sample students belonged to both engineering and non-engineering students. From the findings of this study we conclude that as far as consumer behavior is considered engineering students are more rational and logical in their choice making. Their cognitive skills make them informed consumer. Difference between the Cultural Orientations were not so meaningful except for collectivism which points to the fact that engineering students share a similar thought process in decisionmaking. Engineering students had a more acumen in registering their consumer behavior towards financial products. The findings of this study will be helpful to the marketers who can customize their offering keeping in mind the more logical and rational consumer group.