Impact of FDI on economy of Pakistan

Impact of Customer Satisfaction and Brand Image on Brand Loyalty:Evaluation for the Mobile Industry of Pakistan&Impact of Foreign Direct Investment and Unemployment on Economic GrowthCase Study of PakistanSubmitted By:KAMRANUDDIN (8534)[email protected] TALAL HASAN (7454)[email protected] MAJID (7621)[email protected] AWAN (7935)[email protected]

MBA ProgramSubmitted to:Mr. Tehseen Jawaid

Spring (2015)

PRIMARY REPORT

IMPACT OF BRAND IMAGE AND CUSTOMER SATISFACTION OF BRAND LOYALTY

Acknowledgement

In the Name of Allah, the most Beneficent, the most Merciful

We take this opportunity to express our profound gratitude and deep regards to our guide Mr. Tehseen Jawaid for his exemplary guidance, monitoring and constant encouragement throughout the course of this project. The blessing, help and guidance given by him time to time have given us the strength to look beyond our limits and get the best out of us.

AbstractThis study focuses on the effect of customer satisfaction & brand image on brand loyalty. Many researches indicate significant positive effect of customer satisfaction & brand image on brand loyalty. To test the robustness of initial results, empirical research is been conducted with the sample of 73 original customers.The respondents and their responses have been further tested by applying different statistical techniques of SPSS software. Results concluded from the study are that brand loyalty is significantly dependant on customer satisfaction and brand image.Both have a positive significant impact on brand loyalty but the impact of brand image on customer through proper positioning is slightly greater than customer satisfaction. The results indicate that when the companies provide high quality of service and quality product to the customers, the customer will become more satisfied and become loyal with the brand.

Key Words: Brand Image, Customer Satisfaction and Brand Loyalty

1. IntroductionAs the market is changing rapidly and competition is increasing furiously it is very important for marketers to understand that what effects Customer retention / brand loyalty. This is the time that marketers should put themselves in the shoes of customers and try to understand the importance of brand loyalty from their perspective. It will not only give them the understanding of how consumer perceive and buy brands but also help them in making better future marketing strategies.. Many studies have been done to spell out the effects of customer satisfaction & brand image on brand loyalty& most of them found significant relationship among variables. Ghafoor, iqbal & Murtaza (2011) identifies that brand image has greater effect on brand loyalty while Sondoh Jr, Omar, Wahid, Ismail and Harun (2007) explain customer satisfaction as an strong influential factor of brand loyalty. Customer satisfaction is the key variable for any business. Price, quality, response time & product variety etc they all sum up into customer satisfaction.Brand image is a perception in customers mind about the real & imaginary qualities & shortcomings. Its built over a period of time through massive brandings & delivering beyond customer expectations. To gauge brand image different attributes were asked to the respondents.If a customer is satisfied with the product, it will automatically lead to a better brand image & positive outcomes of both results in brand loyalty/ customer retention. Customer will prefer to buy again & again product of that brand.On the basis of previous studies hypothesis has been developed that there is a positive significant relationship among variables.The rest of the paper is organized as follows. Literature reviews the theoretical and empirical background on the effects of customer satisfaction and brand image on brand loyalty. Concluding Remarks and Implications provides some policy implications and set directions for further research.2. Literature Review2.1 Theoretical BackgroundAccording to Hsieh, Pan, and Setiono (2004), "a successful brand image enables consumers to identify the needs that the brand satisfies and to differentiate the brand from its competitors, and consequently increases the likelihood that consumers will purchase the brand" (p. 252). A company or its product/ services which constantly holds a favorable image by the public, would definitely gain a better position in the market, sustainable competitive advantage, and increase market share or performance (Park, Jaworski, & MacInnis, 1986). In addition, several empirical findings have confirmed that a favorable image (i.e. brand, store/retail) will lead to loyalty (e.g. Koo, 2003; Kandampully & Suhartanto, 2000; Nguyen & LeBlanc, 1998), brand equity (Faircloth, Capella, & Alford, 2001; Biel, 1992; Aaker, 1991; Keller, 1993), purchase behavior (Hsieh et al., 2004) and brand performance (Roth, 1995). Reynolds (1965) noted that "an image is the mental construct developed by the consumer on the basis of a few selected impressions among the flood of the total impressions; it comes into being through a creative process in which these selected impressions are elaborated, embellished, and ordered" (p. 69). Kotler (2001) defined image as "the set of beliefs, ideas, and impression that a person holds regarding an object" (p. 273). On the other hand, Keller (1993) considered brand image as "a set of perceptions about a brand as reflected by brand associations in consumer's memory" (p. 3). A similar definition to Keller's was proposed by Aaker (1991), whereby brand image is referred to as "a set of associations, usually organized in some meaningful way" (p. 109). Biel (1992) however defined brand image as "a cluster of attributes and associations that consumers connect to the brand name" (p. 8). Brand image has been conceptualized and operationalized in several ways (Reynolds & Gutman, 1984; Faircloth et al., 2001). It has been measured based on attributes (i.e. Koo, 2003; Kandampully & Suhartanto, 2000); brand benefits/ values (i.e. Hsieh et al., 2004; Roth, 1995; Bhat& Reddy, 1998); or using Malhotra's (1981) brand image scale (i.e. Faircloth et al., 2001). Measuring image based on the above definition would help marketers to identify the strengths and weaknesses of their brand as well as consumers' perceptions toward their product or services. For brand attitude, Keller (1993) referred to Wilkie's (1986) definition of brand attitudes which was "consumers' overall evaluations of a brand" (p. 4)Overall, image can generate value in terms of helping customer to process information, differentiating the brand, generating reasons to buy, give positive feelings, and providing a basis for extensions (Aaker, 1991). Creating and maintaining image of the brand is an important part of a firm's marketing program (Roth, 1995) and branding strategy (Keller, 1993; Aaker, 1991). Therefore, it is very important to understand the development of image formation and its consequences such as satisfaction and loyaltyOliver (1997) defined satisfaction as "the consumer's fulfillment response. It is a judgment that a product or service feature, or the product or service itself, provided (or is providing) a pleasurable level of consumption-related fulfillment, including levels of under- or over-fulfillment" (p. 13). Szymanski and Henard (2001) noted that previous research on consumer's satisfaction focused primarily on the effects of expectations, disconfirmation of expectations, performance, affect, and equity on satisfaction. The importance of expectations has been acknowledged in previous studies on customer's satisfaction (e.g. Churchill &Surprenant, 1982; Oliver, 1980; Tse& Wilton, 1988). Customer's expectations are pre-trial beliefs about a product (Olson & Dover, 1979) that function as comparison standards or reference points against which product performance is judged (Oliver, 1980; Bearden & Teel, 1983). The expectancy disconfirmation paradigm suggests that consumers are satisfied when the product perform better than expected (positive disconfirmation), dissatisfied when consumers' expectations exceeded actual product performance (negative disconfirmation), and neutral satisfaction when the product performance matches expectations (zero disconfirmation/confirmation) (Oliver, 1980; Churchill & Surprenant, 1982; Oliver & Sarbo, 1988; Bearden & Teel, 1983). Several researchers have explored different types of alternative comparison standards beside expectations such as experience-based norms (Woodruff, Cadotte, & Jenkins, 1983; Cadotte, Woodruff, & Jenkins, 1987); equity theory (Oliver & Swan, 1989; Tse& Wilton, 1988); desires (Spreng and Olshavsky, 1993), and ideal performance (Tse& Wilton, 1988). All of these aforementioned comparison standards have been tested empirically in customer's satisfaction/ dissatisfaction research. For this study, the satisfaction response will be reflected towards the level of affection for the brand which is in line with the suggestions by Jacoby and Chestnut (1978) and Oliver (1997, 1999). Oliver (1999) noted that consumers at the affective stage would develop a positive attitude towards the brand or liking the brand as a result of satisfactory repetitive usage over time.Loyalty Intention Jacoby and Chestnut (1978) have identified more than 50 operational definitions of brand loyalty, which can be classified as behavioral, attitudinal and the composite approach in the literature. Generally, more than 60% (33) of the 53 loyalty measures are behavioral terms found in Jacoby and Chestnut's (1978) work. Behavioral loyalty has been considered as repeat purchase frequency (e.g. Brown, 1952) or proportion of purchase (e.g. Cunningham, 1956), whereas attitudinal brand loyalty included "stated preferences, commitment or purchase intentions of the customers" (Mellens, Dekimpe, & Steenkamp, 1996: p. 513). However, most of these behavioral definitions above are criticized by Oliver (1999), Jacoby and Chestnut (1978) and Day (1969) as problematic. Oliver (1999) for instance argued that "all of these definitions suffer from the problem that they recorded what customer did, and none tapped into the psychological meaning of loyalty" (p. 34). The composite definition of loyalty emphasized two different approaches of loyalty: the behavioral and attitudinal concept, which was initially proposed by Jacoby and Chestnut (1978) and later by Oliver (1997) Jacoby and Chestnut (1978) provided a conceptual definition of brand loyalty as: (i) biased (i.e. non-random), (ii) behavioral response (i.e. purchase), (iii) expressed over time, (iv) by some decision-making unit, (v) with respect to one or more brands out of a set of such brands, and is a function of psychological (decision-making evaluate) processes. Brand loyalty can be functionalized either based on behavioral, attitudinal or composite approach (Jacoby & Chestnut, 1978). Behavioral loyalty has been considered as repeat purchases frequency (e.g. Brown, 1952) or proportion of purchase (e.g. Cunningham, 1956), while attitudinal brand loyalty referred to "stated preferences, commitment or purchase intentions of the customers" (Mellens et al., 1996: p. 513). In addition, few academicians suggested that using the composite approach (attitudinal and behavioral approach) will provide a more powerful definition of brand loyalty (Day, 1969; Jacoby & Chestnut, 1978; Dick & Basu, 1994). All of the above aforementioned approaches however have been argued by several scholars and have several limitations. Jacoby and Chestnut (1978) argued that the behavioral measures simply represent the static outcome of a dynamic decision process (i.e. solely on actual behavior). Therefore, this approach makes no attempt to understand the factor underlying brand loyalty purchasing and insufficient to clarify the causative factors that determine how and why brand loyalty developed or modified (Jacoby & Chestnut, 1978). The attitudinal measures are concerned with consumer feelings toward the brand and stated intention such as likelihood to recommend and likelihood to repurchase the product (Schiffman & Kanuk, 2004; Jacoby & Chestnut, 1978). Intention to repurchase can be measured by asking consumers about their future intentions to repurchase a given product or service (Jones & Sasser, 1995). Furthermore, Jones and Sasser (1995: p. 94) suggested that (i) companies can capture this information (i.e. intent to repurchase) when they measure satisfaction, making it relatively easy to link intentions and satisfaction for analytical purposes, (ii) intent to repurchase can be measured at any time in the customer relationship make its especially valuable in industries with a long repurchase cycle, and (iii) intent to repurchase is a strong indicator of future behavior.It is important to note that the entire brand loyalty phenomenon cannot be assessed if the attitudinal loyalty is not extended over the action behavior (Amine, 1988). In relation to loyalty, the linkages between attitude and behavior approach was found to be weak (East, Gendall, Harmond, & Wendy, 2005). For instance, Hennig-Thurau and Khee (1997) indicated that those studies that used actual behavior outcomes showed weak associations or negative relationships with satisfaction. Noting this, the authors will adopt the attitudinal approach as suggested by Rundle -Thiele and Bennett (2001) in conceptualizing the subject matter. Rundle-Thiele and Bennett (2001) argue that attitudinal loyalty measures should be appropriate to predict future brand loyalty under these circumstances: (i) where the market is not stable, (ii) where there is a propensity towards sole brands, and (iii) where there is a high involvement and high perceived risk.To sum up, the issues of loyalty mainly concerned on how loyalty is operationalized. It is very important to understand how we should measure loyalty. Although there are three approaches that can be used to measure loyalty (i.e. behavioral, attitudinal, and composite approaches), most researchers resorted to attitudinal measurement in terms of intention to repurchase and intention to recommend as an indicator of loyalty (e.g. Lau & Lee, 1999; Kandampully & Suhartanto, 2000; Sivadas& Baker-Prewitt, 2000; Chiou et al., 2002).2.2 Empirical StudiesThis section consists of some reviews on previously done studies.Ghafoor, Iqbal and Murtaza (2011) analyze the impact of customer satisfaction and brand image on brand loyalty by taking the sample of 200 respondents over a period of a month. Customer Satisfaction, Brand image and Brand loyalty are the variables. Different Statistical techniques are applied. Result shows that the customer satisfaction and brand image both have a significant positive impact on the brand loyalty. Brand Image has greater impact on brand loyalty than the customer satisfaction. It is suggested that the customers can be made loyal to the brand by providing satisfaction through better quality services and communicating and developing a good brand image through accurate positioning. Sondoh Jr, Omar, Wahid, Ismail and Harun (2007) analyze the effect of brand image on overall satisfaction and loyalty intention by carried out sample of 97 females. 5 brand images are taken as variables along with satisfaction (Independent variable) and Loyalty (Independent variables). Data were arranged with respect to their age, occupation, education, ethnic and income. Regression Techniques have been used. Result shows that overall satisfaction does influence customers' loyalty. It is suggested that marketers should focus on brand image benefits in their effort to achieve customer loyalty.Ahmed, Rizwan, Ahmad and Haq (2014) investigate the effect of service quality, perceived quality, perceived value, brand trust and customer satisfaction on brand loyalty by collecting data from the randomly selected 150 Hewlett Packard (HP) product consumers. Brand loyalty, brand trust, customer satisfaction is used as a variables. Descriptive nature of data is analyzed. The results shows a better understanding about brand loyalty among customers for companies to analyze and part played by each element in the progress of brand loyalty.Ramiz, Qasim, Aslam and Khursheed(2014) investigate what kinds of factors influence brand loyalty in Pakistan by using self-administered questionnaires we collected data from our 152 respondents. Brand loyalty, Brand image, Brand trust, Customer satisfaction, Perceived quality, Purchase criteria and Advertising are used as variables. Regression techniques have been used. Results show significant relationship of brand image, brand trust, customer satisfaction, perceived quality, purchase criteria and advertising spending on brand loyalty. These factors influence the green purchase intention. Saeed, Lodhi, Mehmood, Ishfaque, Dustgeer, Sami, Mahmood and Ahmad (2013) investigate to check the effect of brand image on brand loyalty and the moderating role of customer satisfaction in it by taking sample of 150 students and teachers. Brand Image, Brand Loyalty and Customer Satisfaction are used as variables. Pearson Correlation and Regression techniques have been used. The result shows that positive and significant relation exists between Brand Image and Brand loyalty and Customer Satisfaction. It is suggested that Organizations ought to pay special attention to the building of brand image, achieving customer satisfaction. And through this they would also be successful in achieving brand loyalty 3. Methodology3.1 Modeling Framework:To analyze the impact of customer satisfaction and brand image on brand loyalty, the sample has been collected through questionnaire survey from 73 respondents and 100% response was received in which male & female both were captured. The questionnaire survey was conducted at workplaces. The questionnaire survey contains two sections. First section contains questions that are about respondent personal profile in which they were asked about gender and age by nominal scaling technique. The second section contains 15 questions based on dependent variable brand loyalty and independent variables customer satisfaction and brand image by using 5-likert scaling technique.The model used in this research is:Brand Loyalty = + 1 (Brand Image) + 2 (Customer Satisfaction)

4. Results & Estimation4.1 ReliabilityFor checking the reliability of the data, we use reliability test by using SPSS. The questionnaire for this study is based on 15 questions which includes both dependent & independent variables.Table 4.1.1[footnoteRef:1] [1: See Appendix-A, Table-2, Page#35-36.]

Reliability Statistics

Cronbach's AlphaN of Items

.85615

The main focus on the value of Cronbachs Alpha which should be more than 0.5 or 50%. Now we can see that the value of Cronbachs Alpha for this study is 0.856 means 85.6% data is reliable and acceptable for this study.Data reliability has been demonstrated by applying statistical tests of reliability. The questionnaire for this study based on 15 questions which includes both dependent and independent variables. Reliability test has been applied in SPSS software and according to the limitation; the value of Cronbachs Alpha should be more than 0.5 means 50%. The Cronbach alpha value of this study is means %, and shows that the reliability of data should be acceptable.

4.2 Factor Analysis KMO & Bartletts Test is a measure of sampling adequacy and it shows that how much sample is accurate for this study. For factor analysis to be recommended suitable, the value of KMO should be more than 0.5 or 50% and the value of Bartletts test should be less than 0.05 means it should have significant.Table 4.2.1[footnoteRef:2] [2: See Appendix-A, Table-4, Page#38.]

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy..756

Bartlett's Test of SphericityApprox. Chi-Square503.074

Df105

Sig..000

From the above table, the value of KMO is 0.756 means 75.6% and the value of Bartletts test is 0.000 which less than 0.05 means that the factor analysis is significant.

Table 4.2.2

Rotated Component Matrixa

Component

123

SAMSUNG SMART PHONE has good/reasonable prices..796

SAMSUNG SMART PHONE has a large variety of products..790

SAMSUNG SMART PHONE offers the greatest range of different features and prices..780

SAMSUNG has a differentiated image from other smart phones..707

I Preseive a very good image of SAMSUNG..697

The product is sincere to the customers. SAMSUNG is known for providing the best quality..693

SAMSUNG SMART PHONE is fimiliar to my needs..570

I Would love to recommend SAMSUNG SMART PHONE to my friends..886

SAMSUNG SMART PHONE would be my first choice when considering smart phones..777

I will keep on buying as long as the SAMSUNG provides me satisfied products..687

I Consider myself to be loyal to SAMSUNG SMART PHONE..658

I will not buy another brands of smart phone if SAMSUNG SMART PHONE is available at the store..623

I relate some specific characteristics of SAMSUNG SMART PHONE..474

In general, I am satisfied with the SAMSUNG SMART PHONE.-.606

I feel comfortable when I buy SAMSUNG SMART PHONE..540

Rotated component matrix defines the correlation in the variables to the dependent variable. The higher the value shows the highest level of correlation to the dependent variable. It also show that how much independent variables are fit in the data

4.3 Regression Analysis: Regression analysis is used for forecasting the relationships among variables. It also shows that how much independent variables effect on dependent variable.In the table below the R Square value is taken as for explanation of the overall model. The overall model is 26.5% explained by the variables.

Model Summaryb

ModelRR SquareAdjusted R SquareStd. Error of the EstimateDurbin-Watson

1.534a.286.265.688721.803

4.4.2 Independent Sample T-test: Here we compare the averages of variable across the two categories. i.e. averages of Male and Female among Brand Loyalty and other variables.The claim for our data is that variances are equal. I.e. Ho: Variances are equalTable 4.4.2.1One-Sample Test

Test Value = 0

tdfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference

LowerUpper

Brand Loyalty33.67972.0003.167122.97973.3546

Brand image45.17472.0003.553423.39663.7102

Customer Satisfaction45.21872.0003.493153.33923.6471

The above table 4.4.2.1 shows the mean of Male & Female in three of the above variables.

Independent Samples Test

Levene's Test for Equality of Variancest-test for Equality of Means

FSig.TdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference

LowerUpper

BrandimageEqual variances assumed1.5570.216-0.635710.527-0.100450.15811-0.415720.21481

Equal variances not assumed-0.64268.6410.523-0.100450.15637-0.412420.21152

CustomerSatisfactionEqual variances assumed0.9060.344-1.116710.268-0.172330.15437-0.480130.13547

Equal variances not assumed-1.10665.2450.273-0.172330.15576-0.483390.13873

BrandLoyaltyEqual variances assumed7.2240.0091.667710.10.310080.18595-0.06070.68085

Equal variances not assumed1.64560.930.1050.310080.18851-0.066880.68703

Table 4.4.2.2 shows leveenes test which tells us whether variances are different or not. In levenes test we claim that variances are equal. Here we see the sig value for levenes test in first two cases i.e. Brand Image and Customer Satisfaction is greater than 0.05, thus we accept our claim that is variances are equal but in case of Brand Loyalty the variance is not equal. Once we accept the claim we choose row named equal variance assumed to check the sig value of independent sample t-test. In independent sample t-test we check whether the mean of two different groups is different or not. We claim that mean of two groups are not different. In all the above cases the sig value is greater than 0.05, thus we accept our claim that is mean of two groups are not different.

5. Conclusion and RecommendationsIt is concluded that the brand loyalty, brand image and the customer satisfaction are correlated with each other, so companies need to focus on these three factors altogether in order to gain profit and become stable in the market.On the basis of all essential tests and past studies we also said that the brand loyalty has significant and positive relationship with the customer satisfaction and brand image, thus when the customer get high quality product in reasonable prices the customer become more satisfied and loyal with the specified product or service.In this competitive era the price and quality is no more the only differentiation factor among the various brands, now brand loyalty is the only differentiation factor that is basically the real asset of an organization or that is not copied by others; hence it is recommended that the companies make serious commitments to invest in making the brand strategies for the product and services.This research is also valuable document for Pakistani mobile industry so on the basis of this research it is recommended that they need to put investment in order to finding out the needs and wants of the customers related to their product. In other words they need to make their research and development department more effective in order to garb more and more market share.

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Appendix-A1. Outliers

Case Processing Summary

Cases

ValidMissingTotal

NPercentNPercentNPercent

BL173100.0%00.0%73100.0%

BL273100.0%00.0%73100.0%

BL373100.0%00.0%73100.0%

BL473100.0%00.0%73100.0%

BL573100.0%00.0%73100.0%

BI173100.0%00.0%73100.0%

BI273100.0%00.0%73100.0%

BI373100.0%00.0%73100.0%

BI473100.0%00.0%73100.0%

BI573100.0%00.0%73100.0%

CS173100.0%00.0%73100.0%

CS273100.0%00.0%73100.0%

CS373100.0%00.0%73100.0%

CS473100.0%00.0%73100.0%

CS573100.0%00.0%73100.0%

Descriptive

StatisticStd. Error

BL1Mean3.1918.13181

95% Confidence Interval for MeanLower Bound2.9290

Upper Bound3.4545

5% Trimmed Mean3.2131

Median3.0000

Variance1.268

Std. Deviation1.12617

Minimum1.00

Maximum5.00

Range4.00

Inter quartile Range2.00

Skewness-.570.281

Kurtosis-.562.555

BL2Mean3.1370.14243


Upper Bound3.4209


Median3.0000

Variance1.481


Minimum1.00

Maximum5.00

Range4.00


Skewness-.174.281

Kurtosis-.734.555

BL3Mean2.4932.10527


Upper Bound2.7030


Median2.0000

Variance.809

Std. Deviation.89943

Minimum1.00

Maximum4.00

Range3.00


Skewness.021.281

Kurtosis-.717.555

BL4Mean3.4521.11381


Upper Bound3.6789


Median4.0000

Variance.946


Minimum1.00

Maximum5.00

Range4.00


Skewness-.933.281

Kurtosis.469.555

BL5Mean3.5616.11863


Upper Bound3.7981


Median4.0000

Variance1.027


Minimum1.00

Maximum5.00

Range4.00


Skewness-.666.281

Kurtosis.458.555

BI1Mean3.0959.10890


Upper Bound3.3130


Median3.0000

Variance.866


Minimum1.00

Maximum5.00

Range4.00


Skewness-.620.281

Kurtosis.275.555

BI2Mean3.7534.11338


Upper Bound3.9794


Median4.0000

Variance.938


Minimum1.00

Maximum5.00

Range4.00


Skewness-.989.281

Kurtosis1.140.555

BI3Mean3.4795.11722


Upper Bound3.7131


Median4.0000

Variance1.003


Minimum1.00

Maximum5.00

Range4.00


Skewness-.326.281

Kurtosis-.706.555

BI4Mean3.7945.10854


Upper Bound4.0109


Median4.0000

Variance.860


Minimum1.00

Maximum5.00

Range4.00


Skewness-.648.281

Kurtosis.207.555

BI5Mean3.6438.11764


Upper Bound3.8783


Median4.0000

Variance1.010


Minimum1.00

Maximum5.00

Range4.00


Skewness-.492.281

Kurtosis-.472.555

CS1Mean3.0548.14842


Upper Bound3.3507


Median3.0000

Variance1.608


Minimum1.00

Maximum5.00

Range4.00


Skewness-.063.281

Kurtosis-.981.555

CS2Mean3.8630.11427


Upper Bound4.0908


Median4.0000

Variance.953


Minimum2.00

Maximum5.00

Range3.00


Skewness-.638.281

Kurtosis-.480.555

CS3Mean3.6164.12124


Upper Bound3.8581


Median4.0000

Variance1.073


Minimum1.00

Maximum5.00

Range4.00


Skewness-.552.281

Kurtosis.021.555

CS4Mean3.3562.11098


Upper Bound3.5774


Median3.0000

Variance.899


Minimum2.00

Maximum5.00

Range3.00


Skewness-.075.281

Kurtosis-1.002.555

CS5Mean3.5753.10847


Upper Bound3.7916


Median4.0000

Variance.859


Minimum2.00

Maximum5.00

Range3.00


Skewness-.442.281

Kurtosis-.676.555

BL1

BL2

BL3

BL4

BL5

BI1

BI2

BI3

BI4

BI5

CS1

CS2

CS3

CS4

CS5

2.Reliability

Reliability Statistics

Cronbach's AlphaN of Items

.5643

Item-Total Statistics

Scale Mean if Item DeletedScale Variance if Item DeletedCorrected Item-Total CorrelationCronbach's Alpha if Item Deleted

Brand loyality7.06651.254.455.329

Brand image6.62101.252.411.402

Customer satisfaction6.75601.643.265.612

ANOVA with Tukey's Test for Nonadditivity

Sum of SquaresdfMean SquareFSig

Between People61.31772.852

Within PeopleBetween Items7.62023.81010.260.000

ResidualNonadditivity.023a1.023.062.804

Balance53.449143.374

Total53.472144.371

Total61.092146.418

Total122.410218.562

Grand Mean = 3.4073

a. Tukey's estimate of power to which observations must be raised to achieve additivity = 1.355.

3. Regression

Model Summaryb

ModelRR SquareAdjusted R SquareStd. Error of the EstimateDurbin-Watson

1.472a.223.201.660981.821

a. Predictors: (Constant), customer satisfaction, Brand image

b. Dependent Variable: Brand loyality

ANOVAa

ModelSum of SquaresDfMean SquareFSig.

1Regression8.76924.38510.036.000b

Residual30.58270.437

Total39.35272

a. Dependent Variable: Brand loyality

b. Predictors: (Constant), customer satisfaction, Brand image

Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics

BStd. ErrorBetaToleranceVIF

1(Constant)1.097.5012.190.032

Brand image.390.103.4073.785.000.9601.041

Customer satisfaction.188.118.1721.597.115.9601.041

a. Dependent Variable: Brand loyality

Residuals Statisticsa

MinimumMaximumMeanStd. DeviationN

Predicted Value2.29273.78483.1553.3489973

Residual-2.540581.19917.00000.6517373

Std. Predicted Value-2.4721.804.0001.00073

Std. Residual-3.8441.814.000.98673

a. Dependent Variable: Brandloyality

4. Factor Analysis

Correlation Matrixa

a. Determinant = .000

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy..756

Bartlett's Test of SphericityApprox. Chi-Square503.074

Df105

Sig..000

Total Variance Explained

ComponentInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings

Total% of VarianceCumulative %Total% of VarianceCumulative %Total% of VarianceCumulative %

15.30135.33835.3385.30135.33835.3383.88725.91425.914

22.12814.18549.5242.12814.18549.5243.49523.29849.211

31.2748.49458.0171.2748.49458.0171.3218.80658.017

41.1457.63565.652

5.9646.43072.082

6.8225.48077.562

7.6964.64182.203

8.6044.02786.230

9.5343.56389.793

10.4523.01292.806

11.3102.06894.874

12.2631.75096.624

13.2021.34897.972

14.1681.12399.095

15.136.905100.000

Extraction Method: Principal Component Analysis.

Component Matrixa

Component

123

I Consider myself to be loyal to SAMSUNG SMART PHONE..652

SAMSUNG SMART PHONE would be my first choice when considering smart phones..707

I will not buy other brands of smart phone if SAMSUNG SMART PHONE is available at the store..425

I will keep on buying as long as the SAMSUNG provides me satisfied products..652

I Would love to recommend SAMSUNG SMART PHONE to my friends..641.615

I relate some specific characteristics of SAMSUNG SMART PHONE..433

I Preseive a very good image of SAMSUNG..770

SAMSUNG has a differentiated image from other smart phones..641

The product is sincere to the customers. SAMSUNG is known for providing the best quality..666

SAMSUNG SMART PHONE offers the greatest range of different features and prices..651-.450

SAMSUNG SMART PHONE has good/reasonable prices..604-.531

SAMSUNG SMART PHONE has a large variety of products..606-.511

SAMSUNG SMART PHONE is familiar to my needs..764

I feel comfortable when I buy SAMSUNG SMART PHONE.-.511

In general, I am satisfied with the SAMSUNG SMART PHONE..640

Extraction Method: Principal Component Analysis.

a. 3 components extracted.

Component Transformation Matrix

Component123

1.745.662.081

2-.666.730.154

3-.043.169-.985

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

5. One Sample T TestOne-Sample Statistics

NMeanStd. DeviationStd. Error Mean

Brand loyality733.1553.73929.08653

Brand image733.6008.77077.09021

Customer satisfaction733.4658.67355.07883

One-Sample Test

Test Value = 0

tDfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference

LowerUpper

Brand loyality36.46572.0003.155252.98283.3277

Brand image39.91572.0003.600783.42093.7806

Customer satisfaction43.96372.0003.465753.30863.6229

6. Independent Sample T TestGroup Statistics

DemographicsNMeanStd. DeviationStd. Error Mean

Brand loyalityFemale383.2851.60764.09857

Male353.0143.84634.14306

Brand imageFemale383.5075.77659.12598

Male353.7020.76255.12889

Customer satisfactionFemale383.4474.62374.10118

Male353.4857.73250.12381

Independent Samples Test

Levene's Test for Equality of Variancest-test for Equality of Means

FSig.tdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference

LowerUpper

Brand loyaltyEqual variances assumed3.925.0511.58071.119.27080.17143-.07102.61262

Equal variances not assumed1.55961.260.124.27080.17373-.07656.61817

Brand imageEqual variances assumed.081.777-1.07871.284-.19452.18037-.55417.16513

Equal variances not assumed-1.07970.700.284-.19452.18023-.55393.16488

Customer satisfactionEqual variances assumed.807.372-.24171.810-.03835.15884-.35507.27838

Equal variances not assumed-.24067.083.811-.03835.15990-.35750.28081

Impact of Foreign Direct Investment and Unemployment on Economic GrowthCase Study of PakistanKAMRANUDDIN (8534)[email protected] TALAL HASAN (7454)[email protected] MAJID (7621)[email protected] AWAN (7935)[email protected]

MBA ProgramSubmitted to:Mr. Tehseen Jawaid

Spring (2015)

SECONDARY REPORT

Impact of Foreign Direct Investment and Unemployment on Economic Growth

ABSTRACT:This paper investigates the impact of Foreign Direct Investment and Unemployment on Economic Growth (GDP) of Pakistan. This relationship is tested by applying Ordinary Least Square method. The GDP is taken as dependent viable while FDI and Unemployment are considered as independent variables. The data used for this is ranging from year 1991 to 2013 of Pakistan. The result shows that the overall model is significant. There is a positive and significant relationship between GDP and FDI while the study showed no significant effect of unemployment on GDP.Keywords: Gross Domestic Product, Foreign Direct Investment, Unemployment, GDP dependent.

1. INTRODUCTION:

Foreign direct investment (FDI) refers to long term participation by a country A into country B (in this case Pakistan) . It usually involves participation in management, joint-venture, transfer of technology and expertise. Increasing foreign investment can be used as one measure of growing economic globalization.Foreign Direct Investment (FDI) has emerged as the most important source of external resource flows to developing countries over the years and has become a significant part of capital formation in these countries. The role of the foreign direct investment (FDI) has been widely recognized as a growth-enhancing factor in the developing countries (Khan, 2007). The effects of FDI in the host economy are normally believed to be; increase in the employment, augment in the productivity, boost in exports and amplified pace of transfer of technology.The relationship between Gross Domestic Product (GDP) and unemployment rates can be seen by the application of Okuns Law. According to the principles established by this law, there is a corresponding two percent increase in employment for every established one percent increase in GDP. The reasoning behind this law is quite simple. It states that GDP levels are driven by the principles of demand and supply, and as such, an increase in demand leads to an increase in GDP. Such an increase in demand must be accompanied by a corresponding increase in productivity and employment to keep up with the demand.GDP and unemployment rates are linked in the sense that both aremacroeconomic factorsthat are used to gauge the state of an economy. GDP and unemployment rates usually go together because a decrease in the GDP is reflected in a decrease in the rate of employment.

Unemployment is the macroeconomic problem that affects individuals most differently and severely. The loss of employment means reduced standard of living and psychological stress. Researchers study unemployment to identify its causes and to help in policies that affect unemployment. Levinson (2008) explained that unemployment is associated with social problems such as poverty, crime, violence, a loss of morale and degradation. The significance of employment lies not only in the income earned but also the intangible and invaluable benefits it provides including dignity, accomplishment and freedom. High job opportunities and economic participation would help in reducing poverty and income inequality. Ernst and Berg (2009) explain that high growth is associated with a high degree of employment intensity which is a necessary condition for the reduction of poverty. In 2006, 195 million workers were unemployed, amounting to 6.3% of the world labour force. that same year, 1.37 billion workers, nearly half of the world workers were considered as working poor implying that they live less than U$D 2 dollars per day.

2. LITERAURE REVIEW:

2.1 THEORETICAL BACKGROUND:Jyun-Yi, Wu and Hsu Chin-Chiang (2008) they examine whether the FDI promote the economic growth by using threshold regression analysis. The empirical analysis shows that FDI alone play an ambiguous role in contributing to economic growth based on a sample of 62 countries covering the period from 1975 to 2000 and find that initial GDP and human capital are important factor in explaining FDI. FDI is found to have a positive and significant impact on growth when host countries have better level of initial GDP and human capital. Laura Alfaro at el (2003) they examine the various links among FDI and GDP growth. They explore whether countries with better financial systems can exploit FDI more efficiently. Using empirical analysis using cross-country data between 1975 and 1995 shows that FDI alone plays an ambiguous role in contributing to economic growth, however countries with well developed financial markets gain significantly from FDI in their economic growth.

2.2EMPIRICAL STUDIES:Amna Imran and Salman analyzed the impact of foreign direct investment (FDI) in Pakistan for the period 1981 to 2010. It evaluated the GDP growth performance and assessed the historical trends of the FDI and CPI in Pakistan.The link between gross domestic product (GDP,) foreign direct investment and Inflation is measured with the help of multiple regression models. GDP in this model is used as dependent variable whereas FDI and inflation (CPI) are measured as independent variables. According to the results, the model is overall significant with the positive and significant association of GDP and FDI while a negative and significant relationship found between GDP and inflation.Nadeem, Naveed, Zeeshan and Sonia validated the relationship between GDP and FDI . Foreign direct investment (FDI) is considered as a growth accelerating component that has received a great attention in developed countries even in developing and less developed countries during recent years. This model consists of three variables; two independent factors and one dependent factor i.e. Foreign Direct Investment (FDI), Gross Domestic Product (GDP) and openness to trade (OP). 30 year data from 1983 to 2012 was collected and the cobb-Douglas Production function is used to test the relationship. For data analysis, they have examined the descriptive statistics, correlation and regression model. For this they incorporated the production function in regression model. In brief, their results show that there is a positive relationship between FDI and GDP in Pakistan.Misbah investigated the impact of foreign direct investment on Growth (GDP) for Pakistan. He studied a long run relationship between the foreign direct investment and gross domestic investment in Pakistan. By using cointegration analysis, he demonstrated that there exists a long run relationship between the two variables. The GDP is taken as dependent viable while FDI is considered as independent variables. The data used for this analysis is varying from 1980 to 2010. The result shows that there exists a long term relationship between GDP and FDI.Lim and Pehlaj analyzed the relationship between Gross Domestic Product and Foreign Direct Investment. Quantitative approach is conducted by simple regression analysis by Ordinary Least Square (OLS) to capture the long-term relationship between FDI inflows and GDP in Cambodia. The data sets are obtained from World Development Indicator (WDI) collected and developed by World Bank. Gross Domestic Product (GDP) is used as the indicator for economic growth and net FDI inflows for FDI. Both indicators are annual data set measured in million US dollar from 1993 to 2011. Limitations on regression analysis include there are only two variables used in this study FDI and GDP. All data of both variables was in current US dollar in millions. There is only one model (OLS estimator) was used to determine the relationship between the two variables, the econometric methodology is limited to examine only the long term relation, and there are only 19 annual observations, so if the results from this study are considered to use for other purposes rather than academic purpose, a few more samples and tests should be included (i.e. Error Correction Model and Causality Test).Qaisar, Salman, Ali, Hafiz and Muhammad investigated the impact of foreign direct investment on Growth (GDP) of SAARC countries. The relationship is tested by applying multiple regression models. The change in GDP is taken as dependent variable while FDI and inflation are considered as independent variables. The data used for this is ranging from year 2001 to 2010 of SAARC Countries. The result shows that the overall model is significant. There is a positive and significant relationship between GDP and FDI while an insignificant relationship between GDP and inflation.Nuzhat Falki (2009) examined the Impact of FDI on Economic Growth of Pakistan. She collected the data of FDI from the Handbook of Pakistan Economy-2005 published by the State of Pakistan and the World Bank Development indicators-2008 from 1980 to 2006 with variables of domestic capital, foreign owned capital and labor force. With the help of endogenous growth theory and applying the regression analysis she concluded that FDI has negative statically insignificant relationship between GDP and FDI inflows in Pakistan.Jyun-Yi, Wu and Hsu Chin-Chiang (2008) they examine whether the FDI promote the economic growth by using threshold regression analysis. The empirical analysis shows that FDI alone play an ambiguous role in contributing to economic growth based on a sample of 62 countries covering the period from 1975 to 2000 and find that initial GDP and human capital are important factor in explaining FDI. FDI is found to have a positive and significant impact on growth when host countries have better level of initial GDP and human capital. Laura Alfaro at el (2003) they examine the various links among FDI and GDP growth. They explore whether countries with better financial systems can exploit FDI more efficiently. Using empirical analysis using cross-country data between 1975 and 1995 shows that FDI alone plays an ambiguous role in contributing to economic growth, however countries with well developed financial markets gain significantly from FDI in their economic growth.M. Sayeed Alam and Mahmud Zubayer (2010) they founded that in SAARC FDI from outside is more important than in intra regional investments in most the countries (the only exception is Nepal) where Indian investments dominated. The concept of some region can be applicable to increase intra regional FDI. The FDI has a significant impact on GDP of SAARC countries.Shaari, Hussain and Ab.Halim [16] examined the impact of FDI on the unemploy-ment rate and economic growth for Malaysia over the period 1980-2010. The resultsof the study showed that FDI helps in reducing unemployment, creating more domes-tic jobs and also hasa positive effect on GDP.Abbas, Akbar, Nasir , Aman Ullah and Naseem Global Journal of Management and Business Research Volume 11 Issue 8 Version 1.0 August 2011 investigated the impact of foreign direct investment on Growth (GDP) of SAARC countries. This relationship is tested by applying multiple regression models. The change in GDP is taken as dependent viable while FDI and inflation are considered as independent variables. The data used for this is ranging from year 2001 to 2010 of SAARC Countries. The result shows that the overall model is significant. There is a positive and significant relationship between GDP and FDI while an insignificant relationship between GDP and inflation.3.MODELING FRAMEWORKThe aim of this study is to examine the impact of Foreign Direct Investment (FDI) and Unemployment (UEP) on Gross Domestic Product (GDP). The FDI has positive relationship with GDP while UEP has negative relationship with GDP. For this purpose the data was collected from Ministry of Finance (www.finance.gov.pk) for Pakistan from period 1991 to 2013. Our model consist of three variable that are GDP, FDI, UEP of which GDP is dependent variable and FDI and UEP independent variables.

GDP = + 1(FDI) + 2(UEP) + e

1. Estimation and ResultsAt the beginning the trend in the variables was tested using Stationary Analysis to check whether the regression we are going to perform is correct or spurious. Stationary Analysis for the detection of variable that overfits the model. The Hypothesized claim is:Ho : The Series is Not StationaryThe Unit Root Test for Stationary Analysis confirms that there is a trend at level in GDP and UEP but no trend at 1st Difference. However in case of FDI there is a trend at Level intercept but no trend at Level Intercept & Trend. Since there is a trend in FDI at Level Intercept so we are proceeding as per this assumption. The results of Unit Root Test are mentioned in Table 4.1 below.Table 4.1[footnoteRef:3] [3: See Appendix-A, Table-12 See Appendix-A, Table 2]

Stationary Test ResultsVARIABLESLEVEL1ST DIFFERENCE

INTERCEPTINTERCEPT & TRENDINTERCEPTINTERCEPT & TREND

Sig.Sig.Sig.Sig.

GDP0.99990.9870.03170.097

FDI0.12040.03420.05700.1968

UEP0.18290.13930.00000.0001

HYPOTHESIS:The hypothesized claim about the study is as follows.Ho: FDI has no significant impact on GDPHo: UEP has no significant impact on GDPTable 4.1.12Long Run Determinants of Gross Domestic ProductVariableCoefficientt-StatisticProb value

C31.326521.08960.288

FDI17.06052.42200.0251

UEP14927.911.57380.1312

Adj. R0.30822F-statistic5.9011

D.W0.27518Prob value0.0000

Source:Authors estimations

To determine the relationship of considered variables, regression technique is applied. Results of the test are shown in Table 4.1.1. It is clear that there is a significant impact of FDI on GDP. In table we have regression statistics of our proposed model. The R-square of this model is at a 0.371, which suggest that the 63% variation in this model is unexplained while the remaining variation of this model is explained by FDI and UEP. Durbin Watson is 0.27518 so, there was a chance of autocorrelation and we can check this through Breusch-Godfrey Serial Correlation LM Test[endnoteRef:1][footnoteRef:4] and accepted our null hypothesis that is there is no autocorrelation is present in our model . [1: ] [4: ]

In our study we also checked our independent variables i.e. FDI and UEP whether multicollinearity exist or not. The table 4.3.1 below shows the results. Both the variables shows VIF values 1.104 which is less than 10. We have no serious issue of multi collinearity.

Table 4.2.11VARIANCE INFLATION METHOD

FDI1.104

UEP1.104

JOHANSEN CO-INTEGRATION TEST:HYPOTHESIS:Ho: NO CO-INTEGRATION EXIST AMONG THE VARIABLES Table 4.2.22Cointegration Test ResultsHypothesizedTrace5%Max. Eigen5%

No. of CE(s)StatisticCritical Valuevalue statisticsCritical Value

None *47.223029.797030.6819

21.1316

At most 116.541015.4947112.722114.2646

At most 23.81833.84143.81833.8414

The most important is that when there are more than two variables in the model, there can be more than one cointegration vector. The approach developed by Johansen (1988, 1991) and extended by Johansen and Juselius (1990) is considered superior to the Engle-Granger method. This approach provides a multivariate framework and allows for more than one cointegration vectors. Johansen and Juselius (1990) have derived two tests for cointegration, namely, the Trace test and the Maximum Eigen value test. The computed Trace and Maximum Eigen value test statistics are about their corresponding critical values are presented in Table 4.2.As shown in table 4.2.2 that Trace statistic is greater than Critical value & Max. Eigen statistic is greater than Critical value therefore our null hypothesis has been rejected and cointegration exist among variables.

Graph 4.3CUSUM and CUSUM of Squares test

We can check consistency of data through CUSUM and CUSUM of Squares test as shown in the graph 4.3.In CUSUM test the results are within 2 standard deviations but CUSUM of Squares test show fluctuation in 1998 till 2011 and outsides the 2 standard deviations. So, we can confirm this through chow breakpoint test as shown in the table 4.5CHOW BREAKPOINT TEST:HYPOTHESIS:H0: COFFICIENTS ARE NOT DIFFERENTTable 4.5[footnoteRef:5] [5: See Appendix-A, Table-5, Page#16.]

Chow breakpoint

Prob.F(3,17)0.5493

F-statistics0.727945

In table 4.5 we checked the consistency of beta through Chow breakpoint test by taking the year 1998 and the prob. value that is greater than 0.1 which means that coefficient are not different before and after 1998 and we can accept our hypothesis that coefficients are not different.Causality AnalysisThe directions of causality between GDP, FDI & UEP remain unspecified. One mode of dealing with such an issue is to find out the direction of causality using Granger causality method. The usual Granger causality leads to forged regression results unless the variables in level are co integrated. Also Granger causality deals with bivariate regression model. On the other hand Toda and Yamamoto (1995) procedure uses a modified Wald (MWALD) test which can be applied irrespective of order of integration and also deals with multivariate regression model.The results of Granger causality test based on Toda and Yamamoto procedure are reported in Table 5.1. The values in parentheses are probability values.Table 5.1[footnoteRef:6] [6: See Appendix-A, Table-8, Page#25.]

Causality Test Result lag1Dependent VariablesGDPFDIUEP

GDP-0.60620.3345

-

0.3875-0.7673

FDI

UEP0.35100.5464-

-

We accept our null hypothesis: GDP does not Granger Cause FDI and GDP does not Granger Cause UEP at lag 1 because prob. values are greater than 0.1. We accept our hypothesis: FDI does not Granger Cause GDP at Lag 1 because of the prob value that is 0.3875 and we also accept FDI does not Granger Cause UEP. Our hypothesis UEP does not Granger Cause GDP because of the prob value that is 0.3510. Hypothesis that UEP does not Granger Cause FDI has been accepted because prob value is greater than 0.1.Table 5.1.2[footnoteRef:7] [7: See Appendix-A, Table-8, Page#25.]

Causality Test Result lag3Dependent VariablesGDPFDIUEP

GDP-0.05310.9175

-

0.0019-0.9891

FDI

UEP0.53930.0260-

-

We reject our null hypothesis: GDP does not Granger Cause FDI because prob value is 0.0531 there is a significant effect of GDP on FDI at lag 3. This means that the GDP effect at the 3rd year on FDI. GDP does not Granger Cause UEP at lag 3 because prob values are greater than 0.1. We reject our hypothesis: FDI does not Granger Cause GDP at Lag 3 because of the prob value that is 0.0019 but we accept FDI does not Granger Cause UEP. Our hypothesis UEP does not Granger Cause GDP is accepted because of the prob value that is 0.5393. Hypothesis that UEP does not Granger Cause FDI has been rejected because prob value is 0.0260 i.e. less than 0.1.

Conclusion and ImplicationsThis study intend to contribute to the existing literature using time series data of Pakistan and paying due attention to the standard econometric techniques. The policy implication of this study is that, there is a significant and positive impact of FDI on GDP economic Growth while unemployment is not significantly effecting the GDP at first. The government should create positive opportunities to attract Foreign Direct Investment in the country. On the other hand the inverse relationship has been confirmed of GDP and Unemployment. A decrease in unemployment will bring economic growth. Vice versa an increase in GDP will bring about decrease in unemployment. Firm and concrete measures should be taken for utilization of labor of the country. The FDI should be implemented in the country such a way that it brings opportunity for the unemployed skilled labor and the overall productivity of the country and hence real increase in GDP can be obtained.

REFERENCES RFRENCES REFERENCIAS:1. Qaiser Abbas, S. A., Nasir, A. S., Ullah, H. A., & Muhammad, A. N. (2011). Impact of Foreign Direct Investment on Gross Domestic Product (A Case of SAARC Countries).Global Journal of Management And Business Research,11(8).2. Gudaro, Amna Muhammad, Imran Umer Chhapra, and Salman Ahmed Sheikh. "Impact of foreign direct investment on economic growth: A case study of Pakistan." (2012): 22-30.3. Haider, Zeeshan, et al. "Impact of foreign direct investment (FDI) on GDP: A Case study from Pakistan." International Letters of Social and Humanistic Sciences 05 (2014): 73-80.4. GuechHeang, Lim, and Pahlaj Moolio. "The Relationship between Gross Domestic Product and Foreign Direct Investment: The Case of Cambodia." KASBIT Journal of Management & Social Science 6 (2013): 87-99.5. Abbas, Qaiser, Salman Akbar, Ali Shan Nasir, Hafiz Aman Ullah, and Muhammad Akram Naseem. "Impact of Foreign Direct Investment on Gross Domestic Product." Global Journal of Management & Business Research 11, no. 8 (2011).6. Falki, Nuzhat. "Impact of foreign direct investment on economic growth in Pakistan." International Review of Business Research Papers 5.5 (2009): 110-120.7. Alfaro, Laura. "Foreign direct investment and growth: Does the sector matter." Harvard Business School (2003): 1-31.8. Alam, M. Sayeed, and Mahmud Zubayer. "Intra regional foreign direct investment (FDI) prospect in South Asian Association of Regional Cooperation (SAARC) region." International Journal of Economics and Finance 2.3 (2010): p114.9. Qaiser Abbas, Salman Akbar, Ali Shan Nasir, Hafiz Aman Ullah, and Akram Naseem Muhammad. "Impact of Foreign Direct Investment on Gross Domestic Product (A Case of SAARC Countries)." Global Journal of Management And Business Research 11, no. 8 (2011).10. Toda, Hiro Y., and Taku Yamamoto. "Statistical inference in vector autoregressions with possibly integrated processes." Journal of econometrics 66.1 (1995): 225-250.11. Nosheen, Misbah. "Impact of Foreign Direct Investment on Gross Domestic Product." World Applied Sciences Journal 24.10 (2013): 1358-1361.

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Impact of FDI on economy of Pakistan