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    PAKISTAN BUSINESS REVIEW JULY 2010

    Pakis tans Balance of Payments Prospects and Policy Proposals Motif

    PAKISTANS BALANCE OFPAYMENTS PROSPECTS AND

    POLICY PROPOSALS

    M. Ashraf Janjua

    Institute of Business Management, Karachi

    Analysis of current account balance of Pakistan

    As we look at the accounts on a long term basis we find

    that since FY03, the trade balance in goods is in continuous

    deficit and the deficit is ever increasing. It reached a maximum of

    US $ 14,970 million in the year FY08 mainly because of increase

    in oil prices (see next page).

    The world economic slowdown and reduction in oil

    prices brought down the trade deficit but still it remained

    unreasonably high as compared to the potential of the economy.

    The decreased deficit of the services account is mainly

    attributable to the lower payments on account of transportation

    because of lower imports, and the other major cause being the

    lower payments for other business services. If we look at the

    income account, there is an ever increasing debt burden.

    Although foreign investment both direct and portfolio isattractive investors want return on their investment to stay in

    business. Under the present circumstances it is a pipe-dream to

    produce a positive income account. The only component of a

    surplus is current transfers from workers abroad to their families.

    As a rule of thumb, when a country is in a crisis, creditors avoid

    lending any money or alternatively they also charge for the risks

    involved. Such a situation gives rise to a vicious circle where a

    country has low productivity, low exportable surplus, low

    reserves for payment, low investment from abroad, low capacity

    to borrow on soft terms and so on. In order to get out of this

    vicious circle a country has to make extraordinary efforts. With

    ever increasing current account deficit, we are facing difficulties

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    Motif

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    Pakistans Balance of Payments Prospects and Policy Proposals

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    in controlling the budgetary deficit for various reasons, and there

    is a lack of good governance. Though government has taken

    several steps on its own and also as a part of IMF conditionality

    more steps are needed to control expenditure and enhance

    government revenues.

    The main cause underlying our balance of payments

    difficulties is our inability to increase value addition to the raw

    materials we export. The main underlying reasons are the high

    cost of energy for the production of tradables, poor infrastructure,

    and low capital investment in the modernization of the machinery

    and equipment to generate internationally competitive products.

    Heavy investment, strategic planning and above all the

    determination of the government and the nation are required for

    gaining a competitive edge in the global markets in order to achievea trade surplus. It is worth mentioning that due to certain factors,

    like low production, low quality, income inelastic demand for our

    products and weak image of the Pakistani traders in the global

    market, Pakistan has not been able to utilize its quota in the United

    States and the European Union (average quota realization has

    been around 70 %)in the less liberal scenario of the past1. We

    need to explore the area of services where we can perform better

    with relatively little investment and easily acquire competitive

    advantage over our rivals.

    1WTO Regime and Its Impact on Pakistan, Syndicate No. 1o,

    Civil Services Academy, Lahore, 31stCommon Training Programme

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    PAKISTAN BUSINESS REVIEW JULY 2010

    Pakistans Balance of Payments Prospects and Policy Proposals

    Services are the fastest growing sector in the global

    economy, constituting more than 60% of GDP of many countries.

    Services are the largest and most dynamic component of both

    developed and developing country economies. Services

    currently account for over 60 percent of global production and

    employment. Services, such as telecommunications, banking,

    insurance and transportation are strategically important for

    enhancing overall economic efficiency, performance and growth.

    With services liberalization we may access quality service

    providers and as a labor-abundant country we can develop

    capabilities to capitalize our human resource with massive training

    and development programs and capacity building initiatives. This

    would generate savings, faster innovations enhance

    transparency and predictability with technology transfer and

    optimum utilization of the work force.

    The following factors seem to be affecting our exports:

    i. Law and order and war on terror affecting, among

    other things, the inflow of foreign direct investment

    (FDI)

    ii. Power shortage also affecting investment flows, both

    foreign and domestic.

    iii. Erosion of competitiveness because of the increase in

    unit prices of imports used as inputs for exportables.

    iv. On the demand side constraints are.

    a. Recession in the world economy althoughnow there are some indications of an upturn.

    b. Dependence on raw material exports with low

    value added.

    c. A number of other countries have competitive

    edge in case of a number of commodities and

    services.

    d . We have not made any visible progress in

    diversification of our exports mix.

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    Around 30-35% of our imports are composed of crude/

    furnace oil. We have to spend substantial amount on import of

    edible oil, chemicals and chemical products for manufacturing

    and agriculture, import of fertilizers, sugar and even wheat.

    Workers remittances are a big part of net transfers and

    it is this component that is largely supporting the balance of

    payments along with other private and official transfers. As our

    economy has the tendency of an increasing current account deficit,

    we are facing difficulties in financing this deficit. War on terror

    and expenditure on law and order is increasing our budget deficit

    and building pressures on our external resources because we are

    not generating enough public revenue. As a result the cost of

    borrowing is increasing; FDI inflows are drying up and exerting

    further pressures on cost of external financing. Our reserves havemainly been built on borrowing from the IMF.

    Policy Proposals

    Balance of payments problems may be resolved by

    taking the following steps:

    i. Efforts should be made to restore law and order and

    conclude the countrys war on terrorism. Both these

    factors are expected to pave the way for increase in

    production and exports. Also, these improvements

    should have salutary effects on FDI and tourism inflows.

    ii. Increase in FDI should lead to inflow of advancedtechnology, expansion in services and growth in

    production

    iii. Inflow of FDI should also lead to capacity building in

    human resource sectors with healthy effects on

    production. Even if skilled people move abroad, that

    should lead to expansion in home remittances.

    iv. In addition to improvement in law and order, visible

    progress should be ma de in good governance:

    part icularly transparent and timely decision making,

    monitoring of efficient implementation and setting up

    effective accountability mechanisms.

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    Pakistans Balance of Payments Prospects and Policy Proposals

    v. Control of law and order situation will give a boost to

    our stock exchange and consequently lead to an

    increase in foreign portfolio investment.

    vi. The quality of social and physical infrastructure should

    be improved

    vii. Alternative energy resources should be developed,

    leading to enhanced production including exportable

    surplus with increase in competitiveness

    viii. There should be improvement in macroeconomic

    stability, particularly the containment of inflation to a

    modest level and keeping the Pakistan Rupee

    competitive in the international market (in terms of

    REER)

    ix. Serious efforts are needed to diversify our exports withemphasis on the services sector, dairy products, fruitand vegetables and labor intensive segments of smallscale industry.

    x. In addition to diversification of exports there is a need

    to revisit the direction of trade and exploiting untapped

    markets (including those in Africa and Latin America)

    The long term sustainable level of the balance of

    payments deficit depends on two fundamental variables: (1) the

    ratio of foreign savings to investment and (2) growth in foreign

    exchange earnings from exports of goods and services, workers

    remittances and other private transfers. Depending on these two

    variables, sustainable annual current account balance of

    payments deficit could fall anywhere in the range of 2-3% ofGDP. Hence, there is need for developing guidelines and a

    framework which will keep the current account balance of

    payments deficit at sustainable levels taking into account the

    gap between savings and investment and the growth in foreign

    exchange earnings. Consideration may be given to the following

    guidelines:

    Establish a ceiling for the share of foreign savings in

    total investment to ensure that large balance of

    payments deficits do not finance consumption and that

    the country doesnt become over-reliant on external

    financing. Various empirical studies suggest that foreign

    228

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    Pakistans Balance of Payments Prospects and Policy Proposals

    Investment (BOI) needs to gear up its efforts to attract

    direct foreign investment in export related industries and

    in the energy sectors.

    It seems that competitive pressures for Pakistans textile

    and clothing exports (64% of exports) arising from the

    phasing out of the Multifibre Arrangement (MFA) were

    not anticipated fully and new investments on the scale

    required to move up the value chain have not been

    forthcoming. The textile industry, thus, needs to adjust

    to the realities of the world market and restore its

    competitiveness through enhancing productivity. The

    structural problems characterizing Pakistans textile and

    clothing sector cannot be solved without major

    investments in both plant and equipment and human

    skills, investments in there areas are not taking place at

    the required scale.

    The government has already met the textile industry at

    least half way largely through credit subsidies. The textile

    industry needs to adjust to the realities of the world

    market and must restore its competitiveness mainly

    through enhancing productivity. Conceptually, any

    subsidy in support of textile exports where international

    prices are fall ing is not a good option. The fact that

    Pakistani textile exports are dominated by cloth and yarn

    while etc in the world clothing imports that are

    expanding faster is a handicap which cannot be easilyaddressed by further cash subsidies.

    Pakistans export sector, in general, is faced with

    structural constraints e.g. extremely narrow export base,

    low unit value exportables, lack of competitiveness, lax

    quality control etc. Hence, any future export growth

    strategy will have to be premised on structural solutions.

    Any cosmetic measures to boost exports might yield

    some marginal increases but would in no way address

    the root causes of the problems afflicting the export

    sector.

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    Pakis tans Balance of Payments Prospects and Policy Proposals Motif

    The government needs to carefully examine the

    adequacy of the existing incentive regime for Pakistans

    exports.

    The government also needs to review the overall

    production st ructure of the country to de termine

    whether the existing tariff regime encourages

    production for domestic consumption or for exports.

    There is also a dire need to re-orientate our exports

    strategy from the goods sector to services exports.

    Services are a fast growing sector of the Pakistan

    economy and their export potential needs to be tapped

    through seeking enhanced market access.

    The government should also assess the efficacy of

    subsidies as a tool of export promotion and in this

    regard an analysis ought to be made of the impact of

    R&D support being provided to textile garments. The

    evidence so far leads to the conclusion that financial

    support has not helped expand value of textile exports.

    For the long run an ambitious program of increasing

    Pakistans market share in world trade which at present

    is a paltry 0.15% is needed, but this cannot be achieved

    without massive efforts to diversify exports and make

    export development a central plank of our policy. Despitethe fact that Pakistan has liberalized its trade

    substantially since the late 1980s (trade GDP ratio

    changed to 0.5 during liberalized period from 0.1 during

    the pre-liberalized period) it shows a much lower

    response to trade liberalization in terms of exports

    growth compared to its competitors and regional

    counterparts (India; 0.4%; China 1.3%; Bangladesh

    0.6%; Sri Lanka 0.3%; Malaysia 2.7%; Turkey 0.8% and

    Iran 0.1%).

    Policy attention needs to be focused on non-textile

    manufactured exports, the new promising areas of IT

    231

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    PAKISTAN BUSINESS REVIEW JULY 2010

    Pakistans Balance of Payments Prospects and Policy Proposals

    exports and agricultural and livestock products areas

    where Pakistans world presence is minimum.

    In order to broaden the export base, Government policy

    must specially target foreign investment in

    manufacturing, aimed both at improving technology and

    productivity of promising export sectors.

    A cabinet level committee to be chaired by the Prime

    Minister could help improve much needed policy

    coordination and implementation of export and industrial

    policies as well as close monitoring of exports.

    Import of goods management with competitive

    alternatives, exports oriented imports, rails, roadsfacilities and other transport facilities, reducing wear

    and tear leading to save imports on parts, domestic

    savings and lower dependency on external resources

    Imports of services management by developing our HR

    capacity for earning of foreign exchange through

    various modes of supply of services or otherwise

    through migration of HR and earning transfers through

    deploying our resources on more remunerative jobs.

    We need to increase domestic savings and investment

    and deploy our resources for financing a competitive

    debt and equity mix so that we move to sustainability inthe medium term

    Government policies need to be adjusted to ensure social

    safety nets for the poor to protect the welfare of those

    who are likely to be displaced in the transition period by

    the process of trade liberalization and globalization more

    generally.

    Skill development and training schemes must be

    instituted so that any displaced workers can be quickly

    retained, relocated and reabsorbed in the labor force. It

    is important to keep inflation in check in order not to

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    Pakis tans Balance of Payments Prospects and Policy Proposals Motif

    lose the gains from trade liberalization and other policy

    reforms that have been undertaken.

    Reduced tariffs, particularly on imported raw material,

    imports, components and machinery can help boost

    exports. However, other complementary policies are also

    required. Meanwhile, with capacity constraints being

    reached in the economy and inflation remaining high,

    overestimating potential growth of the economy in the

    short term runs the risk of letting demand grow at a rate

    that cannot be sustained, which would make it difficult

    to contain inflation.

    Policy coordination on exports needs substantial

    improvement. The responsibilities for export promotionare very dispersed among many agencies, including the

    Ministry of Commerce, the Trade Development Authority

    (a very good idea), the Ministry of Industry, the Textiles

    Ministry, etc. A cabinet-level committee could help by

    closely monitoring exports and speedily resolving policy

    and implementation issues.

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    PAKISTAN BUSINESS REVIEW JULY 2010

    ResearchProcess Capability Analysis for Non Normal Data

    PROCESS CAPABILITY

    ANALYSIS FOR NON

    NORMAL DATA

    Ejaz Ahmed

    College of Computer Science andInformation Systems, Karachi

    Suboohi Safdar,

    Department of Statistics

    University of Karachi, Karachi

    234

    Research

    Abstract

    Process capability analysis refers to the normal behavior of a

    process when operating in a state of statistical control. Drives to

    continuous improvement are usually associated with the processcapability measures. Typically we assume that the processes

    follows normal probability distribution ensuring a high

    percentage of the process measurements falling between 3of the process mean and the total spread amounts to about 6

    variations. This article describes the estimation of pkp CanC d ,

    commonly used process capability indices (PCI), in case of non-

    normal data using the characteristics of Weibull distribution.

    Earlier work of Lovelace and Swain (2009) has been extended for

    this distributional assumption. Quantiles are estimated by

    probability plotting technique and then control limits are obtained

    to determine whether the process is in statistical control or not.Percentage points of the fitted distribution have been used to

    compute under the assumption of Weibull distr ibution. We have

    used Delta method (Stuart and Ord, 1987) to estimate parameters

    and their standard errors. These estimated parameters are then

    used to develop new PCIs. Average PCI values are given along

    with the standard errors.

    Keywords: Process capability, Estimation, Non normal Data,

    Weibull Distribution

    JEL Classification: C1160

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    Process Capability Analysis for Non Normal Data

    1. Introduction

    Process capability refers to the inherent ability of a

    process to produce homogeneous parts for a sustained time

    period under given conditions. Kane (1986) described six areas

    of applications of capability indices that include presentation of

    non conforming products, measuring continuous improvement,

    communication, prioritization, identifying direction for

    improvement and auditing a quality system. Deleryd, et. al. (1999)

    identified six critical factors for successful implementation of

    process capabi lity studies. Tsim (1997) identi fied four key

    objectives of PCI including, among others the ability to compare

    different processes (unit less measure) and to identify the

    closeness to target (Taguchi Loss Function concept).

    Process capability measures the variability of a process

    relative to its specification limit based on three assumptions,

    namely, (i) the process is itself in control, (ii) target value and

    specifications of a quality characteristics are specified, and (iii)

    the process measures quality characteristics that follow a normal

    distribution.

    2. Process Capability Indices- Review

    The most commonly used PCI, named pC Index,

    measures the potential process performance (process

    consistency) which only reflects the consistency of the productquality characteristic. pC measures process spread related to

    specification limits and hence the location of process mean is

    not considered. Assuming a quality characteristic to follow

    ( )2,N with upper and lower specification limits USL andLSL, this measure is defined as ( ) .6/ LSLUSLCp = The

    expected proportion of nonconforming products assuming

    ( )2/)USLLSL+= can be obtained as

    235

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    ResearchProcess Capability Analysis for Non Normal Data

    Where ( ). denotes standard normal cumulative distributionfunction and 2/)( LSLUSLd

    = . Constable and Hobbs

    (1992) defined capable as percentage of output within

    specification while Montgomery (2001) recommended minimum

    Cp equals to 1.33, for an existing process, and 1.50 for a new

    process. Small values of Cpare bad sign but large values do not

    guarantee of acceptability in the absence of information about

    the values of the process mean.

    As is obvious pC depends on the true standard

    deviation (SD) of the process which is usually unknown. It

    therefore forces to use an estimator of the SD which then results

    in estimated PCI, based upon sample observations. The estimatedPCI given as is evaluated using

    suitable unbiased estimator of , such as (Montgomery,

    2001). An important ratio is quite frequently used as it

    yields the statistics. This relationship

    is useful in constructing confidence intervals or testing

    hypotheses. Kane (1986) introduced another PCI known as

    p kC which depends on both mean and standard deviation of the

    236

    ( ) 6/ LSLUSLCp = 2dR

    pp CC

    ( ) pnp CnC

    =

    211

    ( ) ( ) ( )

    ( )

    [ ]

    d

    USLLSLLS L

    LSLX

    USLZpLSLZ

    USLXpLSLXpE

    =

    =

    +=

    + +

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    PAKISTAN BUSINESS REVIEW JULY 2010

    Process Capability Analysis for Non Normal Data

    process to deal with violation of centering assumptions and to

    measure the actual capability performance. Mathematically this index

    is described as .

    Obviously, . Under

    the assumption of normality exact confidence interval

    for involves the joint distribution of two random variables

    following non-central t-distribution. Nagata and Nagahata (1992)

    showed that approximate confidence interval

    for is given by,

    These measures are unit less and permit comparison amongst

    hundreds of process emanating from a whole range of productionprocesses and indust ries . However the methodology and

    inferences about the process capability indices do not remain

    too straight forward in the absence of normality assumption.

    Next sections of this article will discuss the PCIs when the

    underlying distribution is skewed.

    3. Case under Non-Normality

    Numerous authors have discussed the construction and

    interpretation of PCIs under non-normal process behavior. Chen

    at al. (1988) proposed PCIs with distribution free tolerance

    intervals to estimate while Clement (1989) and McCormacket. al. (2000) proposed empirical non-normal percentiles to

    evaluate both Cp

    and Cpk

    . Lovelace and Swain (2009) discussed

    the construction of Cp

    and Cpk

    assuming process behavior

    following a Log-Normal distribution. They used 99.875th and

    0.135thquantiles to estimate both PCIs. Their proposed capability

    indices are given below,

    237

    ( ) ( ){ } 3/,3/ LSLUSLMinCpk =( )( ){ } ppk CdUSLLSLdC /2/+=

    pkC

    ( ) %1001 pkC

    )1(2

    9

    12/

    2

    +n

    C

    npkpk

    ZC

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    ResearchProcess Capability Analysis for Non Normal Data

    Chen and Chen (2004) compared these two process

    capability measures using four approximating methods, including

    bootstrap estimators. Since skewed distributions are not too

    common in production or service industries comparatively smaller

    proportion of literature is devoted to address PCIs in non-normal

    behaviors. Lovelace and Swain (2009) discussed both capability

    indices assuming process data following a Log-Normal

    distribution. Pal (2005) assumed process distribution to follow

    Generalized Lambda distribution and evaluated PCIs. We intend

    to discuss the construction of PCIs when the process distribution

    follows a Weibull distribution. Interested readers are recommended

    to refer to Munechika (1992), Pyzdek (1992), Kotz and Johnson

    (1993), Somerville and Montgomery (1996), and Pal (2005) for

    further details.

    4. Process Capability Index- Weibull Distribution Case

    Weibull distribution was originally proposed to describe

    data from life testing commonly used in reliability, fatigue and

    survivor analysis. Weibull distribution is a very important

    distribution and has been widely used in studies related to, for

    example, earthquakes, flood, breaking strengths, and reliability

    under censored or truncated situations. However parameter

    estimation is not easy especially in case of three parameter Weibull

    distribution. Quantiles are recommended to be used while

    estimating parameters. In this article we used delta method (Stuart

    and Ord, 1987) to find the standard error of the estimates. Later

    these estimated standard errors are used to construct our proposed

    process capability index.

    238

    { }

    00135.0

    99865.0

    00135.099865.0

    .

    XMedian

    LSLMedianC

    MedianX

    MedianUSLC

    where

    CCMinCXX

    LSLUSLC

    pl

    pu

    plpupk

    p

    =

    =

    =

    =

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    Process Capability Analysis for Non Normal Data

    239

    The three-parameter Weibull distribution probability

    density function is given by

    parameter Weibul l di stribution by assuming the location

    parameter equal to zero. Many distributions are special cases

    of Weibull distribution, for example exponential distribution is a

    transformed form of Weibull distribution with shape parameter

    .

    We consider the two-parameter Weibull distribution with

    probability function given as ,where x>0. A linear regression model was developed using the

    cumulative distribution function, as described below.

    The last expression (equation 2) is equivalent to

    simple linear regression model, , where

    Least square estimators of both parameters were determined andstandard errors were obtained using the Delta method (Stuart

    and Ord, 1987) as given below:

    ( )

    =

    xxxf exp

    1

    where ,>x 0,0,0 >>> . We may derive the two-

    1=

    ( ) ( )[ ]( ) ( ) ( )( ){ }[ ]

    ( ) ( ) ( )( ){ }[ ]

    xFx

    xFx

    xxF

    +=

    =

    =

    1lnlnlnln

    1lnlnlnln

    exp1

    ezy ++= 10 ( ) ( ) ( )( ){ }[ ]xFxy ==== 1lnlnzand,1,ln,ln 10

    ( ) [ ] ( ) [ ]( )

    ( ) ( ) ( ) ( )( )

    )4(11111

    )3()exp()exp()exp()(

    2

    2

    4

    1

    1

    4

    1

    1

    2

    2

    1

    1

    2

    1

    2

    2

    2

    2

    00

    2

    00

    2

    0

    =

    =

    =

    =

    ==

    =

    yyVVVV

    yyn

    yVVV

    i

    i

    i

    ......2

    ( ) ( )[ ]

    xxF = exp1

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    Process Capability Analysis for Non Normal Data

    241

    Table 1: Table showing PCIs based on Weibull Parameters

    Alpha Beta se(Alpha) se(Beta) Cp(Alpha) Cp(Beta)

    2 3 Mean 1.678 81.970 0.140 0.001 1.225 279.682

    SE 0.141 214.749 0.024 0.001 0.219 125.525

    3 3 Mean 2.417 144.614 0.327 0.000 0.761 69807.179

    SE 0.239 248.027 0.458 0.000 0.208 254108.036

    4 3 Mean 3.287 250.767 0.749 0.000 0.551 104157.087

    SE 0.356 753.433 0.990 0.000 0.374 394228.414

    5 3 Mean 3.375 104.995 0.875 0.000 0.451 14053.322

    SE 0.315 305.475 1.021 0.001 0.251 53594.197

    Control charts for with 6 variations can be easily plotted

    constructing ( )[ ] 3 seUCL += and

    ( )[ ] 3 seLCL = . Table 1 reveals that the estimated

    values for are highly deviated from the original parametric

    values. The proposed method is recommended when the

    process behavior follows a Weibull distribution and the

    characteristic of interest is .

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    ResearchProcess Capability Analysis for Non Normal Data

    242

    6. References

    1. Chan, Lk., Cheng, S. W., and Spiring, F. A. (1988). A New

    Measure of Process Quality: pmC . Journal of Quality

    Technology, 20, 162-175.

    2. Chen, J . P. and Chen, K. S. (2004). Comparing the

    capability of two processes using Cpm. Journal of Quality

    Technology, 36, 329-335.

    3. Clements JA (1989) Process Capability Calculations for

    non normal distributions. Quality Progress, 95-100

    4. Constable, G. K and Hobbs, J. R, (1992), Small Samplesand Non Normal Capability, Trans, ASQCQuality

    Congress 1-7.

    5. Deleryd, M., Deltin, J. and Klefsjo, B. (1999). Critical

    factors for successful implementation of process

    capability studies. Quality Management Journal, 6, 40-59.

    6. Kane, V. E. (1986), Process Capability Indices,Journal

    of Quality Technology, 18, 41 -52.

    7. Kotz, S. and Johnson, N. L. (1993),Process Capabil ity

    Indices, London: Chapman & Hall.

    8. Kotz, S. and Johnson, N. L. (2002). Process Capability

    IndicesA Review, 19922000. Journal of Quality

    technology, 34, 2-19.

    9. Lovelace, C. R. and Swain, J. J. (2009). Process capability

    analysis methodology for zero bound, non-normal

    process data. Quality Engineering, 21, 190-202.

    10. McCormark, D. W. Jr., Harris, I. R., Hurwitz, A. M. and

    Spagon, P. D. (2000). Capability indices for non normal

    Data.Quality Engineering, 12, 489-495

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    CROSS VALIDATION OF

    RYFF SCALES OFPSYCHOLOGICAL WELL-

    BEING: TRANSLATION INTO

    URDU LANGUAGE

    Sadia Aziz Ansari

    Department of Social ScienceCollege of Business Management, Karachi

    244

    Abstract

    The present study aimed to investigate the structural validity ofan Urdu translation of 54- items Ryff scales of psychological

    well-being including; (six sub-scales: self-acceptance, positive

    relations, autonomy, environmental mastery, personal growth

    and purpose in life). Analyses were based on data from 261 men

    and women, with a mean age of 25.64 yrs between 1860 years.

    The calculated internal item correlation coefficients of the

    translated scales were Cronnbachs alpha= (0.853), and

    standardized item alpha= (0.855) significantly higher than the

    original Ryff scales. Besides confirming previously reported

    findings correlation among six subscales range between (r= 0.57

    to 0.70). The present findings demonstrate the adequacy of the

    Urdu version of the Ryff scales as instrument for assessing

    psychological well-being among males and females in Karachi.

    Keywords: Psychological well-being; Ryff scales; Urdu

    translation.

    JEL Classification: Z0000

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    However, clarifying the structure of well-being has

    neither been easy nor straightforward as evidenced by the

    multitude of conceptual approaches that exist. For instance,

    Bradburn (1969) proposed that SWB is composed of two

    components, whereas others state that SWB consists of either5five components, 6six components or 7seven components.

    Despite the lack of agreement about the number of

    dimensions that compose SWB, a number of investigators agree

    that SWB contains a cognitive and an affective component.8The

    affective component is best understood as a hedonic balance

    constituting ones overall emotional tone determined by an

    individuals level of positive and negative affect and the difference

    between these emotional states (Bradburn, 1969). Further, hedonic

    balance is the conceptual basis for the most well known instrument

    that measures the affective dimension of well-being9

    The basis of the cognitive component of SWB emerged

    from studies examining adaptation to gain and recognise the

    contentment, or life satisfaction, approach. The logic behind this

    approach is that if one has a favorable evaluation in many life

    domains, such an evaluation will lead to an overall positive outlook

    on ones life and the experience of higher levels of SWB.

    3affectivepart, refers to both the presence of positive affect (PA)

    and the absence of negative affect (NA)4cognitive part is an information-based appraisal of ones life.5(Lawton, 1975)6(Neugarten et al., 1961; Ryff, 1989)7(Reker and Peacock, 1981)8

    8(Andrews and Withey, 1976; Diener, 1984; Diener and Emmons,

    1984; Liang, 19848, 1985; Lucas et al., 1996; Stock et al., 1986).9Affect Balance Scale (ABS; Bradburn, 1969).

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    Ryff s Scales of Psychologi cal Well-being

    According to Ryff (1989), well-being is not composed

    simply of positive affect, negative affect, and life satisfaction;

    rather, well-being is best conceived as a multidimensional

    construct made up of life attitudes. Based on tenets of humanistic

    psycho logy, wi th such cons truc ts as pu rpose in li fe and

    autonomy, Ryff centers attention on normative criteria for mental

    health. The result is a means for assessing a persons level of

    positive functioning and psychological well-being. Ryff (1989)

    created the Scales of Psychological Well-Being (SPWB) based

    on an integration of mental health, clinical, and life span

    developmental theories. These dimensions are assumed to

    measure all aspects of wellbeing and include self-acceptance10,

    positive relations with others

    11

    , autonomy

    12

    , environmentalmastery13, purpose in life 14, and personal growth15Ryff, 1989).

    Ryff and Keyes (1995) examined the structure of Ryffs six factor

    model using Structural Equation Modeling. The model that best

    fitted the data was one of six primary factors joined together by

    a single higher order factor defined as well-being. Ryff (1989)

    also performed factor analysis on the six subscales of the SPWB

    and found highest factor correlation between self-acceptance

    and environmental mastery (0.76), self acceptance and purpose

    in life (0.72).

    10Self-Acceptance = positive evaluations of oneself11Positive Relations with others = quality relations with others12Autonomy = sense of self- determination13Environmental Mastery = capacity to effectively manage ones

    life and surrounding world14Purpose in Life = belief in a purposeful and meaningful life15Personal Growth = sense of continued growth and development

    as a person

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    Subscales loaded on a general well-being factor with

    the remaining four subscales loading on two other factors.

    Specifically, personal growth, positive relations with others and

    purpose in life loaded on a factor believed to represent new

    dimensions of well-being with control (i.e., powerful others,

    chance).

    Central to this discussion Kozma et al. (1991) reported

    that it is important to establish the construct validity of a measure

    by examining the extent to which the presumed components

    emerge in factor-analytic studies. It is also imperative that the

    items making up a measure load on the appropriate factors.

    Establishing the construct validity of a measure is one way to

    establish the usefulness of a scale. Therefore, the primary aim of

    this study is to establish the construct validity of the Ryffs SPWB54-item (Urdu version).

    I t i s hypothesis that an Urdu version of SPWB would

    validate the construct vali dity of Ryff scale.

    Extensive research exists on the correlation of

    demographic and other environmental factors with happiness.

    These findings started with Cantrils (1965) study of 23,875 people

    in 11 countries, the research of Bradburn (1969) and Campbell,

    Converse, and Rodgers (1976) in the United States, and Ingleharts

    (1990) analysis of Eurobarometer studies of 16 countries with

    over 163,000 respondents. Veenhoven and colleagues (1994) later

    reviewed 603 such studies from 69 countries. It is concluded that

    demographic and environmental factors affect happiness at

    varying levels. Thus, fur ther thi s study will provide in sight on

    subjective psychological well -bein g in Pak istani context.

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    Methodology

    Ryff Scale of Psychological Well-being (RSPWB)

    TheRyff Scale of Psychological Well-Being (RSPWB )

    54-item, is a theoretically-grounded instrument that focuses on

    measuring six dimensions of psychological well-being: self-

    acceptance, personal growth, purpose in life, positive relations

    with others, environmental mastery, and autonomy (Ryff, 1989).

    Each dimensional scale contains 9 items equally split between

    positive and negative items. Items are scored on a 6-point scale

    ranging from strongly agree to strongly disagree.

    Ryffs scales have been found to correlate positively

    with prior measures of well-being, such as the

    16

    Affect BalanceScale and the 17Life Satisfaction Index. However, it is negatively

    correlated with measures of depression like 18Zungs Depression

    Scale. Internal consistency coefficients (alpha) for 19Ryffs six

    sub scales range from (0.82 to 0.90).

    Translation of Ryff Scale into Urdu

    Dr.Carol Ryff, consented the author to translate the scale

    into Urdu. Translation from English to Urdu and cultural

    adaptation of scale was performed in two steps. The scale was

    first translated by a bilingual expert working as assistant

    professor; Communication at Institute of Business Management.

    In the second step scale was examined by a Native Languageexpert working as lecturer at University of Karachi to avoid

    syntactic errors in translation.

    16(Bradburn, 1969)17(Neugarten et al., 1961)18Zungs (1965)19(Schmutte and Ryff, 1997).

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    Sample:

    Participants were university students, un/married, un/

    employed individuals in the vicinity of Karachi city. Three hundred

    participants completed questionnaires for the study. .Thirty nine

    questionnaires were excluded for data analysis due to incomplete

    responses. Two hundred sixty-one research participants made

    up the final sample. The sample contained 111 males and 150

    females, whose mean age was 25.43 yrs (SD = 3.76; range: 1848

    years) and mean education of 14.22 years (SD = 1.37; range: 10

    19 years).

    Data Collection:

    All respondents were asked to fill the RSPWB 54-item

    Urdu Version questionnaire either in groups or individually.

    Group administration of RSPWB 54-item was undertaken

    at the Institute of Business Management (IoBM), Karachi

    Foundation School (KFS) and Meezan Bank. 120 students from

    the Freshman Introductory Psychology class at IoBM, 34 teachers

    and management staff at KFS and 45 employees from Meezan

    Bank were conveniently selected at random to complete the

    questionnaire. 68 questionnaires were dispatched to postal

    addresses in the vicinity of Karachi and the author received 24

    completed questionnaires. The rest of the data was collected bydistributing the questionnaire in the locality of Karachi city.

    Results

    Demographic profiles of participant suggest that sample

    was largely comprised of (73.3%) unmarried and (25.6%) married

    individual from general population, with average age of 25.43yrs

    between (18-60 years).A large group of respondent were between

    17-25yrs of age (38.3%) while others were (19.1%). Educational

    qualification of majority of research participant were grouped

    into under graduation (Intermediate=28%,A-levels=13%) and (

    Masters=31% ).On the other hand, the rest of the participant

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    were graduate (20%), professionals such as doctors, Engineers

    (05%) and matric (03%). The gender profile of research participant

    showed a bit larger female composition (57.6%) than male (42.4%).

    The construct validity of RSPWB 54-item was tested

    against RSPWBs Urdu Version. Estimated internal item

    correlation coefficients of the translated scales were

    (Cronnbachs alpha (=0.853), and standardized item alpha=

    (=0.855) see table 1. Inter-correlation among six subscales range

    between (r=0.57 to 0.70) correspondingly: autonomy and

    environment mastery (r=0.54), autonomy and personal growth

    (r=0.64), autonomy and positive relation (r=0.45), autonomy and

    purpose in life (r=0.34), autonomy and self acceptance (r=0.53),

    environmental mastery and personal growth (r=0.58),

    environmental mastery and positive relations (r=0.54),environmental mastery and purpose in life(r=0.47), environmental

    mastery and self acceptance (r=0.46), positive growth and positive

    relations(r=0.55), positive growth and purpose in life (r=0.42),

    positive growth and self acceptance (r=0.46), positive relations

    and purpose in life (r=0.48), positive relations and self acceptance

    (r=0.38), purpose in life with self acceptance (r=0.51) were

    significant at 0.01 level (see table 3). Obtained value suggests

    that probability of individual item responses on six subscales

    were consistently same with the total responses (See Table 2).

    Considerably low inter correlations depicted among

    subscale of Autonomy and purpose in life scale20and positive

    relations and self acceptance scale 21.This is contrary to Ryffssix factor model for subjective psychological well-being. It

    remains debatable; Do the items intended to measure each

    theoretical domain? 54-items are enough to measure subjective

    psychological well-being? Is their an overlap of items pertaining

    to more than one domain? This may recommend that Subjective

    Psychological well-being in Pakistani context comprises of less

    than six subscales.

    20Autonomy and purpose in life scale (r=0.34)21Positive relations and self acceptance scale (r=0.38)

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    However considerably strong inter correlation among

    subscales appeared between autonomy and personal growth

    (r=0.64), and personal growth and environmental mastery (r=0.58).

    These validate Ryffs theory of six factor psychological well-

    being measure. Psychological well-being is greater, as majority of

    respondent were unmarried undergraduates between 17-25yrs of

    age.22 This also suggests dominate of factors that determine

    psychological well-being in Pakistani context.

    Ryffs theory of PWB equates autonomy with self-

    determination, independence, internal locus of control,

    individuation, and internal regulation of behavior. While other

    authors assumed that autonomy is related to the western concept

    of liberty and freedom. However present study suggests that the

    attribute of autonomy also exist in non-western cultures. Such as

    item 35 on RSPWB I have confi dence in my opini ons, even i f

    they are contrar y to the general consensus reveals higher

    consistency than other individual items on the subscale of

    autonomy (r=0.46).

    Likewise, it is reported previously that personal growth

    is the ability to grow and expend as a person who is considered

    as sense of individuals well-being rather than moral imperative.23

    Even though inter correlation among all six subscales:

    autonomy, environmental mastery, personal growth, purpose in

    life, positive relations and self acceptance with total subjective

    psychological well-being was strongly larger than the individual

    items on six subscale. Inter correlation range between (r=0.70 to

    r=0.79).22A Small but significant correlation between education and SWB

    is indicated (Campbell et al., 1976; Cantril, 1965; Diener et al.,

    1993). In a meta-analysis of the literature, Witter, Okun, Stock,

    and Haring (1984) observed a median effect size of .13.This effect

    size was similar to educations influence upon life satisfaction

    (.15), morale (.15), and quality of life (.12). Education correlates

    with well-being moreso for individuals with lower incomes

    (Campbell, 1981; Diener et al., 1993), and in poor countries

    (Veenhoven, 1994a);23 (Bellah et al. 1985, p.47)

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    References

    Andrew, F.M and S.B Withey : 1976, Social Indicators of Well-being (Plenum Press , New York).

    Bradburn, N.M 1969,The Structure of the Psychological Well-

    being (Aldine Publisher Co, Chicago)

    Diener, E. and R.A. Emmons: 1984, The independence of positive

    and negative affect, Journal of personality and social psychology

    47, pp. 1105-1117.

    Diener , E, R.A., Emmons, R.J.Larsen, and S.Griffin:1985, The

    Satisfaction with Life Scale, Journal of personality Assessment

    49,pp.71-75.

    Diener ,E,Suh, E.M, Lucas, R.E & Smith,H.L. (1999) Subjective

    well-being: Three decades of progress. Psychological Bulletin,

    125(2),276-302. Doi: 10.1037/0033-2909.125.2.276.

    Liang J:1984, Dimensions of the life satisfaction Index A:A

    structural Formulation, journal of Gerontology 39,pp. 613-622.

    Lucas, R.E, E.Diener and E.Suh:1996, Discriminant validity of life

    satisfaction, Journal of Personality and Socail Psychology 71,

    pp.616-628

    Neugarten,B.,R.Havighurst and S.Tobin:1961, The measurement

    of life satisfaction, Journal of Gerontology 16,pp.134-143.

    Reker, G.T. and E.J.Peacock:1981, The life Attitude Profile (LAP):

    A multidiemensional Instrument for assessing attitude towards

    life Canadian Journal of Behavioral Sciences 13, pp.264-273.

    Ryff, C.D. 1989, Happiness is everything or is it? Explorations

    on the meaning of psychological well-being, Journal of Personality

    and Socail Psychology 57, pp.1069-1081.

    Ryff, C.D. 1989, Beyond Ponce de Leon and life satisfaction:

    New directions in quest of successful aging International Journal

    of Behavioral Development, 12, pp.35-55.

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    Schmutte, P.S. and C.D. Ryff: 1997, Personality and well-being:

    Reexamining methods and meanings Journal of Personality and

    Socail Psychology 73, pp.549-559.

    Stock,W.A.,M.A.Okun and M.Benin: 1986, Structure of

    Subjective well-being among the elderly Psychology and

    Aging 1, pp.91-102.

    Correlations among six factor Model of Ryffs Psychological

    Well-being Scale (54-item)

    Table 1: Item analysis Statistics SPSS out put

    Cronbach's

    Alpha

    Cronbach's

    Alpha Based onStandardized Items

    N of

    Items

    .853 .855 6

    Table 2: Descriptive Statistics for Ryffs

    Psychological well-being Six Subscales

    Scales

    Mean

    Std.

    Deviation N

    Autonomy

    37.72 6.022 261

    *E.Master

    y

    37.13 6.334 26

    1

    **P.Grow

    th

    32.19 5.502 26

    1

    ***P.Rela

    tion

    35.49 6.241 26

    1

    ****P.I.L

    ife

    32.07 6.514 26

    1

    *****S.Accept

    35.54 5.508 261

    Note: (Ryffs Psychological Well-being Scale 54-item 6 factorModel (Autonomy,*E. Mastery =Environmental Mastery,

    **P.Growth= Personal Growth, ***P.Relation=Positive Relation,

    ****P.I.Life= Purpose in Life, *****S.Accept= Self Acceptance)

    (N=261, Age= 25.64yrs )

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    Table 3: Inter-Item Correlation among Ryffs Psychological well-being

    Subscales

    Scales Autonomy E.Mastery P.Growth P.Relation P.I.Life S.Accept TPWB

    Autonomy 1.000 0.545 0.648 0.454 0.349 0.534 0.73

    E.Mastery 0.545 1.000 0.584 0.549 0.478 0.469 0.77

    P.Growth 0.648 0.584 1.000 0.557 0.429 0.460 0.79

    P.Relation 0.454 0.549 0.557 1.000 0.485 0.382 0.73

    P.I.Life 0.349 0.478 0.429 0.485 1.000 0.512 0.70

    S.Accept 0.534 0.469 0.460 0.382 0.512 1.000 0.71

    TPWB 0.73 0.77 0.79 0.73 0.70 0.71 1.00

    Note: TPWB= Total Psychological well-being (all correlations are significant at 0.01 level)

    Table 4: Inter-Item Covariance among Ryffs Psychological well-being

    Subscales

    Scales Autonomy E.Mastery P.Growth P.Relation P.I.Life S.Accept TPWB

    Autonomy 36.265 20.776 21.466 17.063 13.684 17.723 123.0

    E.Mastery 20.776 40.121 20.352 21.690 19.726 16.360 138.0

    P.Growth 21.466 20.352 30.271 19.129 15.368 13.931 120.0

    P.Relation 17.063 21.690 19.129 38.951 19.733 13.126 127.0

    P.I.Life 13.684 19.726 15.368 19.733 42.430 18.353 129.0

    S.Accept 17.723 16.360 13.931 13.126 18.353 30.342 110.0

    TPWB 123.0 138.0 120.0 127.0 129.0 110.0 789.23

    Table 5: Summary Item Statistics Ryffs Psychological well-being Scales SPSS out put

    Mean Minimum Maximum Range

    Maximum /

    Minimum Variance

    N of

    Items

    Item Means 35.022 32.065 37.716 5.651 1.176 5.789 6

    Item Variances 36.397 30.271 42.430 12.160 1.402 26.202 6

    Inter-Item

    Covariances

    17.899 13.126 21.690 8.563 1.652 7.973 6

    Inter-ItemCorrelations

    0.496 0.349 0.648 0.299 1.857 0.006 6

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    Table 6: Item-Total Statistic Ryffs Psychological well-being Scales SPSS out put

    Scales

    Scale Mean if

    Item Deleted

    Scale

    Variance if

    Item Deleted

    Corrected

    Item-Total

    Correlation

    Squared

    Multiple

    Correlation

    Cronbach's

    Alpha if Item

    Deleted

    Aoutunmy 172.42 537.652 .650 .515 .827

    E.Mastery 173.00 517.412 .686 .478 .819

    P.Growth 177.94 544.577 .703 .542 .818

    P.Relation 174.64 534.907 .629 .427 .831

    P.I.Life 178.07 539.180 .574 .388 .842

    S.Accept 174.59 566.011 .607 .415 .835

    ((all correlation are significant at 0.01 level)

    Appendix B: Ryffs Psychological Well-being Scale Urdu Translation

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    DIFFERENCES IN MOBILE

    SERVICE PERCEPTIONS:

    COMPARISON OF STUDENTS

    AND STAFF AT A BUSINESS

    UNIVERSITY

    Laiq Muhammad KhanCollege of Computer Science and Information Systems,

    Karachi

    Abstract:

    Mobile services are widely used all over the world and with theincrease of mobile service users the competition between

    different mobile service providers is also increasing in every

    country. Mobile service users are very conscious about networkquality, perceived value, billing service, satisfaction etc.

    Perception of users about different services and packages offeredby different mobile service providers varies age wise, gender

    wise, country wise etc.

    This study aims to investigate the difference in mobile serviceperception and its impact on perceived value, satisfaction, loyalty

    between two significant groups of mobile service users, thestudent and staff of a big business university at Karachi.

    A group of statistical technique comprising analysis of variance,regression and inferential statistics are used for testing

    hypothesis about the attributes and for developing model of

    loyalty.

    The results identify the mobile service quality attributes that are

    important for two groups of users. This study also findssignificant difference between the two groups of users in terms

    of effect of perceived economic and emotional value, satisfaction,

    network quality and loyalty.

    Key Words: Customer loyalty, customer satisfaction, perceived

    value, network qualityMobile service .

    JEL Classification: M3310

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    I. INTRODUCTION

    Mobile phones are widely used all over the world. The

    number of mobile users around the world was 1.5 billion at the

    middle of the year 2004 which is about 25% of the population

    (Mobile Tracker: Cell phone Demand 2005). By the end of the

    year 2005, it reached two billion (CNN 2005). In the year 2009,

    40% of the world population were mobile phone users (Gartner;

    2009)

    According to Pakistan Telecommunication Authority

    (PTA) Industry Analysis Report 2008 Cellular mobile services in

    Pakistan commenced in the 90s with two mobile service provider

    Paktel and Pakcom ( Instaphone ). There has been strong growth

    in the cellular market. By the end of 2007; five cellular operators

    were in the market in Pakistan.

    The growth rate of number of subscribers was 80% in

    the year 2007. The total subscribers were 76.9 million by Dec.

    2007, it was 34.5 millions in the year 2006, and 12.7 millions in

    2005.In 2008-09, the cellular mobile companies added over 6.3

    million subscribers, while the previous year, the addition was

    about 25 million. During the period 2007-08 the number of

    subscribers were 88 millions, 94.3 millions during the period 2008-

    09, about 97.6 millions by the end of year 2009 and 95.4 millions

    by the end of Jan. 2010 (PTA report on March 11, 2010). In USA,

    Mexico, Hong Kong, Taiwan, China studies have been made

    about the difference in perception of mobile service between

    users of different age group. We have not found any such study

    about the users of mobile services in Pakistan.

    The purpose of this study is to investigate the difference

    in mobile service quality perceptions and its impact on perceived

    value, satisfaction, and loyalty between two important mobile

    service users groups i.e. students and staff at a leading Business

    university at Karachi in 2010.

    As the usage pattern of these two groups is distinct,

    the life style of these two groups as well as the technology

    diffusion may cause significant differences in their satisfaction

    and loyalty decisions.

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    II. LITERATURE REVIEW

    Kumar, A and Lim,H(2006)under took this study to

    investigate the effects of age on mobile service quality perceptions

    and its impact on perceived value, satisfaction and loyalty

    decisions with respect to two different consumers groups 12 years

    to 26 years termed as Generation y and 42 60years termed as

    Baby boomers.

    They have collected data for the age group 18-24 years

    old from a sample of 159 out of the total population of the students

    at Southern University USA, using the method of convenience

    sampling. The data for the persons of the age group 42-60 years

    has been selected on the basis of a sample size of 139 through a

    web-based survey. In both the groups, the respondents are onlymobile services users.

    In this study analysis has been performed in three steps.

    1. Separate baseline models have been established

    for both the groups Gen Y and baby boomers

    by using the data sets for Gen Y and baby

    boomers respective ly. The signif icant paths

    were reconfirmed by using the multi-group SEM

    model. Chi Square statistic has been used to

    test the goodness of fit.

    2. I n o rd er t o a sse ss t he m et ri c e qui va le nc yof the constructs in the two groups, a

    measurement model has been fitted.

    Goodness of fit test has been performed.

    3. At the fi nal stage, to tes t the equa li ty of the

    structural parameters additional constraints

    were added and test1 of invariance has been

    performed by using Chi- Square stat ist ic.

    1They have used test of invariance to discover that the structural

    parameters are equal or not across the groups of the babyboomers and Generation-y 1

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    263

    They have tested several hypotheses.

    About the effect of network qual ity on perceived

    economic and perceived emotional value. They have

    concluded that there is significant positive effect of

    network quality on perceived economic and emotional

    value for both groups.

    About the effect of billing system on perceived

    economic value and perceived emotional value for both

    age groups. They have concluded that there is a

    significant difference between billing system and

    perceived economic and emotional value for the two

    groups.

    About the effect of counter service quality on perceived

    economic value and perceived emotional value for thetwo groups. They have concluded that customer service

    quality has an insignificant relationship with perceived

    economic value and emotional value for baby boomers

    while customer service quality has a positive effect on

    perceived emotional value alone for Gen Y- ers.

    About the effect of emotional value on satisfaction for

    the two age groups. They have concluded that there is

    significant difference for both the groups with respect

    to emotional value and satisfaction; perceived emotional

    value has a greater effect on Gen Y-ers than the baby

    boomers.

    About the effect of perceived economic value on

    satisfaction for the two age groups, it has been observedthat there is a positive significant effect of perceived

    economic value on satisfaction for baby boomers but

    not for Gen-Y and hence the effect of perceived

    economic value on satisfaction is greater for baby

    boomers.

    Relationship between Satisfaction and Loyalty has been

    studied and it was found that there is a positive relationship

    between satisfaction and loyalty in both age groups.

    Yang, Z and Peterson, R.T (2004)have undertaken a

    study to investigate the moderating effects of switching over

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    costs on customer loyalty through both satisfaction and perceived

    value measures.

    They have collected data through a Web-based survey.

    Arandom selection of 4000 subjects from an e-mail list

    provided by an e-mail broker has been selected and respondents

    were invited to participate in the survey .Responses from 257

    participants were received by the authors , including 22 incomplete

    or duplicated responses,therefore the actual sample size is 235.The

    analysis has been performed in four steps

    1. In the first step, exploratory factor analysis has been

    performed to determine the underlying factor structure of the scale

    items. 2. In the second phase, confirmatory factor analysis has

    been performed.

    Chi-Square statistics have been used and goodness of fit

    test has been performed in order to test the fitting ofthe model.

    3. The discriminant validity of the measures has been

    examined by two different procedures.

    a) The AVE2has been compared with the square of the

    parameter estimate among the latent variables.

    b) The discriminant validity of each construct has beendetermined by loading higher on the construct of

    nterest than any other variable.

    4. Simultaneous maximum-likelihood-estimation

    procedures have been used in order to examine the hypothesized

    relationships among perceived, customer satisfaction, and

    customer loyalty. Goodness of fit test has been performed.

    They have tested the hypothesis:

    About the effect of customer loyalty on customer

    perceived value. As the difference is significant, therefore

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    it is concluded that customer loyalty will be positively

    influenced by customer perceived value

    About the effect of customer loyalty on customer

    satisfaction. As the difference is significant, therefore it is

    concluded that customer loyalty will be positively

    influenced by customer satisfaction.

    About the customer satisfaction on customer perceived

    value. As the difference is significant, therefore it is

    concluded that customer satisfaction is positively

    influenced by customer positive value.

    About the level of switching cost on customer loyalty

    through customer satisfaction, As the difference is

    insignificant, it is concluded that the higher the level of

    switching cost customer loyalty will not lead to greater

    customer satisfaction.

    About the level of switching cost on the customer loyalty

    through perceived value. As the difference is insignificant,

    hence it is concluded that the higher the level of switching

    cost, the perceived value will not lead to greater customer

    satisfaction

    Anderson, R.E and Srinivasan, S.S (2003)undertook a

    study to observe the impact of satisfaction on loyalty in electronic

    commerce (e-commerce). In order to observe the relationship between

    satisfaction and loyalty, they included the variables convenience

    motivation and purchase size as consumer level factors where as

    trust and perceived values were included as business level factors.

    2The AVE (Average Variance Extracted) represents the amount of

    variance captured by the constructs measures relative to

    measurement error and the correlations among the latent variables.

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    Initially they selected a random sample of 5000 consumers

    from a large list of e- retailing consumers maintained by an online

    marketing research firm. They collected the data by sending an

    invitation through an e-mail to each of the 5000 respondents and 1211

    complete and usable responses were received. The representative-

    ness of the data has been evaluated by comparing the collected sample

    data with the data collected by a Greenfield Online study showing

    similar demographic characteristics. To measure various constructs,

    validated items used by other researchers have been adapted.

    For the purpose of analysis, the sample has been split in to

    two sets:

    (a) An exploratory data set of 360 observations.

    (b)The model estimation data set of 851 observations.

    1. An exploratory factor analysis technique has been applied to the

    exploratory data set and internal consistency estimates were obtained.

    High internal consistency between various constructs was observed

    on the basis of the estimates.

    2. The model for loyalty has been obtained on the basis of the model

    estimation data by applying regression analysis. The coefficient alphas,

    means and standard deviations for various constructs on the basis of

    model estimation data sets have been obtained.

    The regression model which was run in this research paper is as follows:

    LT = 0+ 1 SA + 2TR + 3PV + 4IN + 5CM + 6SA*TR +

    7SA*PV +

    8SA*PS +

    9SA*IN +

    10SA*CM +

    LT: e- Loyalty

    SA: e- Satisfaction

    TR: Trust in the e-Business

    PV: Perceived value

    IN: Inertia

    CM: Convenience orientation

    PS: Purchase size

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    Their tested hypotheses are:

    About the effect of the level of e-satisfaction on the level

    of e-loyalty. As the difference is significant, therefore it is

    concluded that the higher the e-satisfaction, the higher

    the e-loyalty

    That the impact of customer e-satisfaction on e-loyalty is

    moderated by inertia. As the difference is significant, it is

    concluded that e-satisfaction will have a higher impact on

    e-loyalty at a lower values of inertia than at higher values

    of inertia.

    That the impact of customer e-satisfaction on e-loyalty is

    moderated by convenience motivation. As the difference

    is significant, it is concluded that convenience motivation

    positively moderates the impact of customer e-satisfactionon e-loyalty.

    That the impact of customer e-satisfaction on e-loyalty is

    moderated by purchase size. As the difference is

    significant, it is concluded that purchase size moderates

    the impact of customer e-satisfaction on e-loyalty.

    That the impact of customer e-satisfaction on e-loyalty is

    moderated by trust. As the difference is significant, it is

    concluded that trust moderates the impact of customer e-

    satisfaction on e-loyalty.

    That the impact of customer e-satisfaction on e-loyalty is

    moderated by trust. As the difference is significant, it is

    concluded that trust moderates the impact of customer e-

    satisfaction on e-loyalty. That the impact of customer e-satisfaction on e-loyalty is

    moderated by perceived value of a Web site. As the

    difference is significant, it is concluded that perceived

    value moderates the impact of customer e-satisfaction on

    e-loyalty.

    Definitions of Variables (Kumar, A and Lim,H, 2006)

    Service quality

    Perceived value (i.e. economic, emotional)

    Satisfaction and

    Loyalty intention

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    Overall service quality:

    Quality is defined as the overall excellence or superiority

    that consumers perceive from a product/service

    (Zeithaml, 1998). Service quality in the use of mobile

    services can be perceived through both technical (e.g.

    pricing plans, network quality and data services) and

    functional attributes (e.g. billing system and customer

    service quality) of mobile services. Overall perceptions

    of service quality are formed by a consumers evaluation

    of multiple quality dimensions (Gronroos, 1984). In

    general, researchers, agree that positive perceptions of

    service quality enhance consumers perceived value and

    the level of satisfaction. In other words, a consumers

    initial appraisal of service quality can arouse positiveemotion, which results in behavioral responses (Bagozzi,

    1992).

    Perceived Value:

    Previous studies examined perceived value in terms of

    monetary tradeoffs only (McDougall and Levesque,

    2000) what you get for what you pay. However,

    consumers appear to assets perceived value not only by

    monetary tradeoffs but also by other psychological

    benefits (e.g. enjoyment and fun) (Sweeney and Soutar,

    2001). Similarly, previous studies emphasized both

    intrinsic and extrinsic motivations as predictors ofbehavioral intentions (e.g. Davis et al.,1989). While

    extrinsic motivation is goal oriented, intrinsic motivation

    pertains to the pleasure and inherent satisfaction driven

    by service experience (Vankatesh et al., 2000). Therefore

    this study measures the effects of both perceived

    economic and emotional value on consumers

    satisfaction.

    Satisfaction and loyalty:

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    Satisfaction has been widely studied as a mediator

    between perceived value and loyalty (Andreassen and Lindestad,

    1998; Cronin et al., 2000). Customer satisfaction is an important

    factor for a long term relationship between a firm and a customer

    (Anderson and Srinivasan, 2003). Loyalty refers to a consumers

    commitment to repurchase a preferred product or service

    consistently in the future (Oliver, 1980). Researcher has shown

    that the consumers positive affect toward a service provider is

    likely to motivate the consumer to stay with the provider and

    also recommend the service to other (Zeithaml et al., 1996).

    Therefore, this study measures the direct effect of satisfaction

    on consumers loyalty decisions.

    III. Methodology

    The purpose of our research is to investigate the

    difference in mobile service quality perceptions and its impact

    on perceived value, satisfaction, and loyalty between two

    important mobile service users groups i.e. students and staff at a

    major Business University in Karachi. Consequently the group

    of students is appearing as the group of consumers of mobile

    service without income and the group of staff is the group of

    consumers with income.

    As the usage pattern of these two groups is distinct, the life

    style of these two groups as well as the technology diffusion

    may cause significant differences in their satisfaction and loyalty

    decisions.

    This study proposes that differences in mobile service usage

    between students and staff may be caused by different attributes.

    The following hypotheses have been tested.

    H1a: customer loyalty will be positively influenced by customer

    emotional value

    H1b: customer loyalty will be positively influenced by customer

    economic value

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    H2a: customer satisfaction will be positively influenced by customer

    economic value

    H2b: Satisfaction will be positively influenced by customer

    emotional value

    H3: Data services will have a greater effect on perceived economic

    value for students than for staff.

    H4: Data services will have a greater effect on perceived emotional

    value for students than for staff.

    These hypotheses are based on the literature survey

    formulated in Section II above.

    Measures

    The research instrument has been adapted from the researches of

    Kumar, A and Lim, H(2006) , Yang, Z and Peterson, R. T(2004),

    Anderson, R. E and Srinivasan, S.S (2003)

    The likert scale rating 5 steps has been used to measure the

    variables

    1=strongly agreed, 2=agreed, 3= dont know, 4= disagreed, 5=

    strongly disagreed

    But for the negative response questions Q.13, 14,15and Q.25 scale

    rating is1= strongly disagreed, 2=disagreed, 3=dont know, 4=agreed,

    5=strongly agreed

    The questionnaire comprising of the questions regarding mobile

    service in use, age, gender, perceived quality of mobile service,

    perceived value, the level of satisfaction, and loyalty.

    The questionnaires for both the groups comprises of 28 questions.

    However the questionnaire for staff includes two additional

    informations about designation and education.

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    To measure the various concepts, validated items used by other

    researchers have been adapted.

    To capture the consumers perception about Billing service two

    items have been adapted from the study of Kumar .A and Lim

    H(2006). Q.8 and Q.9 of this study are similar to that of Kumar A

    and Lim H Questionnaire and measures the consumers

    perception about billing service.

    The consumers perception about the Network Qualityhas been

    measured by three items. Two items have been adopted from the

    study of Kumar .A and Lim H (2006), Q.10 and Q.12 are common

    and Q.11 (see appendix II) about the Voice Quality has been

    introduced in this questionnaire.

    The Consumers perception about the Customer Service Quality

    has been measured by using three items. These items have been

    adopted from the studies of Kumar A and Lim H (2006), Yang Z

    and Peterson R.T (2004). Q.13 and Q.14 and Q.15 (see appendix

    II) of this study are measuring the perception about Customer

    Service Quality.

    Data servicesin this study have been measured by using two

    items. These items have been adopted from the studies of Kumar

    A and Lim H (2006), Yang Z and Peterson R.T (2004). Q.16, Q.17

    and Q. 18 (see appendix II) of this study are common and

    measuring the perception of consumer about Data services.

    Perceived value includes both monetary and non-monetary

    benefits that consumer perceive in a service setting. In this study,

    we consider perceived value to include economical and emotional

    value (Kumar .A and Lim H, 2006), Perception about theEconomic

    valuehas been measured by using three items. Two items have

    been adopted from the studies of Kumar A and Lim H (2006),

    Anderson R E and Srinivasan SS (2003) Q.19 and Q.20 (see

    appendix II) of this study are common and a new question Q.21

    (Number of SMS you send per day) has been introduced.

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    Consumers perception about the Emotional value has been

    measured by using three items. All items have been adopted from

    the studies of Kumar A and Lim H (2006), Anderson R E and

    SrinivasanS S (2003). Q.24, Q25, Q.26 (see appendix II) of this

    study measures the consumers perception about Emotional value.

    Consumers perception about the Satisfactionhas been measured

    by using two items. Both items have been adopted from the studies

    of Kumar A and Lim H(2006), and Anderson R E and SrinivasanS

    S (2003). Q.22 and Q.23 (see appendix II) of this study measures

    the perception about the satisfaction.

    Consumers perception about the Loyaltyhas been measured by

    using two items. Both items have been adopted from the studies

    of Kumar A and Lim H (2006), Anderson R E and SrinivasanSS(2003). Q.27 and Q.28 (see appendix II) of this study measures the

    consumers perception about the Loyalty.

    In this study some questions different from the questionnaires of

    other researchers are being included due to the change of the

    environment, habits and behavior of the consumers. These

    questions are about the causes of selecting mobile service,

    changing mobile service, using more than one mobile service at a

    time, and the type of package.

    The question about the rating of SMS service is not included in

    the questionnaire of the other researchers. (How will you rate

    SMS service of your selected mobile company?)

    This research is different from the study of Archana Kumar and

    Heejin Lim (2006), Rolph E. Anderson and Srini S. Srinivasan(

    2003 ) ,Zhilin Yang and Robin T. Peterson ( 2004 ) on the following

    basis:

    The groups are the students and the staff of a large educational

    institution in Karachi in early 2010

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    Some new investigations on the basis of the behavior of the

    consumers of mobile services in this institute are being included.

    (Q.2 to Q.7) in our instrument attached

    In this research it is being investigated:

    Whether Data services (e.g. SMS, RINGTONES, MUSIC,

    and DOWN LOADS) have greater effect on perceived

    economic value for students than for staff?

    Whether Data services (e.g. SMS, RINGTONES, MUSIC,

    DOWN LOADS) have greater effect on perceived

    emotional value for staff than the students.

    Whether Functional service quality (billing system) hasa greater effect on perceived value for staff than for

    students.

    Whether Functional service quality (customer service

    quality) has a greater effect on perceived emotional

    value for staff than for students.

    Whether Perceived emotional value has a greater effect

    on satisfaction for students than the staff.

    Perceived economical value has a greater effect on

    satisfaction for staff than students.

    Difference of perception between staff and student about

    satisfaction.

    Difference of perception between staff and student

    about loyalty. Different mobile services would make a significant

    difference in users satisfaction.

    Sample design:

    The targeted population of this research consists of all

    students and staff (Teaching and Non-teaching) of a Business

    University.

    Keeping in view the variation in the perception of the

    two user groups of mobile services, Stratified random sampling

    has been used independently for both groups in order to select

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    representative samples for both. The data have been collected

    by convenience sampling for both student and staff.

    Sample size and its allocation:

    For students

    There were 3777 total students registered during the fall

    semester, 2009 at the university. They have been classified in the

    groups BBA (H), BS, MBA (Reg.) and MBA (Ex.)

    Groups No. of students

    BBA(H) 1428

    BS 650

    MBA(Reg.) 951MBA(Ex.) 748

    The size of the sample with 5% level of precision and 95%

    confidence level with unknown population variance is 352

    The size of the sample to be selected from each stratum has been

    decided by proportional allocation method. The calculated sizes

    of the samples are 132, 61, 88, and 69 respectively.

    For staff

    Total staff of the university in Fall 2009 was 342. This

    can be split in three categories i.e. management staff (111),permanent faculty (106) and visiting faculty (125).

    The size of the sample with 5% level of precision and

    95% confidence level with unknown population variance is 150

    The size of the sample to be selected from each category

    has been decided by proportional allocation method. The

    calculated sizes of the samples are 48, 42 and 60 respectively.

    Samples from both the groups have collected by convenience

    sampling.

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    1) Correlation analysis has been used to explore

    the relationship between different concepts

    and attributes

    2) Regression analysis is being used to determine

    direction of causation.

    3) Multiple regressions are being used to develop

    regression model for Loyalty.

    4) Tes ting of hypothesis has been performed

    by using t- stat istic, Chi- square statistic,

    analysis of variance etc.

    Data Analysis:

    Table 1-

    Correlation analysis results based on the data of the

    samples for student and staff given in Table 1

    F Sig. t df

    Sig. (2-

    tailed)

    Mean

    Difference

    Std. Error

    Difference Lower Upper

    Equal variances

    assumed

    .747 .388 2.146 467 .032 .17321 .08070 .01464 .33179

    Equal variances no

    assumed

    2.057 217.833 .041 .17321 .08420 .00727 .33916

    Equal variances

    assumed

    3.027 .083 3.480 495 .001 .20277 .05826 .08829 .31724

    Equal variances no

    assumed

    3.286 240.689 .001 .20277 .06171 .08120 .32433

    billing

    service

    data

    services

    Independent Samples Test

    Levene's Test for t-test for Equality of Means

    95% Confidence

    It is found that there is significant difference in the

    perception towards the billing service between the groups of

    students and staff as P

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    Table 2 Correlations between Emotional Value and Loyalty

    classification of respondents emotional value loyaltyStudents emotional

    value

    Pearson Correlation 1 .530**

    Sig. (2-tailed) .000

    N 351 351

    loyalty Pearson Correlation .530**

    1

    Sig. (2-tailed) .000

    N 351 351

    Staff emotional

    value

    Pearson Correlation 1 .677

    Sig. (2-tailed) .000

    N 149 147

    loyalty Pearson Correlation .677**

    1

    Sig. (2-tailed) .000N 147 147

    **. Correlation is significant at the 0.01 level (2-tailed).

    H1a

    stated customer loyalty will be positively influenced by customer emotional value.

    The effect of customer emotional value on loyalty is significant as p

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    H1b

    stated customer loyalty will be positively influenced by customer economical value.

    The effect of economic value on loyalty is significant as p

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    Table 5 Correlations between Emotional Value and Satisfaction

    classification of respondents emotional value Satisfaction

    Students emotional

    value

    Pearson Correlation 1 .542

    Sig. (2-tailed) .000

    N 351 351

    satisfaction Pearson Correlation .542 1

    Sig. (2 -tailed) .000

    N 351 351

    Staff emotional

    value

    Pearson Correlation 1 .602

    Sig. (2-tailed) .000

    N 149 149

    satisfaction Pearson Correlation .602** 1

    Sig. (2 -tailed) .000N 149 149

    **. Correlation is significant at the 0.01 level (2-tailed).

    H2b

    stated satisfaction will be positively influenced by customer emotional value. The

    effect of perceived emotional value on satisfaction is significant as p

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    H3stated Data services will have a greater effect on perceived economic value for

    students than for staff. The effect of data services on economic values is significant

    for students as p

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    Table 8a

    classificaton ofrespondents Model R R Square

    Adjusted RSquare

    Std. Error of theEstimate

    student 1 .584a .341 .329 .62607

    Staff 1 .747b .558 .538 .55288

    a. Predictors: (Constant), satisfaction, network quality, economic value, customer support, data services, emotional value

    b. Predictors: (Constant), satisfa