problems and constraints of the local herbs and medicinal plants (hmp) processing industry

61

Muhamad Setefarzi Mohd Noor and Tengku Mohd. Ariff Tengku Ahmad

Problems and constraints of the local herbs and medicinal plants(HMP) processing industry(Masalah dan halangan industri pemprosesan herba dan tanaman ubatan tempatan)

Muhamad Setefarzi Mohd. Noor* and Tengku Mohd. Ariff Tengku Ahmad*

Key words: herbs, processing industry, growth and development, factor analysis

AbstractA study on market structure, demand and potential of the herbal and medicinalplants (HMP) processing industry was conducted throughout Peninsular Malaysia.Respondents were interviewed using structured questionnaires. One of the objectivesof this study was to identify problems and constraints faced by local HMP processors.A multivariate factor analysis using the principal component method in estimatingthe correlation matrix, communalities, eigenvalues, factor coefficients or loading,and residuals was used on the HMP processing plants. A varimax rotation algorithmwas performed to induce an orthogonal factor dimension. The results of the analysisshowed that capital/finance, workers, technology, raw materials and marketingwere the five significant underlying factors hampering the growth and developmentof the local HMP processing industry. It is important that efforts are taken tofacilitate local HMP processors in overcoming these problems and constraints inorder to ensure that the growth and expansion of the industry can be accelerated.

Economic and Technology Management Review. Vol. 1 No. 1 (June) 2006: 61–70

IntroductionHerbal and medicinal plant-based (HMP)products have been widely accepted by themajority of the world population as alternativesto conventional medicines. World HealthOrganization (WHO) estimated that about 80%of the world population depend on traditionalmedicines for their healthcare (Anon. 1993).In developing countries such as China, India,Sri Lanka and others in Asia as well as Africa,traditional medicines are officially recognisedin their healthcare system (De Silva 1997). Indeveloped countries such as the United States,Canada, the EU and Japan, studies showedthat the majority (more than 50%) of theirpopulations have intentions to use traditionalmedicines (Emerich 1996; Larsen 1996).

World trade and the consumption ofHMP-based products in 1996 was estimatedat US$294 billion, with health foods and drinks

being the main product category, representing85% of the total market value. Productcategories such as herbal remedies,phytomedicines and biopesticides representedonly 3–5% of the world consumption of HMP-based products. Annual increase in demandfor health foods and drinks is expected to beat the rate of 15%, and 10% respectively forherbal remedies, phytomedicines andbiopesticides. In Malaysia, the demand forherbs and herbal-based products showed anincreasing trend. Industry experts haveestimated that the current market value of HMPproducts in Malaysia is about RM4.5 billionand is expected to increase at the rate of about20% per annum. The ever-increasing growthin medicinal herb production and consumptionworldwide suggests encouraging prospects forthis industry with good potential forinvestment.

*Economic and Technology Management Research Centre, MARDI Headquarters, Serdang, P.O. Box 12301, 50774Kuala LumpurE-mail: [email protected]

62

Problems and constraints of the local herbs and medicinal plants (HMP) processing industry

The increasing demand in both domesticand world markets is reflected by the tradedata which showed that Malaysia’s imports ofHMP increased from RM140.7 million in 1986to RM653.0 million in 2001 (Anon. 2003).This amounts to an increase of more than fourtimes within the time period, thus registeringan average growth rate of 10.2%. Realisingthe vast potential of the HMP industry, theprocessing sub-sector had shown a significantnumber of new entrants. In 1995, a total of 62new manufacturing licenses were processedby the National Pharmaceutical Control Bureau(NPCB). This rose to 237 in 1999, a three-fold increased over a five-year period. Thenumber of processing premises of HMP alsoincreased to 264 in 1999 as compared to 103in 1995, an increase of 156% for the 1995–99period (Anon. 2001). Despite clear indicationof strong domestic market demand for HMPproducts, the local HMP processing industryis relatively undeveloped. Many HMP firmsare small and operating below their productioncapacity. Muhamad Setefarzi et al. (2001), intheir study on the HMP processing industry,found that 55% of the firms operated less than70% of their full production capacity, anindication that the local HMP processing firmswere facing constraints in their businessachievements. This study was thereforeprimarily aimed at identifying factors thataffect the growth and development of the localHMP processing industry, and to consequentlysuggest recommendations in overcoming theseconstraints.

MethodologyA cross-sectional survey on 163 local HMPprocessors throughout Peninsular Malaysiawas conducted by personal interviews usingstructured questionnaires. A set of 20 itemsmeasuring the processors’ perceptions ofproblems were developed (Table 1).Respondents were requested to express theirdegree of attitudes or opinions on these itemslisted through a four-point scale: 4 = stronglyagreed, 3 = agreed, 2 = disagreed and1 = strongly disagreed. As suggested byAhmad Mahdzan (1992), the responses of‘uncertain’ or ‘not sure’ were not included inthe questionnaire. This four-point scale wasused to estimate the degree or values of therespondents’ perceptions. The collected datawere analysed using Factor Analysis to identifythe underlying factors facing the HMP.

The factor analysis is both exploratoryand confirmatory. This technique has beendiscussed at length in many statistical books(Harman 1970; Mardia et al. 1979; Seber 1984;Haris 1985; Johnson and Wichern 1988;Tabachnick and Fidell 1989). Relationshipsamong sets of many interrelated variables areexamined and represented in terms of fewunderlying factors. The purpose of utilisingthe factor analysis is to explain or create amodel of internal dependencies among a setof variables, by constructing a set of latentvariables called common factors which arepresumed to manifest themselves through theobserved measurements. Mathematically, thefactor analysis is expressed as a linear

Table 1. The list of problems faced by local HMP processors

Code Variable Code Variable

X1 Low quality of local raw materials X11 Lack of working capitalX2 High prices of local raw materials X12 Difficulty in getting loans/creditsX3 Dependence on imported raw materials X13 High capital cost/interest rateX4 High rate of product deterioration X14 Difficulty in getting skilled workersX5 Frequent machine breakdown X15 Difficulty in getting unskilled workersX6 Stiff competition X16 High rate of employee turnoverX7 Difficulty in getting distributors X17 Demand of increased pay by workersX8 Lack of domestic market information X18 Lack of training facilitiesX9 Lack of foreign market information X19 High import tax/tariffsX10 High cost of production X20 High marketing costs

63


combination of underlying factors. The numberof variance a variable shares with all othervariables included in the analysis is referredto as the communality. The covariation amongthe variables is described in terms of a smallnumber of common factors, plus a uniquefactor for each variable. These factors are notovertly observed. The factor model can berepresented as:

Xi = ai1F1 + ai2F2 + ai3F3 +…… + aimFm+ ej

Where,

Xi = jth standardise variableaij = standardise multiple regression

coefficient of variable i on a commonfactor j

F = common factorej = unique residual factorsm = number of common factors

The unique residual factors are uncorrelatedwith each other and with the common factor.

The common factors themselves can beexpressed as linear combinations of theobserved variables.

Fi = wi1X1 + wi2X2 + wi3X3 + …….+ eikXk

Where,

Fi = estimate of ith factorwi = weight or factor score coefficientk = number of variables

The first factor explains the largestportion of the total variance, and then a secondset of weights can be selected, so that thesecond factor accounts for most of the residualvariance, subject to being uncorrelated withthe first factor. This same principle is appliedto select additional weights for the additionalfactors. Unlike the values of the originalvariables, factor scores are not correlated. Thefirst factor accounts for the highest variance

in the data, the second factor the secondhighest, and so on.

Statistical analysis on dataThe Kaiser-Meyer-Olkin (KMO) measure ofsampling adequacy was utilised in order todetermine the appropriateness of the analysis.This index compares the magnitude of theobserved correlation coefficients to themagnitude of the partial correlationcoefficients. A high value of KMO (between0.5 and 1.0) indicates that the factor analysisis appropriate. A correlation matrix is used toshow the simple correlation, r, between allpossible pairs of variables included in theanalysis. In practice, the Kaiser criterion i.e.the eigenvalue greater than unity is used toextract the factor. Only factors witheigenvalues greater than 1.0 were retained,while factors with less than 1.0 were notincluded in the model. Malhotra (1993) andAaker et al. (2003) recommended that thefactors extracted should account for at least60% and 70% of the variance respectively.Furthermore, the model fit is determinedthrough residuals or the differences betweenthe observed correlations (as given in the inputcorrelation matrix) and the reproducedcorrelations (as estimated from the factormatrix). Small residuals indicate an acceptablemodel fit.

Results and discussionResults of the survey showed that the majority(76%) of the entrepreneurs were involved inlocal HMP processing activities for more thanfive years while some of them (29%) exceeded20 years. This showed that the local HMPprocessing industry have been in existence inthis country for quite sometime. Only 24%(38) of the respondents can be considered asnew players, with their involvement of lessthan five years. The results of the study alsorevealed that 70% (114) of the respondentswere full time HMP processors, while theremaining 30% were part-timers. The latterhad other activities as their main source ofincome, and most of them operated HMPprocessing as a backyard industry.

64


Most of the firms (75%) have 10 andless workers, 18% between 11–50 and theremaining 7% employed more than 50workers. The value of the annual sales of thefirms ranged between RM960 andRM27 million, with an average of RM1.024million. However, the values of the annualsales for most of the firms (65%) were lessthan RM250,000. Based on the value of annualsales and the number of employees, themajority (98%) of local firms involved in HMPprocessing industry are under the small-scalecategory with annual sales of less thanRM10 million and the number of employeesof less than 50. Only 2% can be considered asmedium scale firms (number of employeesare between 50 and 150 and with annual salesof RM10–RM25 million), and none of themcan be classified as a big scale firm.

Fifty-five and 45% of the local HMPprocessing firms operated at less and morethan 70% of their full production capacitiesrespectively. Among the factors affecting thelevel of production of the firms were lowproduct demand, lack of skilled workers andinsufficient supply of raw materials. Otherfactors such as lack of working capital,newness in business, lack of machines/toolsand under utilised plants also influenced thelevel of production.

The products from the local HMPprocessing firms can be classified into threemain categories, namely medicines for internaland external use, health foods/drinks andcosmetics. About 900 types of the products inthese categories were produced by the 163entrepreneurs. Most of them claimed that theirproducts could be used for either one specificdisease or illness or for multi-purpose. Mostof these products are traditionally prepared.These simple processings include such as hotor cold water extraction, crushing, powderingof dried materials, formulation of powder intopaste through blending with water, oil orhoney, as well as fermentation. The productsfrom most of these small-scale firms are mostlymarketed to consumers in the surroundingareas or to other processors as intermediategoods. However, for the medium scale firms

which produce more complex products, theirmarkets include other regions or states inPeninsular Malaysia. This involved about 26%of the firms surveyed. Only 4% of the firmsare involved in the export market.

Results of the factor analysisThe overall KMO was found to be 0.6161(>0.5), indicating that the use of the factoranalysis was an appropriate technique foranalysing the correlation matrix of the 20problems faced by the local HMP processors.The correlation matrix, constructed from thedata obtained, is shown in Table 2, and thecommunalities are presented in Table 3. Thereare relatively high correlations between ‘highprices of local raw materials’ (X2) with‘dependence on imported raw materials’ (X3);‘high rate of product deterioration’ (X4) with‘frequent machine break down’ (X5); ‘highcost of production’ (X10) with ‘lack ofworking capital’ (X11), ‘difficulty in gettingloans/credits’ (X12), ‘high capital cost/interestrate (X13) and ‘high import tax/tariffs’ (X19);‘lack of working capital’ (X11) with ‘difficultyin getting loans/credits’ (X12), ‘high capitalcost/interest rate’ (X13) and ‘high import tax/tariffs’ (X19); ‘difficulty in getting loans/credits’ (X12) with ‘high capital cost/interestrate’ (X13) and ‘high import tax/tariffs (X19)’;‘high capital cost/interest rate’ (X13) with‘high import tax/tariffs (X19)’; ‘high rate ofemployee turnover (X16) with ‘demand ofhigher pay by workers’ (X17) and ‘lack oftraining facilities (X18); and ‘demand of higherpay by workers’ (X17) with ‘lack of trainingfacilities (X18). The communalities (Table 3)show that over 80% of the variances of twovariables (X4 and X16) are accounted for, andthe same is indicated by over 70% in 13 othervariables (X1, X3, X5, X7, X8, X10, X11,X112, X13, X14, X17, X18 and X19), whileless than 70% is accounted for in five variables(X2, X6, X9, X15, and X20).

The eigenvalues which accounted for thevariances of the factor pattern are presented inTable 4. The eigenvalues can be equal, zero ornegative. If the eigenvalues are equal, thestandard error of the coefficients of the

65


Tab

le 2

. Cor

rela

tion

mat

rix

for

20 p

robl

ems

face

d by

loc

al H

MP

proc

essi

ng i

ndus

try

X1X2

X3X4

X5X6

X7X8

X9X1

0X1

1X1

2X1

3X1

4X1

5X1

6X1

7X1

8X1

9X2

0

X11.0

000

X20.1

994

1.000

0X3

0.425

60.5

542*

1.000

0X4

0.023

6–0

.2353

–0.03

951.0

000

X50.2

670

–0.18

56–0

.0701

0.729

1*1.0

000

X60.0

827

0.124

00.2

605

0.118

20.0

000

1.000

0X7

0.260

1–0

.0143

0.222

20.0

681

0.290

80.2

287

1.000

0X8

–0.09

360.0

566

0.073

00.3

280

0.349

40.2

748

0.406

91.0

000

X9–0

.2165

0.253

90.2

041

0.001

70.0

016

0.139

20.3

890

0.477

61.0

000

X10

0.113

00.2

136

0.120

40.1

882

0.218

4–0

.0759

0.189

10.4

647

0.440

41.0

000

X11

0.092

20.2

874

0.119

7–0

.1234

0.074

60.1

569

0.100

40.3

309

0.260

90.5

147*

1.000

0X1

2–0

.0290

0.349

70.2

977

0.099

50.1

612

0.232

10.2

492

0.457

30.4

905

0.533

2*0.6

082*

1.000

0X1

3–0

.0234

0.247

30.1

825

0.172

80.2

004

0.138

90.1

777

0.399

40.3

674

0.705

0*0.6

360*

0.746

9*1.0

000

X14

0.371

80.3

154

0.455

7–0

.1056

0.066

40.2

558

0.189

70.1

390

0.273

60.1

547

0.124

40.2

154

0.104

71.0

000

X15

0.185

20.3

770

0.371

2–0

.2245

–0.17

280.0

877

0.347

7–0

.1958

0.238

00.1

733

0.134

20.2

268

0.158

30.2

430

1.000

0X1

60.1

837

–0.10

19–0

.1019

0.056

20.2

417

–0.07

620.0

246

0.052

20.0

962

0.422

60.1

871

0.355

10.3

310

0.375

00.1

737

1.000

0X1

70.1

892

–0.22

97–0

.0816

0.361

50.4

590

0.150

60.2

699

0.268

30.2

433

0.316

60.2

225

0.350

40.3

257

0.406

80.0

429

0.571

6*1.0

000

X18

0.145

9–0

.0377

–0.03

830.2

141

0.305

1–0

.0335

0.046

40.2

354

0.207

50.4

661

0.230

80.4

264

0.408

40.3

891

0.153

00.8

289*

0.515

6*1.0

000

X19

–0.26

180.0

389

0.127

30.2

749

0.162

30.2

420

0.020

70.3

439

0.380

60.5

781*

0.502

8*0.5

739*

0.640

1*–0

.0561

0.078

80.2

755

0.213

30.3

260

1.000

0X2

00.1

634

0.388

70.1

812

–0.13

020.1

049

–0.04

670.4

479

0.134

80.2

368

0.179

2–0

.0432

0.044

00.0

922

0.184

20.4

261

–0.05

81–0

.1904

0.000

9–0

.1689

1.000

0

*High

ly co

rrelat

ed

66


Table 3. Communalities of different local HMP processingvariates

Attribute Communality Attribute Communality

X1 0.74532070 X11 0.70481414X2 0.67960088 X12 0.72377060X3 0.72786228 X13 0.76017754X4 0.83128409 X14 0.71875716X5 0.78868033 X15 0.66311333X6 0.46109100 X16 0.84833305X7 0.73893347 X17 0.71075642X8 0.73264172 X18 0.77915847X9 0.61713998 X19 0.76947704X10 0.74225566 X20 0.66509111

Table 4. Eigenvalues or accounted for variances of the factor dimensions

Factor Eigenvalues Difference Proportion Cumulativeof variance proportionexplained of variance

1 5.13007365 2.49424626 0.3581 0.35812 2.63582740 0.69206569 0.1840 0.54213 1.94376171 0.35090472 0.1357 0.67774 1.59285699 0.53744591 0.1112 0.78895 1.05541108 0.15966696 0.0737 0.86266 0.89574412 0.30641231 0.0625 0.92517 0.58933181 0.02407959 0.0411 0.96638 0.56525222 0.27316981 0.0395 1.00579 0.29208241 0.05124440 0.0204 1.0261

10 0.24083801 0.08591837 0.0168 1.042911 0.15491964 0.07439940 0.0108 1.053712 0.08052024 0.03770923 0.0056 1.059313 0.04281101 0.06120936 0.0030 1.062314 –0.01839834 0.05617284 –0.0013 1.061015 –0.07457119 0.04988227 –0.0052 1.055816 –0.12445346 0.01604362 –0.0087 1.047217 –0.14049708 0.01843493 –0.0098 1.037318 –0.15893201 0.01451765 –0.0111 1.026319 –0.17344966 0.02919902 –0.0121 1.014120 –0.20264868 –0.0141 1.0000

Eight factors will be retained by PROPORTION criterion

unobserved latent factors is inflated. The vectororientation is undefined. The inferences onthe factor loading are unwise. The covariancesof the categorical variates are not in full rankif the eigenvalues are singular or zero. Thisimplies that there exists a linear relationshipof categories on linear combinations of latentfactors. It might lead to problems in theinterpretation of the factor dimension (Jackson1991). On the other hand, the negative

eigenvalues, which exhibit the vectororientation are undefined and the factorcoefficients, which are imaginary communalityestimates, such as the square of the multiplecorrelation coefficients, may result in non-Gramian matrices. This implies that somenegative eigenvalues could be found. Thefactor loadings based on positive eigenvaluesare inflated. However, the factor scoreestimates computed from such loadings will

67


be deflated. Subsequently the existence ofnegative eigenvalues can be determined. Firstsum all the eigenvalues and then compare withthe trace of the correlation matrix. If the sumof eigenvalues is greater than the trace, thecorrelation matrix is non-Gramian, and somenegative eigenvalues are obtained (Rummel1970). In the light of this information, it hasbeen decided that the five-factor model isadequate to describe the attributable problems,there being five eigenvalues greater than unity.

In this analysis, the cumulative proportionof variance showed that the first five factorsaccount for 86.26% (factor one = 35.81%,factor two =18.40%, factor three = 13.57%,factor four =11.12%, and factor five = 7.37%)of the total variation, which was greater thanthat suggested by Malhotra (1993) and Aakeret al. (2003). Other factors with eigenvaluesless than 1.0 were not included in the model.

The varimax rotated factor matrix of 20items is shown in Table 5. The factor matrixcontains the factor loadings or coefficientsused to express the standardized variables interms of the factors. These factor loadingsrepresented the correlations between the factorsand the variables. A loading with large absolutevalue (equal or more than 0.5) indicates thatthe factor and the variable are closely related.Factor one is accounted by 35.81% of thetotal variation which seems heavily loaded(loading >0.80) on ‘high capital cost/interestrate’ (loading = 0.83463), and highly loaded(loading = 0.70–0.79) on ‘lack of workingcapital’ (0.78246), ‘difficulty in getting loans/credits’ (0.74364), ‘high import tax/tariff’(0.73228) and ‘high cost of production’(0.72178 ). This factor is related to capital orfinance. Factor two is heavily loaded on ‘highrate of employee turnover’ (0.86209) and ‘lack

Table 5. Varimax rotated factor pattern – A matrix of factor loadings for local HMP categorical problemdata

Code Item/Problem Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

X13 High capital cost/interest rate 0.83463 0.17382 0.11986 0.10024 0.08248X11 Lack of working capital 0.78246 0.05738 –0.16791 0.03590 –0.00613X12 Difficulty in getting loans/credits 0.74364 0.24347 0.03637 0.19914 0.12054X19 High import tax/tariff 0.73228 0.09646 0.21759 –0.00969 –0.12566X10 High cost of production 0.72178 0.28622 0.15681 0.12786 0.19037X16 High rate of employee turnover 0.24169 0.86209 0.03170 –0.08818 –0.03466X18 Lack of training facilities 0.29676 0.80456 0.16208 0.02396 –0.00034X17 Demand of increased pay by workers 0.20432 0.65082 0.26201 –0.22131 0.09251X14 Difficulty in getting skilled workers –0.04828 0.56651 –0.11087 0.47964 0.15594X4 High rate of product deterioration 0.06490 0.06688 0.89243 –0.07654 –0.06849X5 Frequent machine breakdown 0.11489 0.20572 0.77667 –0.12435 0.17382X3 Dependence on imported raw materials 0.12159 –0.04585 0.01429 0.74352 0.11333X2 High prices of local raw materials 0.25292 –0.10837 –0.20850 0.73501 0.12202X7 Difficulty in getting distributors 0.11170 0.05056 0.12684 –0.03916 –0.77789X20 High marketing cost –0.01986 –0.05906 –0.01264 0.32911 0.69039X6 Stiff competition 0.11378 –0.02898 0.05469 0.14937 0.04643X1 Low quality of local raw materials –0.06943 0.19750 0.11478 0.30660 0.14104X9 Lack of export market information 0.36799 0.17034 –0.0492 0.18449 0.41894X8 Lack of domestic market information 0.41891 0.05631 0.28877 0.01853 0.29833X15 Difficulty in getting unskilled workers 0.12543 0.15880 –0.21454 0.31199 0.44550

Eigenvalues 5.1301 2.6358 1.9438 1.5928 1.0554Proportion of variance (%) 35.81 18.40 13.57 11.12 7.37Cumulative proportion of variance (%) 35.81 54.21 67.77 78.89 86.26

Significant loading criterion:aim < 0.50 = not significant; 0.51 < aim < 0.69 = moderate loading; 0.70 < aim < 0.79 = higher loading;aim => 0.80 = heavy loading (highly significant)

68


of training facilities’ (0.80456), and moderatelyloaded (loading = 0.51–0.69) on ‘demand ofhigher pay by workers’ (0.65082) and‘difficulty in getting skilled workers’(0.56651). Therefore, factor two can be labeledas a worker factor. The coalitions of the twovariables forming the third factor are ‘highrate of product deterioration’ (0.89243) and‘frequent machine break down’ (0.77667).Thus, this factor can be considered as thetechnological factor. The fourth factor can becategorized as a raw material factor withvariables of ‘dependence on imported rawmaterials’ (0.74352) and ‘high prices of localraw materials’ (0.73501). The fifth and finalfactor can be interpreted as the marketingfactor with variables of ‘difficulty in gettingdistributors’ (–0.77789) and ‘high marketingcost’ (0.69039). These five significantunderlying factors can be considered asimportant factors affecting the growth anddevelopment of the local HMP industry.

Five other problems or variables namely‘stiff competition’ (X6), ‘low quality of localraw materials’ (X1), ‘lack of export marketinformation’ (X9), ‘lack of domestic marketinformation’ (X8), and ‘difficulty in gettingunskilled workers’ (X15) were found not tobe significant. These do not mean that theseproblems are completely unimportant in thegrowth and development of the local HMPprocessing industry, but they do not seem tohave a common underlying factor that issufficiently significant. The differencesbetween the observed correlations and thereproduced correlations or residuals with theuniqueness on the diagonal are shown inTable 6. There is no residual larger than 0.10and only 11 residuals are greater than 0.05,indicating an acceptable model fit.

ConclusionThe multivariate factor analysis helps revealedthe significant underlying factors encounteredby the local HMP processing industry. Fromthe analysis, it can be concluded that the fivesignificant underlying factors affecting thegrowth and development of the local HPMprocessing industry were related to capital/

finance (high capital cost/interest rate, lack ofworking capital, difficulty in getting loans/credits, high import tax/tariff, and high cost ofproduction); followed by workers (high rateof employee turnover, lack of trainingfacilities, demand of higher pay by workers,difficulty in getting skilled worker); technology(high rate of product deterioration and frequentmachine breakdown); raw materials(dependence on imported raw materials andhigh prices of local raw materials); andmarketing (difficulty in getting distributors andhigh marketing cost). It is important that effortsare taken to help the local HMP processors inovercoming these problems and constraints inorder to ensure that the growth and expansionof the industry can be accelerated.

ReferencesAaker, D.A., Kumar, V. and Day, G.S. (2003). Factor

and cluster analysis. In: Marketing Research,8th ed., p. 562–92. New York: John Wiley

Ahmad Mahdzan, Ayob (1992). Kaedah penyelidikansosioekonomi, 2nd ed. p. 264–71.Kuala Lumpur: Dewan Bahasa dan Pustaka

Anon. (1987). External trade statistics, Departmentof Statistics, Malaysia. Kuala Lumpur:Department of Statistics

–––– (1993). Guideline on the conservation ofmedicinal plants. International Union for theConservation of Nature and Natural Resources,

–––– (2001). Yearly report 2001, NationalPharmaceutical Control Bureau, Ministry ofHealth, Malaysia,http://www.serve.com/bpfk/html/index.htm

–––– (2003). External trade statistics, Department ofStatistics, Malaysia. Kuala Lumpur: Departmentof Statistics

De Silva, T. (1997). Industrial utilization of medicinalplants in developing countries, FAO.p. 257– 69. Rome: FAO

Emerich, M. (1996). Industry growth: 22.6%, NaturalFood Merchandiser, Vol. XVII, No. 6, June1996: 1, 22–39

Harman, H.H. (1970). Factor analysis model. In:Morden Factor Analysis, 2nd ed., p. 298–324.London: Academic Press

Haris, R.J. (1985). Factor analysis. In: A Primer ofMultivariate Statistic, 2nd. ed., p. 298–324.London: Academic Press

Jackson, J.E. (1991). PCA with more than twovariables. In: A User’ Guide to PrincipalComponents, p. 26–62. New York: John Wiley

69


Tab

le 6

. Res

idua

l co

rrel

atio

ns w

ith u

niqu

enes

s on

the

dia

gona

l

X1X2

X3X4

X5X6

X7X8

X9X1

0X1

1X1

2X1

3X1

4X1

5X1

6X1

7X1

8X1

9X2

0

X10.2

547

X2–0

.0107

0.320

4X3

0.047

5–0

.0292

0.272

1X4

–0.00

520.0

150

–0.00

480.1

687

X5–0

.0160

0.028

8–0

.0252

0.029

60.2

113

X60.0

126

0.025

9–0

.0697

*0.0

153

–0.02

480.5

389

X70.0

348

–0.06

55*

0.041

0–0

.0252

0.000

8–0

.0067

0.261

1X8

–0.00

16–0

.0089

0.013

50.0

051

–0.02

030.0

458

0.038

40.2

674

X9–0

.0163

0.012

50.0

221

–0.00

04–0

.0070

–0.06

60*

0.025

0–0

.0107

0.382

9X1

00.0

668*

–0.03

330.0

173

0.020

4–0

.0733

–0.04

08–0

.0002

0.032

10.0

397

0.257

7X1

1–0

.0012

0.033

5–0

.0476

–0.02

730.0

479

0.003

9–0

.0207

0.003

8–0

.0053

–0.02

190.2

952

X12

–0.03

360.0

515

0.013

6–0

.0025

0.028

8–0

.0187

0.016

70.0

030

0.001

3–0

.0865

*0.0

083

0.276

2X1

3–0

.0265

–0.00

100.0

032

0.009

5–0

.0137

0.012

50.0

009

–0.02

49–0

.0346

0.009

1–0

.0025

0.042

00.2

398

X14

–0.01

800.0

052

0.024

5–0

.0176

0.018

80.0

202

–0.02

68–0

.0195

0.019

20.0

083

0.018

5–0

.0433

0.002

40.2

812

X15

–0.01

630.0

313

–0.00

470.0

232

–0.01

530.0

104

0.017

2–0

.0465

0.005

80.0

046

0.029

4–0

.0036

–0.02

27–0

.0108

0.336

9X1

60.0

102

–0.00

50–0

.0107

–0.03

240.0

079

0.021

20.0

056

0.013

8–0

.0351

–0.00

11–0

.0285

0.023

4–0

.0095

–0.00

28–0

.0073

0.151

7X1

7–0

.0073

0.008

0–0

.0314

0.035

80.0

168

–0.03

96–0

.0074

–0.04

520.0

547*

0.019

10.0

108

0.011

00.0

264

0.048

00.0

314

–0.02

040.2

892

X18

0.015

2–0

.0149

–0.01

480.0

081

–0.00

880.0

237

–0.00

580.0

467

–0.02

58–0

.0349

0.000

80.0

212

–0.00

06–0

.0029

0.012

60.0

449

–0.06

15*

0.220

8X1

9–0

.0081

–0.04

470.0

377

–0.01

510.0

174

0.041

4–0

.0028

–0.00

360.0

184

0.056

5*0.0

200

–0.05

35*

–0.02

10–0

.0035

–0.00

370.0

272

–0.04

650.0

037

0.230

5X2

0–0

.0223

0.041

9–0

.0617

*–0

.0129

0.042

90.0

517*

0.008

60.0

108

–0.03

65–0

.0212

–0.00

55–0

.0158

0.035

60.0

283

0.023

30.0

118

–0.05

110.0

150

–0.00

500.3

349

*p≥ 0

.05

70


Johnson, R.A. and Wichern, D.W. (1988). Factoranalysis and inference for structured covariancematrices. In: Applied Multivariate StatisticalAnalysis, 2nd ed., p. 378–430. Englewood Cliffs,New Jersey: Prentice-Hall

Larsen, J. (1996). Trends in the education and practiceof alternative Medicine clinicians. HealthAffairs, 15(3) (Fall 1996): 226–38

Malhotra, N.K. (1993). Factor analysis. In: MarketingResearch, an Applied Orientation, p. 617–43.Englewood Cliffs, New Jersey: Prentice-Hall

Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979).Factor analysis. In: Multivariate Analysis,p. 253–280. London: Academic Press

Muhamad Setefarzi, M.N., Tengku Mohd Ariff, T.A.,and Sarmin, S. (2001). The local herbalprocessing industry: An analysis of raw materialutilization and production constraints.Proc. Malaysia Science and TechnologyCongress 2001, Kota Kinabalu, p. 492–501,Kuala Lumpur: MOSTE

Rummel, R.J. (1970). Factor scores. In: Applied FactorAnalysis, p. 433–45. Evanston: NorthwesternUniv. Press

Seber, G.A.F. (1984). Dimension reduction andordination. In: Multivariate Observations,p. 175–204. New York: John Wiley

Tabachnick, B.G. and Fidell, L.S. (1989). Principalcomponents and factor analysis of a survey. In:Sampling Methods of Censuses and Survey, 4thed., p. 108–30. London: Charles Griffin

AbstrakKajian struktur pemasaran, permintaan dan potensi industri herba dan tanamanubatan telah dijalankan di seluruh Semenanjung Malaysia. Responden kajian terdiridaripada pemproses herba dan tanaman ubatan (HTU) yang telah ditemu bualmenggunakan soal selidik berstruktur. Salah satu daripada objektif kajian adalahuntuk mengenal pasti masalah dan halangan yang dihadapi oleh pemproses HTUtempatan. Analysis faktor multivariate dimanfaatkan dengan menggunakan kaedahkomponen utama untuk menganggarkan matriks korelasi, ‘communalities’, nilai-eigen, pemberat model faktor dan residual. Algoritma putaran ‘varimax’ digunauntuk mengurangkan dimensi faktor yang berotogonan. Hasil kajian menunjukkanmodal/kewangan, pekerja, teknologi, bahan mentah dan pemasaran merupakan limafaktor bersignifikan yang menghalang pembangunan dan pertumbuhan industripemprosesan (HTU) tempatan. Usaha bagi membantu pengusaha HTU tempatanmengatasi masalah dan halangan yang dihadapi perlu diambil bagi memastikanpertumbuhan dan pembangunan industri.

problems and constraints of the local herbs and medicinal plants (hmp) processing industry

Documents