Vichet Sam
email: samvichet77@gmail.com

Education-job mismatches and their impacts on job satisfaction: An analysis among university graduates in Cambodia

Keywords: vertical and horizontal educational mismatches, job satisfaction, sample selection bias, Heckman probit regression, higher education. JEL Codes: I23, I25, J24, J28

Education-job mismatches, especially overeducation or vertical mismatch, are generally found to lower the worker's job satisfaction, which may generate the counter-productive behaviors such as high rates of absenteeism and turnover in developed countries. The purpose of this article is to examine the impacts of educational mismatches from their both forms and dimensions (match, overeducation, horizontal mismatch and double mismatch) on the job satisfaction among university graduates in Cambodia. To deal with the sample selection bias owing to the unobserved job satisfaction of unemployed graduates, this study applies the Heckman probit model on a survey conducted with nineteen higher education institutions in Cambodia. Results indicate that the both forms of mismatches adversely affect the job satisfaction and the consequence is stronger if graduates suffer both vertical and horizontal mismatches. This suggests that the literature has to focus on all forms and dimensions of mismatches when examining their impacts on the individual outcomes in the labor market. The findings also underline the importance of improvement in the quality of education-job matching in Cambodia because the possible counter-productive behaviors due to inadequate education-employment may affect the firm productivity and thus limit their development.

Introduction

The average level of education has risen successively and considerably worldwide during the past several decades [START_REF] Barro | International data on educational attainment: Updates and implications[END_REF]OECD, 2014). This increase in educational levels has positively contributed to individual earnings and economic growth as predicted by the human capital theory [START_REF] Becker | Human capital: A theoretical and empirical analysis, with special reference to education[END_REF] and endogenous growth theory [START_REF] Lucas | On the mechanics of economic development[END_REF]), yet vertical educational mismatch or overeducation also has emerged as a serious concern, particularly in developed countries. Overeducation refers to an excess of education, beyond the level needed to perform a certain job [START_REF] Rumberger | The rising incidence of overeducation in the US labor market[END_REF][START_REF] Hartog | Over-education and earnings: Where are we, where should we go?[END_REF]. Besides overeducation, horizontal mismatch also exists when people's occupations do not match their fields of education [START_REF] Robst | Education and job match: The relatedness of college major and work[END_REF]. The existence of these mismatches raise questions on their effects on individual outcomes in the labor market such as the job satisfaction.

From the sociological perspective, education-job mismatches adversely affect job satisfaction because the worker's expectations on the social position and type of work are not fulfilled like they thought when they invested in their higher education [START_REF] Capsada-Munsech | Overeducation: Concept, theories, and empirical evidence[END_REF].

Indeed, there exist empirical evidences on the negative impacts of overeducation on job satisfaction, which can reduce the workers' productivity. [START_REF] Tsang | The impact of underutilization of education on productivity: A case study of the US bell companies[END_REF] finds that one additional unit in mean years of overeducation decreases the job satisfaction by 3.3%, and one percent decrease in the value of job satisfaction is associated with a 2.5% decrease in the level of the Bell firms' output in the United States. The negative impacts of mismatches on job satisfaction also exist in different European countries [START_REF] Battu | Overeducation among graduates: A cohort view[END_REF][START_REF] Verhofstadt | The impact of education on job satisfaction in the first job[END_REF][START_REF] Verhaest | The impact of overeducation and its measurement[END_REF][START_REF] Fleming | I'm too clever for this job: A bivariate probit analysis on overeducation and job satisfaction in Australia[END_REF][START_REF] Diem | Overeducation among graduates from universities of applied sciences: Determinants and consequences[END_REF][START_REF] Congregado | Incidence, effects, dynamics and routes out of overqualification in Europe: A comprehensive analysis distinguishing by employment status[END_REF], yet several limits remain in the literature. First, there is no consensus given that some researches such as Badillo [START_REF] Badillo Amador | Education and competence mismatches: Job satisfaction consequences for workers[END_REF], [START_REF] Green | Overqualification, job dissatisfaction, and increasing dispersion in the returns to graduate education[END_REF], [START_REF] Sloane | Overeducation, skill mismatches, and labor market outcomes for college graduates[END_REF] do not find any impacts. Indeed, some workers may prefer that type of job in compensation for other job attributes such as less job pressure for which they may have stronger preferences [START_REF] Mcguinness | Labour market mismatch among UK graduates: An analysis using REFLEX data[END_REF].

Second, less researches analyze the case of horizontal mismatches even though their effects appear to be comparable to the vertical form [START_REF] Domadenik | Horizontal mismatch in the labour market of graduates: The role of signalling[END_REF]. For instance, [START_REF] Béduwé | Mismatch of vocational graduates: What penalty on French labour market[END_REF] find that horizontal mismatch decreases the job satisfaction among French vocational graduates and the effect is stronger if graduates are also overeducated. Third, most of studies seem to ignore the selection bias problem owing to the non-employed graduates sample that could be mismatched if they chose to work [START_REF] Caroleo | Overeducation at a glance: Determinants and wage effects of the educational mismatch, looking at the AlmaLaurea data[END_REF]. Fourth, less researches focus on developing countries where mismatches seem to be higher [START_REF] Mcguinness | How useful is the concept of skills mismatch?[END_REF]. For instance, we only find [START_REF] Zakariya | The effects of overeducation on multiple job satisfaction towards enhancing individuals' well-being in Malaysia[END_REF] who examine the impacts of overeducation on job satisfaction among graduates in Malaysia and report negative impacts.

The objective of this article is accordingly to investigate if the vertical and horizontal mismatches decrease the job satisfaction among Cambodian university graduates by also considering the possible selection bias problem.

Thus, this research contributes to the literature on three main points: First, we examine the effects of education-job mismatches from their both forms (vertical and horizontal) and also the combination of these two forms (a double mismatch) in a developing country, namely Cambodia, that has just moved from a low income to a lower middle income status in 2016, while other previous researches focus mostly on overeducation and more advanced economies. Cambodia represents indeed an interesting case to consider given its remarkable rise in the enrollment rate in higher education but with an increasing concerns on unemployment and education-job mismatches. For instance, the enrollment rate in higher education has increased from just 2.5% in 2000 to 15.9% in 2011 (World Bank's website1 ) with the number of students rises from 20,000 in 2001 to 250,000 in 2014 [START_REF] Un | Cambodian Higher Education Vision 2030[END_REF]. Nevertheless, the unemployment rate of graduates is much higher, 7.7% in 2012, than people with only secondary education, 2.7% (NIS, 2012). Furthermore, around 50% of students are enrolled in economics, management and law majors, while Cambodia is lacking of graduates in engineering [START_REF] Madhur | Cambodia's skill gap: An anatomy of issues and policy options[END_REF].

Second, besides the typical variables controlled in the previous literature, we will also control the match between the graduates' evaluation on the importance and the real implications of several job attributes in their current occupations such as job autonomy, job stability, job salary, job challenge, career opportunities, chances to learn new things, reconciliation between working and family time, social status, job leisure and doing useful things for society. Being able to control these variables that may have strong impacts on the overall job satisfaction should make our results more robust. Third, it is possible that we confront the selection bias problem. Indeed, according to the job competition [START_REF] Thurow | Generating inequality[END_REF] and the assignment models [START_REF] Sattinger | Assignment models of the distribution of earnings[END_REF] that suppose the high-skilled job opportunities are limited and there exists a skill heterogeneity among graduates, sample selection bias may arise because of the fact that the educational mismatch appears first of all in the form of a higher probability of non-employment among some graduates and only at a later stage it takes the form of a penalty on individual outcomes in the labor market [START_REF] Caroleo | Overeducation at a glance: Determinants and wage effects of the educational mismatch, looking at the AlmaLaurea data[END_REF]. Consequently, we will use the Heckman probit model to deal with this issue, applied on a survey data financed by the French-speaking University Agency, known as AUF, and conducted by the French cooperation at the Royal University of Law and Economics among nineteen higher education institutions in Cambodia in 2011.

Data

The French cooperation at the Royal University of Law and Economics in Cambodia conducted the survey that informs this research by phone in 2011, among Cambodian graduates who had received their bachelor's degrees in 2008. The 4,025 observations are randomly selected and representative of nineteen higher education institutions in Phnom Penh, capital of Cambodia. The current study excludes self-employed people from the initial data set, because there are no detailed information available to evaluate the required schooling for their jobs, and thus impossible to define their education-job match status. Observations that offered no information on occupation and other key variables were also dropped. The final sample thus contains 3,211 university graduates who are representative of the study population, and in which 92 graduates are unemployed at the moment of survey.2

To measure the incidence of mismatches, we employ the job analysis (JA) that is known as an objective measure. Based on the JA measure, each occupation classified by the International Standard Classification of Occupations Code (1-digit) is assigned to the required level of education mentioned in the International Standard Classification of Education (ISCED). For example, graduates working in the positions classified as managers, professionals, and technicians/associate professionals, are considered as matched workers because these positions require tertiary education. Other occupational levels such as clerical support workers and elementary occupations do not require higher education. Consequently, graduates in these occupations will be considered as overeducated.3

The data also include information about the specialty of each bachelor's degree acquired from the different universities, which supports an objective determination of the presence of a horizontal mismatch. By reviewing the study program and job prospect of each specialty offered by each university, the author compares these descriptions with each individual occupation to discern if each graduate's job corresponds with his or her field of study. 4Our results indicate that 35.43% and 33.25% of graduates are overeducated and horizontally mismatched, respectively. The incidence of educational mismatches for each category of mismatch is provided in the figure 1 below. Next, the data also possesses information regarding the overall job satisfaction ranking from 1 (very dissatisfied) to 5 (very satisfied). We recode this variable with a dummy variable equaling 1 (satisfied) if workers indicate the value of 4 or 5, and 0 (not satisfied) if the value is from 1 to 3. It is indicated that 63.64% of graduates are overall satisfied with their jobs.

To estimate the impacts of education-job mismatches on the individual job satisfaction, several variables related to individual attributes, fields of study and firm characteristics are needed to be controlled because those variables may affect the individual job satisfaction.

More importantly, the survey questionnaire also provides information regarding the graduates' evaluations on the importance of several job attributes (evaluated from 1, not important at all, to 5, very important) such as job autonomy, job stability, opportunities to learn new things, job challenge, career perspective, high salary, good social status, possibility to do something useful for the society, possibility to reconcile working and family time, and job leisure.

We consider this evaluation as the graduates' preferences on those job attributes. Next, the employed graduates also evaluate the implications of those job attributes in their current jobs (evaluated from 1, not implied, to 5, very implied in the job). These two evaluations allow us estimating if the importance of those job attributes evaluated by graduates are realized in their current jobs or not. Indeed, if the implications of those job attributes in their current jobs equal or exceed their preferences, we consider that their expectations for each job attribute are met. Controlling these variables is necessary because they may strongly reduce the overall job satisfaction if some of these job attributes are not fulfilled as what graduates wish for. Based on the descriptive statistics, we observe that graduates working in a matched job tend to be much more satisfied than other graduates. Nevertheless, we observe that other variables may affect the job satisfaction as well. Thus, an econometric estimation is needed to estimate the real impacts of education-job mismatches on job satisfaction.

Descriptive statistics

Method

The descriptive statistics have shown that many variables, besides mismatches, may affect the job satisfaction. Furthermore, there also exist graduates who are unemployed at the moment of survey, and consequently, we cannot observe their job satisfaction. Thus, to identify the impacts of mismatches on job satisfaction with a possible sample selection bias, we propose the Heckman probit model (Van de Ven & Van Praag, 1981) with the following estimation:

y * j = x j α + mismatch j β + u 1j latent equation (1)
Such that y * j defines the job satisfaction, x j is a vector of independent variables (including individual attributes, fields of study, firms characteristics and implications of other job attributes relative to graduates' expectations), mismatch j reflects the observed categorical variable of educational mismatches, u 1j defines the error term, and α and β are unknown parameters, such that β represents the estimated effect of educational mismatches on job satisfaction, ceteris paribus.

Nevertheless, we observe only the binary outcome in case:

y probit j = (y * j > 0) probit equation (2)
Thus, the dependent variable is not always observed. Rather, the dependent variable for observation j is only observed if:

y select j = (z * j γ + mismatch * j β + u 2j > 0) selection equation (3)
where u 1 → N (0, 1)

u 2 → N (0, 1) corr(u 1 , u 2 ) = ρ
When ρ = 0, standard probit techniques applied to the first equation yield biased results, while the Heckman probit model provides consistent, asymptotically efficient estimates for all the parameters in such models. However, for the model to be well identified, the selection equation should have at least one variable that is not in the probit equation. Otherwise, the model is identified only by functional form, and the coefficients have no structural interpretation.

Thus, besides individual attributes and fields of study, in the selection equation, we add other variables such as type of university, scholarship status, double training, internship during study, and previous monthly job experiences that may affect the probability of being employed.

Results

Table 2 presents the regression results. Models 1, 2, 3 and 4 examine the impacts on job satisfaction from working in a matched, vertical mismatched, horizontal mismatched and double mismatched job, respectively. Only significant variables are reported in the Table 2, while all control variables can be found in the Appendix: C and Appendix: D. Looking at the Wald test result in Table 2, we see that the test value is highly significant, which rejects the null hypothesis of ρ = 0. Consequently, using the standard probit model can yield bias results owing to the sample selection bias, and the Heckman probit model is consistent and more efficient in this case.

Indeed, being employed is not a random process. In the selection equation estimation, we observe that graduates who used to intern during their studies and have longer previous job experiences are more likely to be employed. In the context of low quality in education and limited skilled-job opportunities, typically found in developing countries, employers may stress more importance on the professional background of graduates, rather than just rely on their diplomas. This suggests that university students should look for volunteer jobs, and internship should be compulsory, so that graduates can acquire professional knowledge, useful for finding a job when they finish their studies. We also notice that graduates from the public universities are less likely unemployed. In fact, many private higher education institutions (HEI) in Cambodia gained official recognition without following a clearly defined process [START_REF] Ford | Cambodian accreditation: An uncertain beginning[END_REF]. By international comparison, many of those HEI are very small with narrow academic and resource bases [START_REF] Mak | A current update of higher education in Southeast Asian countries: The case of Cambodia[END_REF]. Additionally, the competition among those private institutions led to the lowering of fees, followed by the reduced revenue, and coupled with the inattention to support quality, results in weak HEI and expressed in high graduates' unemployment rate [START_REF] Ford | Cambodian accreditation: An uncertain beginning[END_REF].

Thus, a choice of university seems to be crucial in Cambodia.

Next, from the main equation, we observe that several individual attributes affect the job satisfaction such as men and persons that need to be in charge of their family tend to be less satisfied. According to a research, women are happier at work because they tend to choose more fulfilling work, more enthusiastic, lower aspiration and expectations,6 while the responsibility on their family may put more pressure on workers to expect more from their jobs, making them less satisfied. Older workers are found to be happier as found by [START_REF] Lee | Age, education, job tenure, salary, job characteristics, and job satisfaction: A multivariate analysis[END_REF]. Perhaps, thanks to their maturity, older workers adapt to the lifework better than young persons. Fields of study also impact job satisfaction such as graduates in management and related-fields are less satisfied, while graduates in engineering are happier. As there are too many graduates in management related fields, this may put them under some pressures of keeping their jobs or positions. Firms characteristics and sector of activity also affect job satisfaction. Indeed, workers with fixed or permanent work contracts are more satisfied than worker without work contract. Given that 73% of employment in Cambodia were accounted for by micro, small, and medium-sized enterprises (MSMEs), and most of them (about 95%) are in the informal sector (ADB and ILO, 2015), we have reasons to believe that workers without contract might be in the informal sector that is characterized by poor working conditions. Working in the public sector increases job satisfaction because it is known that in Cambodia, the job pressures in public sector is generally much lower than in private sector. Surprisingly, graduates working in a small firm less than ten staff are more satisfied. Perhaps, in bigger firms, graduates face more pressures and the tasks might be more specialized, while in small firms, graduates might be able to learn various skills from management to operation, which may positively affect their job satisfaction.

Regarding the match between the implications of several job attributes relatively to what expected by graduates, we clearly see that the jobs that provide chances for graduates to learn new things/skills, and especially good salary, do increase their job satisfaction. Nevertheless, it is surprising that graduates who report to have a great time for leisure and family tend to be less satisfied. Perhaps, those jobs might provide too much free time beyond what graduates wish for, making graduates feel that they are unproductive, and thus not satisfied.

Finally, after controlling many variables and sample selection bias, we still observe the negative impacts of education-job mismatches on job satisfaction. Indeed, graduates who work in a matched position are more satisfied, while working in a mismatched job, either vertical or horizontal, can lower job satisfaction, especially the case of a double mismatch. From the Table 3, graduates with an education-job match are 4.66% more satisfied than other graduates who face at least one type of educational mismatch. Similarly, vertical and horizontal mismatches reduce the job satisfaction with similar impacts, by 4.74% and 4.69%, compared to matched graduates. Thus, the literature that neglects the horizontal mismatch case ignores an important source of problem. Furthermore, when graduates suffer both forms of mismatches, the negative impacts also increase up to 5.34%, which is conformed to what found by [START_REF] Béduwé | Mismatch of vocational graduates: What penalty on French labour market[END_REF] that a double mismatch has a stronger effect on job satisfaction among vocational graduates in France. This signifies that the education-job mismatches is also a main issue that we need to carefully consider in Cambodia.

Conclusion

This article examines the impacts of education-job mismatches from their both forms and dimension (vertical, horizontal and double mismatches) on the job satisfaction among university graduates in Cambodia. To deal with the sample selection bias, we employed the Heckman probit model, and the regression results show that education-job mismatches adversely affect the job satisfaction with the strongest impact from a double mismatch, followed by vertical and horizontal mismatches, despite several variables related to individual attributes, fields of study, firm characteristics and implications of job attributes are controlled for.

This article contributes to the literature in several ways. Indeed, we examine the case of a developing country that has just upgraded to a lower-middle income status and consider the both forms of mismatches together with the possible sample selection bias. Too little studies have analyzed the impacts of mismatches in developing countries, and only a little of researches in developed countries have considered the horizontal form and the combination effect of their both forms as well as the sample selection bias, owing to non-employed graduates, when they examined the impacts of mismatches on individual outcomes in the labor market.

Our results suggest that Cambodia has to be more attentive on the expansion of its higher education sector. Indeed, the negative impacts of educational mismatches on the graduates' job satisfaction, may generate counter-productive behaviors such as high rates of absenteeism and turnover that were actually already observed and reported by many employers in Cambodia.

These behaviors can surely cost firms in terms of a limited productivity and subsequently the firm expansion, which can be bad for the economic growth and development.

Thus, Cambodia should improve the quality in education and create programs allowing students doing more internship to apply their theoretical knowledge in the real practices. Evaluation on each university performance should be conducted and results should be disseminated to public, so that students can make a better choice, and universities will compete more in terms of quality. Job opportunities and economic rewards related to each study program should be widely communicated so that students can rely on those information to decide which program to pursue at higher education.

Appendix: A Notes: One limit of using this measure to estimate the rate of overeducation is that the same job title may not mean that workers are performing the same tasks, and thus workers can be required to possess different educational levels. Nevertheless, other measures of overeducation also possess other drawbacks (please see the literature review of [START_REF] Mcguinness | Overeducation in the labour market[END_REF] and [START_REF] Sala | Approaches to skills mismatch in the labour market: A literature review[END_REF] for a further discussion on this matter). Additionally, the use of this measure is also constrained by the data availability. For instance, previous researches on this issue in developing countries, including Cambodia, conducted by the International Labour Organization and Asian Development Bank also employ this same method by assigning the ISCO with 1 digit level to the ISCED (e.g., [START_REF] Sparreboom | Is education the solution to decent work for youth in developing economies[END_REF]ILO and ADB, 2015).

Figure 1 :

 1 Figure 1: Number and percent of graduates by category of education-job mismatch

Table 1 :

 1 Table 1 contains the descriptive statistics for all variables included in the analysis in relation to overall job satisfaction. 5 Descriptive statistics

	VARIABLES	Mean	Std. dev. Mean satisfaction
	Dependent variable
	Job satisfaction	0.64	0.48
	Independent variables
	No mismatch	0.50	0.50	0.67
	Overeducation	0.35	0.48	0.60
	Horizontal mismatch	0.33	0.47	0.61
	Double mismatch	0.18	0.38	0.60
	Male	0.64	0.48	0.62
	Age at the end of study	21.84	3.98	0.62
	Married	0.25	0.43	0.67
	Being born in Phnom Penh	0.51	0.50	0.63
	In charge of family	0.45	0.50	0.58
	Law-Eco-Management	0.49	0.50	0.58
	Social Science English	0.15	0.36	0.63
	Engineering	0.05	0.21	0.73
	Public sector	0.23	0.42	0.64
	Fixed-terms contract	0.30	0.46	0.66
	Permanent contract	0.31	0.46	0.67
	Work in a small firm	0.10	0.30	0.70
	Job autonomy	0.58	0.49	0.59
	Job stability	0.62	0.48	0.59
	Chance to learn new things	0.56	0.50	0.61
	Job challenge	0.52	0.50	0.59
	Career perspective	0.53	0.50	0.58
	High salary	0.31	0.46	0.63
	Good social status	0.56	0.50	0.57
	Doing useful things for society	0.64	0.48	0.59
	Having time for family	0.63	0.48	0.55
	Job leisure	0.60	0.49	0.55
	Observations		3211

Table 2 :

 2 Impact of education-job mismatches on job satisfaction

	VARIABLES	Model 1	Model 2	Model 3	Model 4
	No Mismatch	0.123**
		(0.048)
	Overeducation		-0.120**
			(0.052)
	Horiz. Mismatch			-0.107**
				(0.050)
	Double Mismatch				-0.131**
					(0.062)
	Male	-0.107**	-0.110**	-0.097*	-0.101**
		(0.051)	(0.051)	(0.050)	(0.050)
	Age at the end of study	0.031***	0.031***	0.032***	0.031***
		(0.007)	(0.007)	(0.007)	(0.007)
	In charge of family	-0.183***	-0.186***	-0.187***	-0.190***
		(0.050)	(0.050)	(0.050)	(0.050)
	Law-Eco-Management	-0.191***	-0.177***	-0.212***	-0.201***
		(0.058)	(0.058)	(0.058)	(0.058)
	Engineering	0.229*	0.239*	0.237*	0.241*
		(0.132)	(0.132)	(0.132)	(0.132)
	Public sector	0.116*	0.122*	0.124*	0.126*
		(0.067)	(0.067)	(0.067)	(0.066)
	Fixed-terms contract	0.272***	0.268***	0.273***	0.268***
		(0.061)	(0.061)	(0.061)	(0.061)
	Permanent contract	0.249***	0.249***	0.248***	0.247***
		(0.060)	(0.060)	(0.060)	(0.060)
	Work in a small firm	0.322***	0.329***	0.312***	0.322***
		(0.086)	(0.086)	(0.085)	(0.086)
	Chance to learn new things	0.110**	0.112**	0.108**	0.110**
		(0.053)	(0.053)	(0.053)	(0.053)
	High salary	0.312***	0.312***	0.312***	0.312***
		(0.056)	(0.056)	(0.056)	(0.056)

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Robust standard errors are in brackets.

Table 2 :

 2 Impact of education-job mismatches on job satisfaction-continued

	VARIABLES	Model 1	Model 2	Model 2	Model 4
	Having time for family	-0.183**	-0.183**	-0.189**	-0.190**
		(0.075)	(0.075)	(0.075)	(0.075)
	Job leisure	-0.266***	-0.269***	-0.264***	-0.266***
		(0.074)	(0.074)	(0.074)	(0.074)
	Selection equation: Being employed
	Previous monthly job experiences	0.081***	0.081***	0.081***	0.081***
		(0.005)	(0.005)	(0.005)	(0.005)
	Internship	0.338***	0.337***	0.337***	0.336***
		(0.125)	(0.125)	(0.125)	(0.125)
	Public university	0.430***	0.429***	0.432***	0.432***
		(0.154)	(0.154)	(0.154)	(0.153)
	Observation	3,211	3,211	3,211	3,211
	Censored	92	92	92	92
	Log pseudolikelihood	-2155.58	-2156.17	-2156.49	-2156.55
	Wald test of indep. eqns. (chi2 value)	8.10***	8.09***	8.23****	8.19***
	(H0 : ρ = 0)

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Robust standard errors are in brackets.

Table 3

 3

	below

Table 3 :

 3 Impacts of education-job mismatches on job satisfaction (Marginal effects)

	Variables	Impacts	Compared to
	No mismatch	+4.66%**	Mismatched workers
	Overeducation	-4.53%**	Non-overeducated workers
	Horizontal mismatch	-4.07%**	Non-horizontal mismatched workers
	Double mismatch	-4.98%**	Non-double mismatched workers
	Overeducation	-4.74%**	Workers with an education-job match
	Horizontal mismatch	-4.69%**	Workers with an education-job match
	Double mismatch	-5.34%**	Workers with an education-job match
		*** p<0.01, ** p<0.05, * p<0.1.

Table 4 :

 4 Correspondence between occupational class and educational level

	ISCO-08 occupational class	ILO skill level	ISCED-97 educational level
	1. Manager	3 + 4	6, 5a and 5b
	2. Professionals	4	6 and 5a
	3. Technicians	3	5b
	4. Clerks	2	4, 3 and 2
	5. Service and sales	2	4, 3 and 2
	6. Skilled agricultural	2	4, 3 and 2
	7. Craft and related	2	4, 3 and 2
	8. Plant and machine operators	2	4, 3 and 2
	9. Elementary occupations	1	1
	Source: ISCO-08, volume I

Table 5 :

 5 Description of educational level required for each skill level

	Skill level	Educational level	Description of educational level
	4	6	Second stage of tertiary education (ad-
			vanced research qualification)
		5a	First stage of tertiary education, 1st degree
			(medium duration)
	3	5b	First stage of tertiary education (short or
			medium duration)
	2	4	Post-secondary, non-tertiary education
		3	Upper secondary level of education
		2	Lower secondary level of education
	1	1	Primary level of education
	Source: ISCO-08, volume I

Data link: https://data.worldbank.org/indicator/SE.TER.ENRR?locations=KH

By comparing the means and standard deviations of all variables used in our analysis before and after the eliminations of those observations, we do not remark any important gaps to consider.

Tables that specify the process of matching the occupational class to the educational level required, are in the Appendix: A.

The matching table can be found in the Appendix: B.

For the continuous variables (age at the end of study), the observed mean satisfaction is evaluated for the two last quartiles. Then, the job satisfaction, firm characteristics and the implications of job attributes (in the second row of table) are only observed among the employed graduates (3,119 observations).

https://www.nafe.com/8-surprising-reasons-women-are-actually-happier-at-work-than-men

Appendix: B

 134,143,231,232,241,242,243,262,263,264,331,332,333,334,411,412,413,421,431,432,522 Engineering and Architecture 132,214,215,216,231,232,233,311,312,313,315,515Social sciences in English language 111,112,121,122,133,134,141,143,216,231,232,233,241,242,261,262,263,264,265,334,341,343,351,352,411,412,413,511,521,522,524 Sociology,Humanities and Arts 112,216,231,232,233,234,262,263,264,265,341,511 Sciences 211,212,231,232,233,311,331,421,431 Information and Computer Technologies 112,121,133,134,231,232,233,251,252,351,352,524 Tourism and Hospitality 112,122,134,141,231,232,243,264,341,343,441,511 Law and Public Affairs 111,121,231,232,242,261,262,263,264,334,335,341 Table source: Author's estimation by reviewing the job prospects described for each specialty in each university, then comparing with individual occupation.

Appendix: C


Sandip Tiwari

Damien Querlioz

On the Physical Underpinnings of the Unusual Effectiveness of Probabilistic and Neural Computation

Abstract-Probabilistic and neural approaches, through their incorporation of nonlinearities and compression of states, enable a broader sampling of the phase space. For a broad set of complex questions that are encountered in conventional computation, this approach is very effective. In these patterns-oriented tasks a fluctuation in the size of data is akin to a thermal fluctuation. A thermodynamic view naturally applies to this computational style to information processing and from this reasoning one may estimate a variety of interesting consequences for computing: (a) efficiencies in energy, (b) complexity of tasks that can be tackled, (c) inaccuracies in inferences, and (d) limitations arising in the incompleteness of inputs and models. We employ toy model examples to reflect on these important themes to establish the following:

• A dissipation minimum can be predicted predicated on the averaged information being discarded under constraints of minimization of energy and maximization of information preservation and entropy. Analogous to the kBT ln 2 for the randomization of a bit, under biological constraints, the ∼ -70 mV base and ∼ 40 mV peak spike potential are then a natural consequence in a biological neural environment. Non-biological, that is, physical implementations can be analyzed by a similar approach for noisy and variabilityprone thermodynamic setting. • In drawing inference, the resorting to Occam's razor as a statistical equivalent to the choice of simplest and least number of axioms in developing of a theory conflicts with Mencken's rule-for every complex problem, there is an answer that is clear, simple and wrong-as a reflection of dimensionality reduction. • Between these two factors, it is possible to make a measure of the error bound predicated on the averaged information being discarded and being filled in, and • This lets one predict the upper limits of information processing rate under constraints.

These observations point to what may be achievable using neural and probabilistic computation through their physical implementation as reflected in the thermodynamics of the implementation of a statistical information mechanic engine that avoids computation via deterministic linear algebra.

I. INTRODUCTION

Probability estimations through the various styles-neural of the different varieties, Bayesian, et cetera-are estimations on limited samples using nonlinearities and compression.

Finding maximum likelihoods, or minimizing energy consumption, and other objectives of a computation are subject to the problems of "fitting" that is natural to statistical model building. Entropy, energy, likelihood are a common theme here and therefore the thermodynamics of information and of physical implementations relevant. Employing the tools of neural and probabilistic computation and of statistical mechanics approaches, we use example toy models to point out a number of interesting conclusions and directions worthy of deeper exploration.

A. Spiking action potentials

The action potential in spiking in the living biological world is viewable through the constraints of thermodynamics. The action potential moves down an axon whose simplest model is that of a capacitive membrane across which an electromotive potential exists due to the ion concentration differences and where conductance channels open and close. This signaling is dissipative. But, absence of signaling too is dissipative. So, the potentials, currents and times need to be consistent within the energetic constraints. The spiking and the noise together make this signaling mechanism effective. For this problem, the equilibrium potential is calculable from the ionic concentration across the membranes, with K + dominating, but N a + , Ca + and Cl -also present. K + concentration is higher outside the tubule, while in case of N + it is higher inside. The biologically sustainable concentrations are in the order of few mM to few 100s mM . For example, for K + , N a + and Cl -outside/inside these are 5/140, 140/12 and 20.

The diffusive and electrical flow balancing establishes the reversal potential, which is the voltage across the specific ion channel during its operation. This reversal potential, for K + flow in its channel, is

V rev = RT zF ln [K + ] out [K + ] in = 8
.314 × 310 96845 ln 5 140 = -88.7 mV.

(1) Here, R is the gas constant (8.314 J/K.mole), T is the body temperature 310 K), z is the ionicity (1 for K + ), F is Faraday constant (96485 J/V.mole) and concentrations of ions is a ratio in identical units. Cl -, which is not actively pumped, settles at a reversal potential close to the resting potential determined by other ions. Chlorine is also highly impermeable. This resting potential, absent any activity, is a balance of concentrations and the permeabilities of their channels. Again following the Nernst equation approach,

V rest = RT F ln i π i [A + ] out + j π j [B -] in i π i [A + ] in + j π j [B -] out , (2)
where πs are permeabilities. The size of the ion (N a + < K + (0.138 nm) < Ca + ) [START_REF] Hille | Potassium channels in myelinated nerve. Selective permeability to small cations[END_REF], for example, and the size of that ion's pore matter for resting potential. π N a /π K < 0.01.

The resting potential is maintained by active ion pumping to compensate for leakages. The pumps-marvels of nearideal electrochemomechanical coupling-and the permeability lead to smaller resting potential, which for these parameters, including leakage of K + , N a + and Cl -are 1 × 10 -6 cm/s, 2 × 10 -8 cm/s and 5 × 10 -10 cm/s [START_REF] Ronald | Theoretical mechanics of biological neural networks[END_REF].This resting potential is -78 meV , and is in range that is measured across species and cell types.

Noise is essential in a driven nonlinear dynamic system [START_REF] Bulsara | Tuning in to noise[END_REF].The noise is Poissonian-1/f -like as in many electronic physical systems. Fisher information [START_REF] Frieden | For a discussion of Fisher information[END_REF]-as a measure of the ability to estimate a parameter as well as of state of disorder-gives us a tool to extracting the consequence that the high voltage of the the action is ∼ 40 mV which is slightly above the thermal voltage to maintain a judicious signal-tonoise ratio where synchronization becomes achievable.

The +40 mV peak-of the order of k B T -in action potential is significant in its role in how noise, spiking and information processing interact. And this spike contains ≈ 0.5 × 110 mV × 10 -3 s × 3 pA ≈ 165 aJ ≈ 40000k B T of energy. The membrane signaling process is a capacitancebased, signaling is through conductance of channels that are manifested in the voltage spike.

So, noise and thermodynamics give a powerful example in the low energy processing of information in the living system.

II. MUTUAL INFORMATION, CONVOLUTION, INFORMATION AGGREGATION AND SEPARATION

Information is relative. It a measure of the difference between two levels of uncertainty. Reduction in this uncertainty is gaining of information. Shannon entropy is one of our tools to quantifying uncertainty over several states of variables (say X). The observation of other variables (say Y ), informing us of the state X is the mutual information. It is something in common. The mutuality of this information is also embedded in the convolutions-pairwise and higher-and this underlies the success of convolution networks as well as in the use of partition functions. Observations on Y , either increase or leave the information on X unchanged. This lets us write the rules of aggregation of information.

With H(X) =x p(x) log 2 p(x) as the Sannon entropy, the mutual information between the two variables is

I(X; Y i ) = H(X) -H(X|Y i ). (3)
Information is a differential so we may write this as I(X; Y i ) = -∆H(X)/∆Y i . As measurements are accumulated over Y , it is now possible to account for the change of entropy-a discrete calculus of uncertainty. One can now see that this leads to

H(X) ≤ H(X|Y k-1 ) ≤ H(X|Y k ), and (4)
I(X|Y k ) ≥ I(X; Y k-1 ) ≥ • • • ≥ I(X|Y 1 ) (5)
Use of chain rule now lets us aggregate this information in the form

I(X|Y k ) = H(X) -H(X|Y k ) = - k i=1 ∆H(X) ∆Y i - k i>j=1 ∆ 2 H(X) ∆Y i ∆Y j -• • • , (6)
where the second term on the right is just I(X; Y j )|Y i ) -I(X; Y j ). Information aggregation produces both a synergy and a redundancy of aggregation and these relationships and their extensions let us formalize information gain and translate them to probability distributions. For example, information gain about X from a pair (Y 1 and Y 2 ) is the sum of independent mutual information with X and an additional term.

The first here is independent mutual information with X and the second is the correlations between the Y 1 and Y 2 variables. One can see in this aggregation picture the ability to extract features as a synergy. The simplest examples are logical gates such as N AN D, N OR, XOR. These can be viewed both as a neural network as well as feature extractor. They tell us about the synergy that exists at the input that the gate aggregates and where the mutual information and convolutions matter. The information theory view here has related to us the entropic notions about how this information aggregation has taken place and its informational features. The problem of statistical inference gains from the insights of this discussion.

III. THERMODYNAMIC IMPLICATIONS FOR INFERENCE

For the difficult reverse problem of inference, machine learning approaches employ a number of thoughts that we have already mentioned. Fisher's measure-the maximum likelihood estimate-is one. Maximum entropy as a guide to prescribe initial probabilities is another one since it leads to inferences being dependent only on the data and the conditions for which there is no prior information. Introduction of noiserandomness-is a tool to faster computation and is also useful where priors cause problem.

Thermodynamics teaches us a number of important principles. One is the principle of maximum likelihood where the best estimators should most duplicate data. The second is the principle of maximum entropy which prescribes no bias to internal states of the variables. The entropies from these two views need balancing. The third important principle from thermodynamics is the need to balance minimum energy and maximum entropy at any temperature. This view can be integrated in the algorithms of machine learning [START_REF] Isozaki | A thermodynamical approach to probability estimation[END_REF].

For information, postulate the Kullback-Leibler divergence between distribution of a target and an empirical distribution as the internal energy. By regarding fluctuation in data size as a thermal fluctuation, temperature can be postulated through the distortion between estimator and the probability function estimated where one data has been removed. The free energy (a Helmholtz energy, F = U -H/β, where F is the free energy, U is the internal energy and β represents a data temperature) then follows from the internal information energy, the Shannon entropy and the temperature. And now one can apply the constraint that the probability functions are to arise from minimization of the free energy. In this formulation, the maximum likelihood is approached when the number of data points is high. This is the condition in the energy equation where β → 1.

If the number of data points is low-the condition when many of machine learning algorithms have large error-the minimum free energy estimate can be more accurate.

Only when the number data points is high does β → 1, and maximum likelihood is approached. However, this minimum free energy estimate continues to be applicable to the small data limit. Figure 1 shows the divergences for the different approaches and the efficacy of this minimum free energy approach is quite noticeable. For N < 15 the three approaches give widely different estimates the minimum free energy approach being the most accurate.

This figure also shows through the divergence at the low N count, the issue of errors. Techniques such as resorting to Occam's razor in drawing inference can produce serious errors. In the physical world and this machine world, one also encounters Mencken's rule, which states that for every complex problem, there is an answer that is clear, simple and wrong. Simplest explanations do not always comport.

This broad outline describes a mapping of the statistical physical techniques to information data, and similar properties as those derived in statistical physics, such as energy fluctuations, variances, et cetera, may be found. The approach shows that ranging over small to large sample sizes, the approach gives more accurate estimations compared to maximum likelihood or maximum entropy approaches alone. It reduces error bounds.

IV. NEURAL NETWORKS AS PHYSICAL MODELS

Forms of neural networks have now shown a variety of properties that correspond to those of physical systems. A stochastic neural network is a Boltzmann machine, that is, a thermodynamic model [START_REF] Torlai | Learning thermodynamics with Boltzmann machines[END_REF]. The example in section III was a specific example of use of thermodynamic principles as an approach to understanding the statistical consequences. Variational renormalization groups have been mapped on the networks [START_REF] Mehta | An exact mapping between the variational normalization group and deep learning[END_REF] and have been tested with two-dimensional Ising models. Features such as entanglement entropy are apparently inherent outcomes of the coarse grained organization in the networks. It has also been argued [START_REF] Lin | Why does deep and cheap learning work so well?[END_REF] that the deeper networks are an ersatz learning with exponentially fewer parameters because of how the general physical functions arising in physical laws can be translated onto a network.

Physics favors simple probability distributions and this is what the networks also prefer. In the correspondence between the physical and the network, the correspondence between a quadratic Hamiltonian to a Gaussian probability distribution, locality to sparsity, translational symmetry to convolution, and free energy difference to the Kullback-Leibler divergence (we employed this in section III), can be easily seen.

An illustration of the success of such a correspondence is the observation on phase transitions where Ising models are useful toy models. The neural network-simple or multi-layeredare a corresponding convenient tool to observe the noise, and the breaking of symmetries in regions of transition as the network evolves from a random state to a phase transitioned state with a change in temperature.

Figure 2(a) and 2(b) show results-of order and of correlation/randomization-of two-dimensional Ising simulations through a restricted Boltzmann machine by sweeping temperature. One can see, as the temperature parameter T is lowered, the appearance of magnetization in (a) and the frequency of the weight amplitudes in (b) in a restricted Boltzmann machine. When T is high, the spins are random, with either polarity of spins evenly distributed, and the weight histogram is strongly peaked. The hidden layer is "decoupled." When T is low, spin is polarized and the frequency is flat.

V. INFORMATION, ERRORS, AND ENERGY

The challenge of rebooting computing and the applicability of the probabilistic and neural techniques to rebooting computing is multifold.

Machine learning techniques employing neural techniques in all their forms, probabilistic versions, together with Bayesian approaches without overfitting constraints are all still affected by two considerations. The first is that, while any new observation that fits with the model already learned is a major success, any observation that does not fit with the model leads to an error. So correlations or information synergy not observed before, or fewer data, both lead to more significant error. The second consideration is that in any network where the calculation of probabilities, weights, et cetera, are performed through deterministic Boolean algebra operations, the calculation is traditionally performed to many significant bits and is therefore very energy consuming.

Section IV noted a direct correspondence between the physical and the network. Since any neural and probabilistic computation is going to be subject to the inherent errors arising in the model that has been learned, it is not unreasonable to speculate that employing the physical for creation of the machine learning primitives would be of use. Among these, low energy probability generations, convolutions, Gaussian probability distributions, and stochastic noise are all quite foreseeable. Multiple possibilities exist for each one of these.

The details of how much energy is consumed for a given task certainly depends on the scale and the nature of task at hand. Boltzmann entropy change is high at the point of phase transition in the physical world. The same will also hold true for the data world's machine learning. If η is a scaling factor for k B T that is reflective of the technology's per bit manipulation energy, and N bits are being manipulated, the energy consumed is ηk B T N log 2 N . Section I had one example of this energy need in the presence of noise in a biological spiking arrangement. Physical implementations will be different. But, this energy is quite small even if η were to be 10 5 . The challenge is to find the model physical embodiments for these primitives that will perform the function desired in the machine learning system.

VI. CONCLUSION

In this work, we have explored a few themes to build an argument related to rebooting of computing. Section I showed how thermodynamic principles suffice in predicting action potentials while utilizing noise in a driven nonlinear dynamic system. Neural networks and probabilistic systems compress states, employ nonlinearities, and work with noise of data size with many equivalences to the physical thermodynamic systems. This thermodynamic basis was employed to explore the question of information aggregation and separation in machine learning. As an example, we showed the increased accuracy of of minimum free energy estimation approach. We extended this thermodynamic discussion to show the equivalences at work in neural network environment for a model physical system. This bringing together of the physical and the machine approaches have let us make remarks on errors and energies that are potentially the limits for the set of problems for which the machine learning approaches are most suitable. We remain far away from these limits.

Fig. 1 .

 1 Fig. 1. The Kullback-Leibler divergences between estimated probability functions and true probability functions based on maximum likelihood, maximum entropy and minimum free energy estimate. The problem assumes an entropy of 0.5 with 3 internal states.

Fig. 2 .

 2 Fig. 2. (a) shows "magnetization" of the Ising model and (b) shows the weight distributions as simulations proceed through the point of criticality.

ACKNOWLEDGMENT Sandip Tiwari acknowledges the hospitality of Université Paris-Sud during summers. Damien Querlioz acknowledges the support of European Research Council, BAMBI EU collaborative FET project, ANR, and the French Ministère de l'écologie, du développement durable et de l'énergie.


Muhammad Soban Badar

Dr Muhammad Irfan

Shopping Mall Services and Customer Purchase Intention along with Demographics

Keywords: Shopping Mall Services, Motivations, Purchase Intention, Customer Patronage

Malls are continuously adopting the positive change and promoting the creativity in the people lifestyle. There are various determinants which help the customer on the selection of shopping mall. Important factors were studied to see the customer preference for the shopping mall with the help of the structured questionnaire. We collected the data from 416 random visitors through a survey. Data were analyzed using simple linear regression. The study will also help the shopping mall management to understand the customer likeness for the services. Future research can be directed toward the shopping mall purchase intentions and customer loyalty.

Introduction

In the last two decades, the shopping centers are impacting the customer shopping decision dramatically. Customers are switching from retail stores to shopping malls rapidly. The reason behind the switching from the retail shop to shopping malls is environment and comfort provided in shopping malls. The choice of particular shopping malls by customer depends upon the position of that mall in customer minds. Nowadays, customers are utilizing shopping malls for several reasons as to have the social communication with other, to feel unwind by having some amusement, to purchase the things under one rooftop, to invest the cheerful energy and so forth. All these services create the positive effect on the customer mind and affect their shopping center choices. The customer is much aware and wants the more cheerful services in shopping malls as it usually takes more time while shopping in a mall as instead of retails shops. Therefore, by one means or another, shopping malls are enhancing their services in order to cater to the emerging needs of the customer.

Shopping malls are improving their facilities day by day in order to attract customers. Shopping choices of customers vary according to their distinctive demographic attributes. In the United Kingdom, shopping malls are named "shopping plazas" or "shopping regions". Most of the shopping malls are situated in the main city areas, which are surrounded by open-air shopping streets in the United Kingdom (Ifrah, 2011). Because of shopping centers, it acquires the modernization the way of life of the client, in which client feels looks current, look great, and feel the better life in Kaula Lumpur [START_REF] Nurani | Mall with a purpose[END_REF]. As the distinctive nations have the diverse shopping center culture. In Hong Kong, the term "shopping center" or plaza are mostly used instead of the shopping mall (Ifrah, 2011). Present day nations are in a battle of making uber shopping center with unique customer facilities. Nine Million square foot an uber shopping center has been build in Dubai, named "Dubai shopping center" is the biggest shopping center on the planet in 2007 [START_REF] Andersen | EMAAR announces 'Dubai Mall' the world's largest shopping centre[END_REF](Andersen, , 2005)). Each nation has a diverse culture and characteristics which impacts their shopping tendency. Many of the factors like difference in culture, lifestyle, income/economic wealth, and country weather have changed the UAE consumers shopping preference [START_REF] El-Adly | Segmenting food shoppers in the United Arab Emirates according to their attitudes towards food shopping[END_REF]. Loved places by customers are neither unequivocally beautiful nor do these places induce magnificent sensory experiences, and are not always perfectly functional. A few places and structures are quite extremely magnetics for the guests. Those places have several features which attract the public towards them but none of them physically pull anyone towards it.

Different individuals have different shopping needs at shopping however they may be looking for the comparable reason like enhancement, accommodation, amusement and so forth (Ahmad, 2007). Shopping centers are not meeting the shopping needs of the customers but also their recreational needs (Ng, 2003). The reasonable information about the client visiting the shopping centers is truly helpful for the shopping center administration for better evaluation of the need of the client. Understanding of customer preferences helps mall administration to fulfill its needs towards shopping centers (Ahmad 2007). If a customer feels dissatisfaction regarding services, shopping mall then it brings change in the customer choice of a shopping mall. Due to perceived dissatisfaction regarding feature options and services of shopping malls customers switch towards small departmental stores; despite the wide range of quality products provided at shopping malls [START_REF] Dennis | The mediating effects of perception and emotion: Digital signage in mall atmospherics[END_REF]Yu, 2007, Finn andLouviere, 1996;[START_REF] Visser | Excitement at the mall: determinants and effects on shopping response[END_REF]Sit, Merrilees, and Birch, 2003). The emerging topic for marketing research in retailer concern is the patronage and re-patronage (Machleit, [START_REF] Meyer | Evaluating the nature of hassles and uplifts in the retail shopping context Muhammad Ibrahim[END_REF]. The patronage and re patronage of the shopping malls for the customer is only possible on basis of services regarding malls. Different criteria should be considered in the decision-making process such as population & Economical Characteristics, environmental consideration, attractiveness, accessibility & transportation, etc. (eswa, 2013).

Literature Review

The development of shopping centers industry brings the enormous changes in the basic style of shopping centers, now shopping centers are changed over into retails which are serving the client as indicated by request of clients [START_REF] Berman | Retail concentration: a comparison of spatial convenience in shopping strips and shopping centres[END_REF]. It is not as simple as it looks, shopping malls are confronting with various types of difficulties like a way of life of shopper, store solidification comparative stock, commercial center immersion [START_REF] Field | Power surge: power centers re-charge with new tenants and a changing format[END_REF][START_REF] Reda | Breaking the monotony: research suggests it's time to reinvent the mall[END_REF]. One of the most heavily examined topics in the shopping center is patronage [START_REF] Yavas | A multi-attribute approach to understanding shopper segments[END_REF]. Presently the customers shopping style is changed towards one-stop shopping in which individuals feels more unwind. These factors create the positive effect on the overall performance of shopping mall, increases the economic wealth of the malls and patronage towards the malls [START_REF] El-Adly | Segmenting food shoppers in the United Arab Emirates according to their attitudes towards food shopping[END_REF]. From the past examinations, it is discovered that the attraction of the shopping center passionate the consumer loyalty for a shopping center (Ala'Eddin Mohammad Khalaf Ahmad,2012). The intestinal things like music, smell and lighting manufacturers the enthusiasm for customer mind and these components delight the purchaser mindsets, shopper normally like these elements on its high inclinations [START_REF] Ezeh | Service scape research: a review and a research agenda[END_REF]. Format, Ambience, Music, Color, and Decoration are the five regular climatic things measured for the attraction (Chime, 1999;Frasquet et al,2001).

Brand Image can help in the estimation of shopping centers achievement, and it additionally hit consumer loyalty and business accomplishment of shopping centers. So, pouring the brand pictured in the shopping centers helps the client fascination [START_REF] Dennis | The mediating effects of perception and emotion: Digital signage in mall atmospherics[END_REF]. According to the American Marketing Association (2008), the brand extension also means "a product line extension marketed under the same generic brand as a previous item or items". A number of different varieties and different product on shelves in the shopping malls are more attractable for the customer (Boatwright and Nunes, 2001). Selection of the shopping mall purely depends on the satisfaction of the customer. Subsequently, the customer has to go through complex decision-making stage while deciding either to purchase from a retail or shopping mall ( Alavi, Sajat Et al: 2015).

Aesthetic Dimension People and Customer Purchase Intention:

Aesthetic dimension asthenic is basically the "Physical environment, review, inner appearance, outline, regular spots and lighter are the source of client attraction". The atmosphere affects the customer both, emotionally and psychologically [START_REF] Jackson | A tale of two images: functional vs. psychological components of three store types[END_REF]Babin and Attaway, 2000;Beyard et al., 2001;[START_REF] Dennis | The mediating effects of perception and emotion: Digital signage in mall atmospherics[END_REF][START_REF] Ooi | The magnetism of suburban shopping centres: do size and Cineplex matter[END_REF][START_REF] Ghee | How do in-store environmental cues influence Malaysian shoppers? A study of hypermarket customers in Malaysia[END_REF]. Shopping malls are required to provide the such an environment that enables a customer to shop and feel engaged in shopping, which enables individuals to shop and they feel abundantly engaged amid shopping (Ahamad,2007). The shopping center atmosphere is an effective stimulus for attracting clients towards stores [START_REF] Phillips | Age differences in information processing: a perspective onthe aged consumer[END_REF]Laroche et al., 2005;[START_REF] Michon | The influence of mall environment on female fashion shoppers' value and behaviour[END_REF]. The "wow sound" inside from the client for the shopping centers demonstrate his resemblance for the shopping centers. Customers feel delighted in shopping centers due to its environment. Aesthetic services have the strong impact on purchaser conduct and on administration assessments [START_REF] Turley | Atmospheric effects on shopping behavior: A Review of the Experimental Evidence[END_REF][START_REF] Zeithaml | Services Marketing[END_REF]. Music, Smell, and light have been the beneficial outcome on purchaser intrigue, and by tentatively it is discovered that these variables are more loved and ideal by the client [START_REF] Ezeh | Service scape research: a review and a research agenda[END_REF].

Escape Dimension & Customer Purchase Intention:

For clarifying the escape dimension, we have to clearly understand its definition. Escape Dimension is described as "The psychological sensory stimulation resulting from mall atmospherics, is an attracting factor relieving from boredom or loneliness".

Customers additionally visit shopping centers to occupy their energy. Customers become exhausted due to the everyday schedule. So, they move toward shopping centers as these centers are the best places for refreshing their temperaments. Shopping centers are the best places to vanish their exposed state. A shopping center is a place where the general population gets diversion at low costs; it is not just, where individuals run for shopping with family or companions (Ahmed et al., 2007). In some cases, they feel alone at home and need some appeal, so the shopping center is a place to accomplish something.

The relationship between loneliness and older consumers' mall patronage motives was explained by (Kim, Kang and Kim, 2005). Clients have strong perfectness for The shopping center with the distinctive assortments and updated things. The shopping centers situations provide energized climate, which has the strong impact on the purchaser dispositions (Bittner, 1992;[START_REF] Michon | The influence of mall environment on female fashion shoppers' value and behaviour[END_REF][START_REF] Ghee | How do in-store environmental cues influence Malaysian shoppers? A study of hypermarket customers in Malaysia[END_REF]. Shopping center facilities expand the day-by-day routine existence of purchaser, make them energized, and feel them revive.

Flow Dimension & Customer Purchase Intention:

For clarifying the flow dimension its necessary to understand its definition, "to get into the fantasy, or indulge in the specific activity for some time, or feel shocked after completing the activity," states the flow dimension. It is the desire to state for the client in the shopping centers. As the present day, shopping centers are made out of various engaging exercises like films, eateries, gaming zones, social spots and so forth in which individuals feel amid the shopping. The customer feels them in the other world when they are in the shopping center. There is generally the high constructive outcome from the youthful customers in stores when shopping excitement is high (Rajgopal, 2007). Due to the flow dimension, the customer mood saturated into the pleasure able state in the shopping mall. They now and then feel dim outside when leaving the shopping center. Variety of entertainment is nowadays much attractable for the customer which surely increases the patronage motives, towards malls, in which customer feels excited [START_REF] Haynes | Does entertainment draw shoppers? The effect of entertainment centres on shopping behavior in malls[END_REF][START_REF] Klara | Courting the future: once the ugly ducklings of the industry, mall eateries are going through some amazing transformations[END_REF][START_REF] Klara | Courting the future: once the ugly ducklings of the industry, mall eateries are going through some amazing transformations[END_REF][START_REF] Visser | Excitement at the mall: determinants and effects on shopping response[END_REF][START_REF] Michon | Incorporating service quality into consumer mall shopping decision making: a comparison between English and French Canadian consumers[END_REF]. The benefactors of shopping centers are sometimes influenced by the time and climate.

Exploration Dimension and Customer Purchase Intention:

Shopping centers are the great place to stay updated for the client. Since each shopping center is attempting to renovate their operations to attract client support. The store serves the client by providing them the awareness of new trend. A shopping center is a place to discover new things to purchase, it likewise the place where individuals cooperate with each other and locate the social hotspot (Ifrah Babar, 2009). It likewise serves to take in the encounters on various stage. The investigation, or finding out about new patterns, is a noteworthy drawing in considering. Shopping centers are much similar to capable for the client because of new items in the shopping centers. It is the immense advantage for the shopping centers to invest age the new items by the client in the shopping centers which reflect their thought processes them in support [START_REF] Visser | Excitement at the mall: determinants and effects on shopping response[END_REF]. Marked Shops, Household items, Entertainment are the which get a kick out of the chance to be the pioneer by a client in shopping centers. Empower purchasers to inspect items (Abratt et al., 1985;[START_REF] Mcgoldrick | Regional Shopping Centres[END_REF][START_REF] Prendergast | Retailers' views of shopping centres: a comparison of tenants and non-tenants[END_REF][START_REF] Yavas | A multi-attribute approach to understanding shopper segments[END_REF]. It is essential for the administration to decide the diverse assortment of inhabitant for the ideal assortment which is more attractable for the client.

Role Enactment and Customer Purchase Intention:

To clarify the definition, we must clearly understand the Role Enactment "patterned characteristics of social behavior during shopping in the shopping mall". Various type of individuals visits the shopping centers to perform distinctive kind of shopping, look at their costs, and deal with them.

People like to bargain in the store and feel like a wise shopper in the malls, generally, customer like to perform the bargain in the malls, grocery shopping is usually performing by the homemakers. Homemakers, Mother, Student and so forth act customarily expected and acknowledged as a feature of part of certain position amid shopping (Tauber,1972). Consumer reacts in the malls differently according to shopping behavior and social roles. Different people perform different types of roles to visit the malls, like being single, married, father and mother etc; they behave differently in malls during shopping [START_REF] Visser | Excitement at the mall: determinants and effects on shopping response[END_REF]. Several studies learned many different things from the diverse behavior of the customer who visits the mall and deals with shopkeepers or preference for shopping are different. Some consumer feels that the bargaining power of the purchaser or the process of haggling is viewed as a sport (Soloman, 1994). The recreational shoppers visit more frequently than the ordinary shoppers do because they are having a responsibility in shopping (mother, father etc.) [START_REF] Visser | Excitement at the mall: determinants and effects on shopping response[END_REF]. Many consumers used to go to malls because they have to fulfill their shopping need, which is significant to their identity.

Social Dimension and Customer Purchase Intention:

Social Dimension describes the following idea "to perform the social activity in the shopping mall like meeting with people, activates with family, visit with the friends etc." Shopping malls are proving the social platform for a customer. Role of a shopping center is to perform activates like recreational activities, social activities and community center (Ng, 2003). Shopping center provides the best place for people to enjoy with family, friends or some social conversation. There are distinctive approaches to build the faithfulness of clients with shopping centers by giving them desired products and by giving them social conditions and experiential needs (Ahamd, 2007). In the present modern era shopping is the process that provides the opportunity for doing social experience outside the home, like meeting with other peoples, or meeting with the opposite sex. A client gets a kick out of the chance to shop from where salespeople are useful and receptive to them, help them in the shopping, and act in the courteous way [START_REF] Loudon | Consumer Behavior: Concepts and Applications[END_REF]. Shopping centers are assuming the part likewise the meeting places for senior and more youthful individuals (Ahmad, 2007). A guest in the shopping center is appreciated by the motion picture in multiplex with the companions and feel energized in the shopping center time. Shopping centers are not assuming the part of just purchasing exercises but rather accommodating individuals for the social communication and social exercises like sustenance, motion pictures, or getting together with family or companions (Ahmad, 2007).

Convenience Dimension and Customer Purchase Intention:

For the purpose of getting the clear understanding of the concept of Convenience Dimension we first define it as "the location of the shopping mall, the operation timing, and friendly environment in which client feels more relax". Client preference for shopping mall depends upon shopping malls. Location and this factor have the great impact on the success of the business (eswa, 2013). Consumers are increasingly undertaking multi-purpose shopping trips they like one-stop shopping centers as it fulfills his multiple purposes for visiting the shopping center [START_REF] Arentze | A multi-purpose shopping trip model to assess retail agglomeration effects[END_REF]. Nowadays centers are competing on basis of time [START_REF] Richbell | Night shoppers in the 'open 24 hours' supermarket: a profile[END_REF]. Convenience (comprising trading hours) is the most important factor that affects customer satisfaction and has center revisit frequency [START_REF] Anselmsson | Sources of customer satisfaction with shopping malls: a comparative study of different customer segments[END_REF]. Mostly, people live in the focal point of urban communities; and due to the huge sizes of the urban areas, individuals feel trouble to reach the shopping centers, which are much far from them. Therefore, they, as a rule, favor the shopping centers that are closest to them. Location of a shopping mall dictates demographics of the area, growth patterns, traffic patterns and competitor's location [START_REF] Mariotti | Strategic Ignorance as a Self-Disciplining Device[END_REF]. Shopping centers distinguished on basis of various items including the environment of the shopping centers, Parking and so (Ruiz, 1999). Area of a shopping center manages socioeconomics of the region, development designs, movement examples and contender's area [START_REF] Mariotti | Strategic Ignorance as a Self-Disciplining Device[END_REF].

All dimensions that we discuss are important but the location of the shopping malls is the backbone factor for the patronage of the customer towards malls. The wide variety of products available in the shopping malls can decrease the perceived cost [START_REF] Pan | Determinants of retail patronage: a meta-analytical perspective[END_REF]. Simple location of the shopping center and the accessibility of the items is the best criteria for the client. The shopping centers comfort, exertion, wellbeing transport, parking, shopping centers timing are the characteristics, which are the basis for the client attraction towards shopping center (Ibrahim, 2002).

Shopping malls are now more concentrated because of the inclusion of all essential shopping related attributes [START_REF] Kaufman | A new look at one-stop shopping: a TIMES model approach to matching store hours and shopper schedules[END_REF]. The mixture of the different tenant in the shopping malls and the role of a bargain by the customer during shopping satisfies the customer motivational level. There are some standard parking structures in shopping centers which are likes by the customer. This exploration is being done in the number of nations like Canada, USA, Saudi Arab and the Middle East yet not directed in Pakistan with the particular measurements. What's more, the components are not clarified in detail. Still, there is a gap in the moderating variables. Demographics of customers are or moderating variable. The demographic variable is measured on the basis of only three attributed named Gender, Income, and Age.

The other gap examines specific factors that customers use to judge their enjoyment of a shopping experience in regional centers (John W. [START_REF] Cadogan | Shopping Experience Enjoyment: Impact on Customers' Repatronage Intentions, and Gender Influence[END_REF]. Further research would also be valuable in identifying other consumer segments than the ones identified in this study [START_REF] Cadogan | Shopping Experience Enjoyment: Impact on Customers' Repatronage Intentions, and Gender Influence[END_REF]. Another gap from the study is "First, the research should be simulated with other region and cities in KSA or other Gulf countries to further examine the attractiveness factors in the other industry (Ala'Eddin Mohammad Khalaf Ahmad,2012). Second, studying the proposed model in the service sector for examples spa villages, or hotels provide more validation for the model and more generalized findings (Ala'Eddin Mohammad Khalaf Ahmad,2012).

Demographics and Purchase Intentions

Age

Age is one of the demographic factors that has the impact on consumer purchase intention [START_REF] Findsen | Ageing, Older Adults, and Later Life Finn and Louviere[END_REF]. Customers are categorized into three categories on basis of age like teenage and old generation adult and each of these categories has different purchasing preferences. It does not mean that mature people are not interested in purchase the updated things. Mature consumers those new products that fit their aging needs, such as reader-friendly labels, helpful sales staff and increased print size [START_REF] Laukkanen | Innovation resistance among mature consumers[END_REF]. Mature people feel themselves to enough knowledgeable, and they need to take the advice from the youngster. Mature female clothing shoppers in South Africa have identified three segments: clothing moderates, clothing enthusiasts and clothing unconcerned [START_REF] Visser | Excitement at the mall: determinants and effects on shopping response[END_REF]. Older female consumers, who actively participated in leisure and formal social activities, were interested and involved in fashion and enjoyed apparel shopping (Joung and Miller,2002). It's all depend on client personal choice e.g. Mature consumers look for healthy food options, value quality more than quantity, is more inclined to utilitarian shopping and purchase apparel for pleasure or need rather than conformity [START_REF] Hu | A cross-cultural examination of the effects of social perception styles on store image formation Ifrah Babar[END_REF]. Usually, young consumer tries new things because its trendy to time. Perusing the most youthful and most established individuals in society will open doors for organizations (Irani and Rahmati, 2010).

Gender:

Shopping behavior also changes on the gender bases. Because though many researchers it is proved that males and females have different preferences. Each male and female have the distinctive part in the shopping conduct, they have diverse styles and desires, which recognized them from the shopping conduct. Therefore, Gender influences high in the shopping conduct (Bakashi, 2012). Moderating effect of gender on purchasing behavior was also considered by [START_REF] Kaur | Mall Purchase Behaviour -The moderating effect of Gender[END_REF]. Men invest more energy in shopping centers and went to shopping center often. Females are keener on fashion related items. Shopping centers are one of the mainstream places to purchasing the different items for both man and women (Kuruvilla, Joshi and shah, 2009). "State of gender differences in environmental attitude, environmental importance and understanding of the environmental responsibility of young consumers' purchasing behavior in Hong Kong" suggested that young women have more considerable scores in environmental attitudes, environmental importance and environmental responsibility in their purchasing behavior than men in Hong Kong [START_REF] Lee | State of Gender Differences in environmental attitudes, Environmental Importance, Understanding Environmental responsibility in the Purchasing Behavior of Young Customers in Hong Kong[END_REF]. "Evaluating the effect of green marketing mixture on consumers' purchasing decision making process" found that green marketing mixture can impact the women's purchasing decision more than men [START_REF] Hedieh Tondkar | The impact of corporate social disclosure on investment behavior: A cross-national study[END_REF]. Male customers are substantially quicker and much dependable than females about their purchase choices [START_REF] Dr | GENDER INFLUENCE ON PURCHASE INTENTION THE CASE STUDY OF THAILAND Eswa[END_REF].

Income:

Income is viewed as a crucial determinant of shopping decision. Since the level of income enables the client to determine the extent of shopping, and also customers purchase decision for a certain item and also. How often he visits the shopping center (Muhammad Ali Tirmizi,2009). As the number and spending power of customer increases and matures, the satisfaction factor needs more services from the shopping mall to achieve self-actualization stage of people (Ifrah Babar, 2011).

A number of studies have noted that mature segment has attractive financial status [START_REF] Hu | A cross-cultural examination of the effects of social perception styles on store image formation Ifrah Babar[END_REF]Laukkanen, Sinkkonen, Kivijarvi & Laukkanen, 2007;Moschis, Bellenger & Cusari, 2003;Walker & Mesnard, 2012). Therefore, some malls are equipped with the high class of brands which are usually unaffordable for the normal customer; whereas some malls have the economical shops that usually cannot make the strong positing in the mature customer mind. In Pakistan, there are four sorts of value lists; Consumer Price Index (CPI), Wholesales Price Index (WPI), Sensitive Price Index (SPI) and GDP Deflator, which are utilized to figure inflation. In this manner, the wage of the customer is the backbone for the shopping choice.

Theoretical framework:

Hypothesis Development:

H1: The greater a Pakistani consumer's preference for aesthetic stimulation, the greater that consumer's motivation to go to a shopping mall. H2: The greater a Pakistani consumer's desire for escape activity, the greater that consumer's motivation to go to a shopping mall. H3: The greater a Pakistani consumer's preference for flow, the greater that consumer's motivation to go to a shopping mall. H4: The greater a Pakistani consumer's desire for exploration, the greater that consumer's motivation to go to a shopping mall. H5: The greater the Pakistani consumer's drive to enact a role, the greater that consumer's motivation to shop in a mall. H6: The greater a Pakistani consumer's desire for social interaction, the greater that consumer's motivation to go to a shopping mall. H7: The greater a Pakistani consumer's desire for shopping convenience, the greater that consumer's motivation to go to a shopping mall.

Research Methodology

This research is quantitative in nature. The data is collected from the respondents of main cities of the country. It is a cross sectional study, the data was collected at one point of time. The targeted population in this study was regular visitors of the shopping malls. collected through online as well as self-administered questionnaires. Total 490 out questionnaires were filled out of them 416 were found complete and were considered fit for the analysis.

Data was analyzed through SPSS 24 software. Firstly, data was checked for missing entries. Then it was checked for regression assumption such as multicolinearity, homoscedasticity, overfitting and normality, skewness and kurtosis. The data was found fit for running regression on it. Cronbach Alpha test was run to check the reliability of the measure. The value of 0.954 for shopping mall facilities shows that the measures was highly reliable to collect the data. While the value of 0.60 for Customer purchase intention is acceptable. {Citation}

Descriptive

Results and Discussions

The impact of services provided by the shopping mall management on the customer purchases intention was analyzed with the help of regression model. The regression model confirms that all the independent variable has positive and significant impact on the purchase intention. 3 shows that independent variables i.e. aesthetic, escape, flow, explorer, role, social and convenience had come to be significant with the p value less than 0.05 which means that these independent variables effects significantly and positively on the dependent variable i.e. Purchase intension.

Moderation variables:

5.12.1 Income:

Conclusion and Recommendations

This study was focused on the shopping mall marketing strategies. As per the brick and kiln model this study is helpful to the mall administration to shifting the shopping trend under one roof. it is significant for the managerial side to know customer preference which increases the customer patronage in shopping mall. It is contribution towards literature as well as for managers.

This paper some limitations. This study is conducted in the big cities only. Future research can be directed to include other relevant variables considering, respondent from small cities which gives more accurate results.

Figure

  Figure 1: Conceptual Framework

  The measure was based on 32 items based on five point Likert scale measuring the concepts. The questionnaires was adopted from Zafar. U Ahmad(2007). The data was

	Gender
	Age
	Income
	Shopping Mall Services
	Escape
	Aesthetic
	Role Enactment
	Social	Customer Purchase Intention
	Convenience
	Exploration
	Flow

Table 1 -Variables description

 1

	Statistics:
	Gender	Frequency %	Marital Status	Frequency %
	Male	199	47.5	Single	244	58.9
	Female	215	52	Married	166	39.7
	Prefer Not Say	2	.5	Other	6	1.4
	Age			Source of Income
	15-25	142	34.1	Scholarship or study loan 18	4.4
	26-35	177	42.5	Sponsored by parents	157	38.2
	36-45	77	18.5	Self-sponsored	233	55.4
	46 & Above	20	4.9	Others	8	2
	Education			Monthly Personal
				Income
	Matric	29	6.9	0-10000	100	23.9
	Intermediate	55	12.8	11000-25000	98	23.2
	Bachelors	128	30.8	26000-50000	109	26.6
	Master degree	166	39.6	51000-100000	57	13.8
	MPhil/PhD	38	9.9	100000 and above	46	11.1

Table 2 Model Summary

 2

	Model	R	R Square	Adjusted R	Std. Error of the Estimate
				Square
	1	.818 a	.669	.663	.46002
	Predictors: (Constant), Aesthetic Dimension, Escape Dimension, Flow Dimension, Explorer Dimension, Role Dimension, Social
	Dimension, Convenience Dimension

Table 2

 2 represents that the value of R Square is .669 which indicates that there is 66 percent variance explained by dependent variable (purchase intention) due to change in predictors.

Table 3 Coefficients

 3

		Model	Unstandardized	Standardized	t	Sig.
			Coefficients	Coefficients
			B	Std.	Beta
				Error
	1	(Constant)	.531	.116		4.579	.000
		Aesthetic	.104	.042	.128	2.470	.014
		Escape	.076	.022	.165	3.409	.001
		Flow	.186	.036	.221	5.183	.000
		Explorer	.274	.042	.300	6.586	.000
		Role	.457	.031	.582	4.581	.000
		Social	.078	.035	.096	2.217	.027
		Convenience	.154	.043	.160	3.602	.000
							Table

Table 10 .1: Hypothesis Income 5.12.2 Gender:

 10 a. Dependent Variable: Purchase intentionThe above table shows the value of moderation variable of income. Income has positive relationship with Aesthetic, Escape and Social Dimension.The above table shows the value of moderation variables of Gender. It confirms that gender has the positive link with the Aesthetic, Escape, Explorer, Role and Social dimesion.

	Hypothesis Statements		Significance Hypothesis
				Status
	Income Moderation
	H1: Positive Relation Ship Between Income and
	Aesthetic Dimension.		0.044	Accepted
	H2: Positive Relation Ship between Income And Escape	0.042	Accepted
	Dimension.
	H3: Positive Relation Ship between Income And Social
	Dimension.		0.019	Accepted
	Hypothesis Statements	Significance Hypothesis
				Status
	H1: Positive Relation between Gender and Aesthetic
	Dimension.		0.000	Accepted
	H2: Positive Relation between Gender and Escape
	Dimension.		0.000	Accepted
	H3: Positive Relation between Gender and Explorer
	Dimension.		0.037	Accepted
	H4: Positive Relation between Gender and Role
	Dimension.		0.052	Accepted
	H5: Positive Relation between Gender and Social
	Dimension.		0.001	Accepted

Table 11 .1: Hypothesis Gender as Moderator Age:

 11 The above table shows the value of moderation variable of age. Age has the positive link with Flow and convince Dimension.

	Hypothesis Statements	Significance	Hypothesis
			Status
	H1: Positive Relation between Age and Flow
	Dimension.	0.066	Accepted
	H2: Positive Relation between Age and Convenience
	Dimension.	0.044	Accepted

Table 12 .1: Hypothesis Age as Moderator

 12