environmenatal uncertainity

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Understanding the impact of environmental uncertainty on efficiency performance

indicator of Thai rice millers

Phatcharee Toghaw Thongrattana, University of Wollongong, [email protected]

Ferry Jie, University of Wollongong, [email protected]

Nelson Perera ,

University of Wollongong,

[email protected]

Abstract

The purpose of this paper is to investigate seven uncertain factors (supply, demand, process,

planning and control, competitors’ action, climate condition and Thai government policy

uncertainty) affecting on efficiency of Thai rice millers. Understanding uncertain factors

influencing on efficiency of Thai rice millers is very crucial to manage them properly, and to

obtain sustainable efficiency performance. The major findings of this research show thatparticular aspects of demand, process, planning and control, competitor and government

policy uncertainty are significantly related to decrease efficiency in Thai rice millers. The

unexpected new price introduction from competitors has the greatest pessimistic influence on

efficiency of Thai rice miller. On the contrast, supply and climate uncertainty are not

significantly related to efficiency.

Keywords: environmental uncertainty, efficiency performance, Thai rice millers

age 1 of 9 ANZMAC 2009

mailto:[email protected]



http://www.uow.edu.au/gsb/staff/nelsonperera/index.html



http://www.uow.edu.au/gsb/staff/nelsonperera/index.html





Understanding the impact of environmental uncertainty on efficiency performance

indicator of Thai rice millers

Introduction

Many firms are trying to have sustainable efficiency to maintain a long-term competitive

advantage (Porter, 1985). To succeed that, environmental uncertainty is essential to be

examined in order to understand its effect on firms’ performance. The number of studies have

addressed the effect of environmental uncertainty on performance indicators of individual firm

and a supply chain. Uncertain factors along a supply chain refer to supply, demand, process

uncertainty (Davis, 1993; Ettlie and Reza, 1992), control and planning uncertainty

(Childerhouse and Towill, 2004), competitor uncertainty (Ettlie and Reza, 1992) transportation

uncertainty (Wilson, 2007) which negatively impact on supply chain performance (Bhatnagar

and Sohal, 2005; Davis, 1993; Paulraj and Chen, 2007) and force firms to implement supply

chain management strategies (Paulraj and Chen, 2007). On the other hand, customer, supplier,

competitor, and technology uncertainty on the study of Li (2002) do not impact on supplychain management practices. Thus, the main objective of this research is to examine the

environmental uncertainty affecting on efficiency of rice millers which is one key player of rice

supply chian in Thailand. In other words, the research question of this study is “in Thai rice

millers, what are the key uncertain factors having greatest pessimistic impact on their

efficiency performance indicator?”

Thai Rice Millers

A rice miller is a key player of rice

supply chain in Thailand as shown

in Figure 1. After harvesting paddyrice in wet season, the purchase of

paddy rice can be directly between

farmers and rice millers, or

indirectly between Thai government

and farmers noted as paddy rice

mortgage scheme. Some paddy rice

from farmers is milled by small or

local rice mills for their own

consumption. Meanwhile, some

paddy rice from a medium-large size of rice farm is milled by medium and large mills, and can

be packed for domestic demand and international demand (Thai Rice Foundation under Royal

Patronage, 2006). Rice millers in Thailand are very important organisations to process paddy

rice to be rice because rice is a key agricultural product of Thailand for the reason that rice

farms make up over 50 percent of farm land use in Thailand, and rice farmers is around 56% of

Thai population (Krasachat, 2004), as well as Thailand is the main rice exporter in the world

rice market (David, 1992). In addition, the majority of rice inventory is owned by rice millers.

Conceptual Framework Development and Hypotheses

Environmental Uncertainty

Uncertainty circle concept in industry is divided into four sides: demand, supply, control and

process that reduce firm’s performance (Childerhouse and Towill, 2004). Similarly, three main

Rice

Suppliers

(Farmers)

Medium

mills

Exporters

(Packaging

International

Customers

: Rice Milled flow

: Information

Retailers/

Local rice

milled

merchant

Domestic

Customers

Small

mills

Paddy

merchantLarge

mills

: Paddy Rice flow

Figure 1: Rice and information flow (modified from

Thai Rice Foundation under Ro al Patrona e 2006

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sources of variability of supply chain are supplier performance, manufacturing process, and

customer demand which force supply chain members to hold safety stock that reduce supply

chain performance (Bhatnagar and Sohal, 2005; Davis, 1993). In contrast, with empirical

approach, supply, demand, competitor, and technology uncertainty are not significantly related

to strategic supply chain management practices (Li, 2002) and might not be linked to their

performance. The sources of uncertainty of agri-food business can be “perish ability ofproducts, variable harvest and production yields and the huge impact of weather conditions on

customer demand” (Jack G. A. J. van der Vorst and Beulens, 2002, p.415). However, some

uncertain factors in agri-business are not different from previous studies. With case study

approach, supply, demand and distribution, process, and planning and control uncertainties

lead to poor agri-food supply chain performance by influencing firms to contain non-value-

adding activities such as safety buffers in time, capacity and inventory in food industry (J. G.

A. J. van der Vorst, 2000). Consequently, it should be investigated whether supply, demand,

process, planning and control, and competitors’ action uncertainty affect on Thai rice miller

performance.

Government regulation can provide both risks and benefits to business in many ways. Forinstance, the changes in government policy in Philippine agri-industry leading to uncertain rice

of copra injure economic performance of copra market (Mendoza and Farris, 1992).

Government policy has played an important role in Thai agricultural production as well

because agricultural products are very crucial in Thailand as discussed in previous section.

Thai government intervene agricultural production in many views such as export rice tax

(Roumasset and Setboonsarng, 1988) and reducing rice capacity during 1993-96 that could not

bring benefits to farmers (Yao, 1999). The main law that is involved paddy rice trade in

Thailand is the paddy rice mortgage scheme that annually and unpredictably set paddy rice

price (Department of Internal Trade, 2008). Indeed, government policies of developing

countries are turbulent and unpredictable (Badri, Davis, and Davis, 2000).

In term of climate uncertainty, by 2100, several international climate models predict that there

will be an increase in incidences of floods in Thailand (Yoshida, 1981) that can reduce

productivity of rice production. According to historical data, there are evidences that in

Thailand, drought or/and flood could damage cultivated rice area considerably. In 1919, for

example, the total failed rice area was 43.4% of cultivated rice area caused by drought, and in

1942, 34.3% of cultivated rice area was failed owing to flood (Yoshida, 1981). As the amount

of rainfall in Thailand are predicted t to vary because of climate change, the rice yield might

drop by 20 percent in 2040 in many provinces such as Thung Kula field (Sukin, 2004). These

prior studies strongly support that climate factors significantly affect to rice production in

Thailand. Thus, climate and government policy uncertainty will be considered as the uncertain

factors in performance of rice millers for Thai context.

Efficiency Performance Indicators

Performance measurement of agri-food

supply chain mainly concern on quality

of product and process in specific terms

of freshness (L. Aramyan et al., 2006)

which is distinguishing from general

product. The four categories of

performance indicators of agri-foodsupply chain are (i) Efficiency (ii)

Flexibility (iii) Responsiveness (iv) Food Figure 2: A proposed model of the impact of seven

uncertain factors on efficiency of Thai rice millers.

Supply

Uncertainty

Demand

Uncertainty

Process

Uncertainty

Planning &

Control

Efficiency of

Thai rice

millers

Competitors’

action

Government

Intervention

Climate

Condition

H1

H2

H3

H4

H5

H6

H7




quality (L. Aramyan et al., 2006; L. H. Aramyan et al., 2007; Luning, Marcelis, and Jongen,

2002). Efficiency of agribusiness is measured by transaction cost metrics and product

realization cycle metrics, profitability requiring accurate financial information, distribution of

return reflected in quantity and quality and chain responsiveness measured in order fill rate, on

time delivery etc. and dealt with total information transparency (Wysocki, Peterson, and Harsh,

2006). Likewise, efficiency performance indicators of L. Aramyan et al.

(2006) refer to cost ofproduction, distribution, transaction, profit, return in investment and inventory. According to

characteristics of rice millers in Thailand, the price of Thai rice can not be too high compared

with rice price from their competitors such as Vietnam. Thus, efficiency is one main important

performance indicators for Thai rice millers. Thus, all seven uncertain factors are hypothesised

to be negatively related to efficiency of Thai rice millers as presented in Figure 2.

Methodology and Results

Survey Instrument Development

The instrument employed to test the hypotheses was a postage questionnaire in Thai languageto rice millers. The questionnaire is concerned with the seven uncertain factors and efficiency

performance. All theses questions are perceptual and apply a 7-point Likert scale with end

points of ‘strongly disagree’ and ‘strongly agree’ that is useful for empirical research related to

managerial evaluation (Feldmann and Olhager, 2008). To measure uncertain factors in

organization, the perceptions of them are considered because managers make decisions on their

perceived factors leading to be more crucial than the objective uncertain factors (Bourgeois,

1980; Duncan, 1972). The subjective ratings of efficiency are sufficiently valid indicators of

actual performance because they were significantly corrected with the actual values (Vickery,

Calantone, and Droge, 1999). In the stage of scale development, supply, demand, process, and

planning and control uncertainty are concerned in three aspects: quality, quantity and time (e.g.

rice quantity, quality and delivery time from rice producers are unpredictable). Meanwhile,competitor uncertainty was measured within three aspects: their actions, competition in

domestics and in international market (Li, 2002). Government policy uncertainty measurement

was generated in four aspects: intervenient policy on rice production, trading, paddy rice

mortgage scheme, and any new government regulations (Badri et al., 2000; Bran and Bos,

2005; Javidan, 1984). Ultimately, climate uncertainty that is related to rice production is

monitored in three aspects: drought, flooding (both in terms of occurrences and duration), and

warmer temperature (Cruz et al., 2007). Efficiency is measured in of cost, return in investment

and profit (L. Aramyan et al., 2006; L. H. Aramyan et al., 2007; Luning et al., 2002; Wisner,

2005).

Pilot testing of ten managers of rice millers lead the questionnaire to easily be completed,

comprehensible, and unambiguous for the respondents’ range of knowledge and responsibility

as well as establishing content validity (Flynn et al., 1990). Construct validity then was

measured by explanatory factor analysis with 0.55 cutting-off point of factor loading due to

102 sample size (Hair et al., 1995, p.385). Reliability was tested by using Cronbach’s α

(Cronbach, 1951) with 0.6 cutting-off point (Nunnally, 1967), and Corrected Item-total

Correlation with 0.3 cutting-off point (Andy P Field, 2005) as shown in Table 1.

Sample

The final draft of questionnaire was then mailed to 698 rice mill companies all aroundThailand, but 46 questionnaires were returned due to, for instance, incomplete address, or

business failure. 112 questionnaires were returned, but 10 of them were abandoned due to

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incomplete information, resulting in an effective response rate 14.61% that is considered

generally for survey in developing country (Ahmed et al., 2002). The final sample included

7.84%, 28.43%, and 61.765% of small, medium, and large milling capacity respectively.

16.67% are both rice millers and rice exporter, and 79.63% are only rice millers. Additional,

62.96% have joined paddy rice pawn policy of government for last 5 years, and 31.48% have

not. The average amount of paddy rice milled in each firm is 21,456.27 tonnes per year. Theaverage inventory of paddy rice in each firm is 7,555.92 tonnes per year.

Hypothesis Testing

Multiple regression analysis was undertaken to test the relationship between seven uncertain

factors and efficiency performance indicators. Four measurement items of efficiency

performance were combined into a factor score as a dependent variable. The raw scores of 25

measurement items of uncertain factors are used as independent variables because a raw score

solution is more appropriate than a factor score regression that can lead to the loss of important

predictors due to consistently low communalities (Dobie, McFarland, and Long, 1986). As a

enter method was conducted because the literature analysis indicates that all independentvariables are important indicators (Andy P. Field, 2009), the results show that six independent

variables significantly contribute to the model with t-test at significant level 0.05.

Table 1: Construct validity and Reliability of Measurement Scale

1 items are deleted when their Corrected Item-total Correlation are less than 0.3 or 0.55 cut-off factor loading

Efficiency Supply Demand Process Planning

and Control Competitor

Government

policy Climate

(α = 0.842) (α = 0.719)(α = 0.779) (α =0.667) (α = 0 .6 6) (α = 0 .683) (α = 0.857) (α = 0.945)

EF1 0.8039 0.8611 None

EF2 0.9080 0.8609

EF3 0.7344 0.7098

EF4 0.5620 0.6760

SU1 0.6929 0.7363 None

SU2 0.7353 0.6171

SU3 0.7979 0.6068

SU4 0.7200 0.7134

DU1 0.7199 0.6794 None

DU2 0.8149 0.6689

DU3 0.8403 0.6761

DU4 0.7268 0.6083

PU1 0.8152 0.5880 PU2

PU3 0.7853 0.7727

PU4 0.7378 0.7951

PCU1 0.7278 0.8492 PCU4

PCU2 0.7998 0.7711

PCU3 0.7954 0.7689

CU1 0.7914 0.8481CU3, CU4

CU2 0.8714 0.8090

GU1 0.8176 0.7944 None

GU2 0.7942 0.8539

GU3 0.8836 0.7975

GU4 0.8622 0.7726

CMU1 0.9304 0.9136 None

CMU2 0.9024 0.8730

CMU3 0.9360 0.8623

CMU4 0.9133 0.8117

CMU5 0.8459 0.7532

Particular

items

deleted1

Commu

nalities F a c t o r s

Items

P l a n n i n g

& C o n t r o l

C o m p e t i t o r

G o v e r n m e n t

P o l i c y

C l i m a t e

S u p p l y

D e m a n d

E f f i c i e n c y

P r o c e s s




The result is based on multi-regression analysis as shown in Table 2. The hypotheses linking

demand uncertainty (H2 /DU4: master production schedule has a high percentage of variation in

demand), process uncertainty (H3 /PU1: yield of rice processing can vary), planning and control

uncertainty (H4 / PCU1: information of stock level of rice and rice production capacity is

complete at this moment), and competitor uncertainty (H5 /CU2: competitors often introducenew price unexpectedly) to efficiency were significant in negative effects. Surprisingly, the

hypotheses linking competitor uncertainty (H5 /CU1: Competitor’s actions are unpredictable),

and government policy uncertainty (H7 / GU4: The new government regulation is introduced

unexpectedly) has a significant negative impact on efficiency.

Table 2: ANOVA and Regression solution of six independent variables significantly contribute

to the model with t-test at significant level 0.05.

Conclusion and Discussion

The empirical approach of this study signifies that unexpected new price introduction from

competitors has the greatest pessimistic influence on efficiency of Thai rice miller. This finding

highlights that there is intensive price competition in Thai rice market, and support the study of

Min & Mentzer (2000) that uncertain competitors’ actions are able to affect on business

performance. The fluctuated demand in master production schedule also decrease efficiency of

Thai rice millers. This findings reinforce the previous study that demand uncertainty is able to

reduce efficiency by leading to higher unstable of inventory level, higher delay of delivering

finish good to customers and finally lower efficiency in every business (Ettlie and Reza, 1992;

Jack G. A. J. van der Vorst and Beulens, 2002) as well as agribusiness (J. G. A. J. van der

Vorst, 2000). Moreover, this enhance the author’s comprehension that fluctuated demand in

master production schedule referring to uncertain in any particular customer orders such as

quantity, or delivery time is the key cause to reduce efficiency compared to other aspects of

demand uncertainty (e.g. lead time of customer order etc.). Likewise, varying yield of

processing is one part of process uncertainty that is enable leading to uncertain production

cycle time (Childerhouse and Towill, 2004) and then increase any particular cost such as

production and inventory cost. Undoubtingly, incomplete information bring low performanceto organisations (Childerhouse and Towill, 2004) because the higher quality of the information

inputs, the higher quality of managerial decision making (Gorry and Morton, 1989).

ANOVAa

Sum of Squares df Mean Square F Sig.

Regression 43.84 25.00 1.75

Residual 57.16 76.00 0.75

Total 101.00 101.00R = 0.659

b, R Square =0.434, Adjusted R Square = 0.248

B Std. Error Beta

DU4 -0.198 0.083 -0.356 -2.378 0.020

PU1 -0.209 0.091 -0.386 -2.310 0.024

PCU1 -0.264 0.125 -0.358 -2.114 0.038

CU1 0.413 0.115 0.623 3.601 0.001

CU2 -0.229 0.101 -0.390 -2.272 0.026

GU4 0.338 0.156 0.515 2.172 0.033aDependent Variable: Factor score of Efficie ncy PerformancebPredictors: (Constant), C MU5, CU1, PCU3, PU4, DU1, PCU2, SU2, GU1, PCU1, PU3,

DU4, DU2, SU1, DU3, SU3, SU4, CU2, GU3, PU1, CMU4, GU2, CMU2, GU4, CMU1, CMU3cAll coefficient presen ted in thi s table have a P-value < 0.05

0.003b

t Sig.cVariables Unstandardized Coefficients

Standardized

Coefficients

2.3320

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Interestingly, the study provides support for the positive relationship between competitors’

action uncertainty and unexpected new regulation introduction from government policy, and

efficiency performance. These issues should be addressed as the future studies in deeply

investigation such as qualitative research method such as focused group study to clearly

understand that relationship. The limitation of the study is simple size. 102 sample sizes isconsidered that less statistical power because the minimum ratio of observations to

independent variables is 5:1 (Hair et al., 2010, p.175). As there are 29 independent variables of

environmental uncertainty, 145 sample sizes should be reached to validate the generalizability

of the results.

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