task environments and performance

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Task Environments and Performance

—The Case of Telecommunication Industry in Asia Pacific Countries—

Norlia AHMAD*Graduate School for International Development and Cooperation (IDEC),

Hiroshima University

MOKUDAI TakefumiCenter for Research of Regional Economic Systems,

Faculty of Economics, Hiroshima University

Abstract

This research provides an in-depth analysis of the most influential industry wide factors of the tele-communication industry, which has experienced enormous changes, resulting from deregulation poli-cies and technological advancement. Particularly, it points toward the determination of how changes inenvironmental forces and the interaction between them may create different conditions of task environ-ment for firms competing within the same industry across countries under study. Further, we identi-fied dimensions of task environment, which captured market concentration and task ambiguity deter-mined at different stages of industry development, and finally their influence on performance. Theempirical analysis indicates a statistically significant relationship between the different combination ofmarket concentration and task ambiguity and the variations of performance outcomes. Our results sup-port the classical model of the influences of industry structure on performance and offer significanttheoretical implications on how the performance of telecommunication industry was influenced by tech-nological innovation and deregulation policies through the changes in market concentration and taskambiguity. Notably, this research attempts to fill the need for more empirical studies and to quantifyeach of the industry forces, which is crucial to developing deeper knowledge of key environmental andstrategic issues affecting the industry in question.

Keywords: Market Concentration, Task Ambiguity and Industry PerformanceJEL Classification: L1, L16, L19, L96

*Corresponding author:Address: 1-5-1 Kagamiyama, Higashi-Hiroshima739-8529 JAPANTel: +81-905-702-4406; Fax: +81-824-23-0494Email address: [email protected] (Norlia A.)

1. Introduction and Objectives of theStudy

An essential part of the environmental analysistask of strategic planning is the analysis of indus-try factors that contribute to the changing envi-ronment in which firms compete. The centralthrust of any environmental analysis is identifying

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industr y wide factors, which influence perfor-mance and profitability rates among firms.

In this research, we attempt to investigate thelinkages between the most influential industryfactors on performance taking the case of thetelecommunication industry. For the past twodecades, the telecommunication industr y hasreceived great attention from economic and man-agement scholars due to its increasing economicand strategic importance. Within those periods,the telecommunications market has experiencedsubstantial changes resulting from deregulationpolicy coupled with technological advancement.

Prior to deregulation era, in most Asian coun-tries, telecommunication industry was composedof telecommunication carriers, which were stateowned monopolies. Basically they had full con-trol over the infrastructures and services in theircountries. This monopolized market was eithervertically integrated with their equipment suppli-ers or connected with a group of both competitiveand cooperative suppliers. At that time theindustry was considerably efficient in the sensethat recurrent cost decreases were echoed on thefinal market by decreases in prices. On the spe-cific domain of the technology of telecommunica-tion networks, it was highly innovative throughthe intensive technological competition betweenresearch laboratories that linked directly to thetelecommunication operators.

In 1990s, the telecommunication industr yincurred two main changes; first, the privatizationof incumbent firms followed by market liberaliza-tion that led to the changes in market structure,and second, the new technology developmentthat related to task ambiguity. Thus, we observethe net effect of these technical changes was anincrease of task ambiguity in the telecommunica-tion industr y under study. The net ef fect ofprivatization and market liberalization is adecrease of market concentration in theindustry. These changes call for the need to re-define industry structure and to further explorethe fundamental aspect of its impact on perfor-

mance.The analyses of the performance determinants

of the telecommunication industry need to con-sider the most influential evolving strategic issuestaking into account the new competitive land-scape of its nature in order to provide a soundtheoretical base and to avoid us missing impor-tant perspectives and results. Many of the previ-ous studies1 account for industry effect throughcross-industries analysis, but did not clarify theeffects of sub sectors influences in one particularindustry and what really constitute the industryeffect. Therefore there is a need to focus on oneparticular industry to allow deeper examinationon each factor, which constitutes the industryeffect and offers more control over sub industrylevel influences.

This research is aimed at providing insightsinto the relationship between task environmentand performance of the telecommunication indus-try in 15 Asia Pacific countries. The reasons forselecting these countries are as follows; first, theyare the countries with the most substantialgrowth in the telecommunication industry in theregion, second, the telecommunication industryin those selected countries are those with consid-erable transformation of industr y structureresulting from government policy of marketderegulation, and finally, referring to the first rea-son, the availability of time series data are onlyavailable for higher performances of telecommu-nications.

Particularly, it points toward the determinationof how the changes in environmental forces andthe interaction between them may create dif fer-ent conditions of task environments for firmscompeting within the same industry across coun-

1 For example, empirical studies on the significantinfluences of industr y and firm ef fect on per for-mance conducted by Schmalensee (1985), Rumelt(1991), Roquebert et al. (1996), McGahan and Por-ter (1997), and Chang and Singh (2000) show thatindustry effect accounts for about 20% in explainingfirms profitability and firm’s level factors comprise of32% of firm’s performance.

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tries under study.The theoretical reasoning upon which we de-

velop our case is based on the research frame-work in figure 1.

their influence on performance.The remaining part of this paper consists of lit-

erature review and theoretical background,model development and research hypotheses,empirical analysis, and finally the discussion offindings and conclusions.

2. Literature Review and TheoreticalBackground

Theoretically, firms’ per formances dependjointly on their strategy and the influences of theindustry forces and market structure in whichthey compete. This is consistent with the theoryof Structure-Conduct-Performance (SCP) model,which states that industry structure drives firmconduct and in turn firm performance. The SCPmodel is designed to study the industry struc-ture, identify all the important linkages in thetask environment and measure or at least com-prehend the form, content and magnitude of thelinkages. In the context of telecommunications,the subject of IO studies such as the industrystructure, regulatory context and proportion ofownership divested has been widely discussedamongst economists, telecommunication scholarsand industry authorities2.

From a strategic management perspective,industry factors that influence the performance ofan industry and firms operating within the sameindustry can be categorized as; First, the externalfactors that consist of general environmentinclude a broad range of Political, Economic,Socio-cultural and Technological factors, oftencalled PEST analysis. Second, the specific ortask environment includes customers, suppliers,distributors and competitors that have a directef fect on the firms. In theoretical (IO) litera-ture, specific environments are sometimereferred to industry forces. Porter (1980) illus-

The research framework is composed of twoconstructs.

First, task environment, which is viewed fromtwo dimensions; the market concentration andtask ambiguity. These two dimensions will bedetailed in the next section. The task environ-ment concept has been explored in most of theIndustrial Organization (IO) literature in charac-terizing market structure and the nature of com-petition in a particular industry. Our reasons forintegrating these two perspectives of task envi-ronment analysis are due to the complex natureof the telecommunication industry that involvemarket regulation and rapid technologicaladvancement that lead to changes in task environ-ment.

Second, the performance, and we propose toexamine certain performance variations due todifferences in task environment represented bydifferent stages of industry development model.

There are three main objectives to beaddressed in this research, which are:

1. Illustrating how task environments are likelyto be af fected by changes in the generalenvironment.

2. Defining task environment based on theextent of market concentration and taskambiguity.

3. Investigating the relationship between taskenvironment (combinations of market con-centration and task ambiguity) and finally

2 See e.g. Bruno (2002), Bernardo et al. (2001), Noll(2000) and Boubakri and Cosset (1998) on empiricalstudies of the transformation of the telecommunica-tion industry in developing countries.

Figure 1 Research Framework

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trates the Five Forces framework for industryanalysis, which consists of threats of newentrants, bargaining power of buyers, bargainingpower of suppliers, threats of substitutes andintensity of rivalry or industry competitors. Hisframework is used for the identification of keyfactors affecting performance and the determina-tion of how changes in the industry environmentmay affect firms’ performance.

In this research, we employ the word ‘task’environments following the conceptual definitionthat commonly used in IO literature to represent‘industry’ environments. Customers, suppliers,potential entrants, and substitute or complemen-tar y products are all market factors in theindustry environments that may be more or lessprominent or active depending on the nature ofthe industry.

Typically, PEST factors are viewed as havingno direct linkage to the organization. However,in the case of the telecommunication industry,regulatory and technological factors have beenthe most influential elements that result in turbu-lence changes of task environment. Therefore,examining how deregulation and technologicalchanges influence the task environment dimen-sions, in our case the concentration of marketand task ambiguity is significant in understandingthe overall phenomenon in the telecommunica-tion industry.

Moreover, given the nature of the telecommu-nication industry which is highly regulated andfacing rapid technological development, there isstill lack of an industry dynamism model thatcould explain industry performance, taking intoaccount the new landscape of industry structure,which constitutes the real boundaries of competi-tion for firms competing in convergence industryof the new economy. In other words, the usualexisting framework adapted from the industrialorganization (IO) model, where an industry isdefined as a group of firms or business units pro-ducing close substitutes (e.g., Porter, 1980) canbe further reconsidered to adapt to the new land-

scape of competition. In view of the evolution ofmodern competition in a technologically drivenand government intervention of telecommunica-tion industry, a more logical way of defining therelevant industry forces could provide rigorousunderstanding of its per formance determi-nants.

The concept of redefining industry structureindicates that in the case of industry convergenceand pace of highly environmental changes havegreatly reduced the utility of traditional IO modelthat is based on stable industry (Sampler, 1998).His argument is supported by Bettis (1998) whocalls for strategic management scholars to con-sider alternative units of analysis besides theusual units of analysis: businesses unit, industryand firm or otherwise labelled, the ‘usual suspect’when talking about strategy or per formancevariations. He emphasised that,

“... The usefulness of units of analysis are notkeeping up with sophisticated tools and analyticalconcepts, nor with the actual nature of competitionand strategy in the late twentieth century...”(Emphasis on original).

Consequently, more ef for ts to redefine thenature of industry boundaries and its competitiveenvironment in a par ticular industr y shouldreceive great attention for current studies.

Furthermore, previous studies in IO literaturepoint that the industry structure is the centraldeterminant of firm performance and in the fieldof strategy, strategic management scholarsargued that firm profitability depends on theamount of value a firm can create relative to itsrivals which is more important than the industryforces. Our attempt in this research is to exam-ine the former and further clarify the significantindustr y forces that constitute the industr yeffect. The main idea is not to continue debat-ing the issue of whether performance is drivenmainly by industry or firm factors since manyprevious findings on the same stream havealready proved that both play relative roles affect-ing performance. Instead, we emphasise on the

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significant influences of industry wide factors thatlead to changes in task environment, the associa-tion of two dimensions of the task environment,which are the market concentration and taskambiguity with the evolutionar y stage of theindustry development, and finally their influenceson industry performance.

Nalebuff and Bradenburger (1996) proposedthe concept of ‘Value Net’, which represents allthe players in the industry and the interdepen-dencies among them. Similar to Porter’s FiveForces framework of industry analysis, Value Netconsists of customers, vendors and substitutors.However, to the contrary, it gives another pointthat is often overlooked in traditional strategicanalysis, which is the complementor. In the con-text of the telecommunication industry that em-braces network systems of production and distri-bution, a new perspective and method of industryversus performance analysis should be devised inresponse to seminal industry forces. As Shaw(2001) indicates;

“... We are now witnessing a chaotic reorderingof industry and competitor rivalries: it is sometimesmore advantageous to pursue cooperative ventures,rather than direct conflict with competitors...”(Emphasis on original).

Simply put, in certain industries that are char-acterized as highly capital intensive and techno-logical oriented, firms can do better to think ofthemselves as complementor and competitorbecause it is better to increase the size of the pierather than competing over the slices. This isparallel to the concept of ‘Co-opetition’3. In thereality of telecommunications, there is coexist-ence between competitive and cooperativeelements. Therefore, it is significant toemphasise the contribution of complementaryproducts in analysing the competitive environ-ment of the telecommunication industry. In thisresearch, we incorporate the influences of othersub-sectors, which have strong linkages with tele-

communication industry such as Internet, com-puter, and broadcasting sector. The key issue isthe balance firms take between industry advo-cacy and pursuing individual success becauseboth are necessar y for industr y sur vival andgrowth.

Finally, as the industry progresses, we couldobserve various stage of industry development orevolution. At different stages, different perfor-mance outcomes are expected due to externalforces operating to affect the task environment inwhich firms operate. According to the theory ofindustry evolution, the model states that marketstructure is intrinsically connected to each stageof its development in explaining firms’ behaviourand industry performance.

Thus, this research attempts to determine howchanges in the external environment may affecttask environments and in turn performances atthe different stages of industry development. Asthe industry development evolves, the number ofnew entrants into the industry begins to alter themarket structure and lead to a more competitivemarket, which could result in performance varia-tion.

Theoretically, firms face dif ferent strategicissues at different stages of industry developmentand they need to respond accordingly to thechanging environment to ensure higher perfor-mance.

As pointed out in SCP model, market structuredetermined conduct (strategy), which in turndetermined performance. In part, firms’ perfor-mance also results from their own strategicchoices and resources. In conclusion, our argu-ment is that conduct merely reflected the envi-ronment in which firms compete.

Therefore, the analysis of this research disre-gards conduct and look directly at the industryenvironment and market structure in trying toexplain the performances of the telecommunica-tion industry.

3 See Nalebuff and Brandenburger (1996).

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3. Model Development and ResearchHypotheses

Based on the literature review, the theoreticalbackground and in association with the discussedresearch issues and objectives presented in theprevious section, we present arguments to fur-ther develop the conceptual model and generatehypothesizing relationships. As mentioned, thebase of the conceptual model is task environmentand how differences in task environment affectperformance.

Subsequently, founded on the development ofthe conceptual model and the argument madehere, hypothesized relationships among con-structs will be presented. The relationshipbetween dimensions of task environment, marketconcentration and task ambiguity, and perfor-mance as the basis of our research is given in fig-ure 2.

3.1 Relationships among ConstructsAs mentioned, firms and their task environ-

ment are linked together within the context of thegeneral environment. This research identifies anumber of exogenous factors deriving from the

general industry wide factors that could createcer tain task environments in one par ticularindustry. For instance in case of the telecommu-nication industry, the technology scale indivisibil-ity and regulation factor make it difficult for newentrants to enter the industry and resulted inhigh market concentration at the first stage ofindustry development. Hence, it is essential tofurther identify the most influential macro envi-ronmental factors in PEST analysis, their impacton task environment and in turn performance oftelecommunications. These factors are viewedto influence the concentration of the market andtask ambiguity of the telecommunicationindustr y. For that reason, we derive factorsfrom PEST analysis for the purpose of operation-alizing the task environment variables.

The central focus begins with the analysis ofthe task environment of the telecommunicationindustry, which consists of market concentrationand task ambiguity. The concept of task envi-ronment can be applied to an industry or at thefirm level. Market concentration is consideredhigh when the productions of products or ser-vices are in the hand of two or three producers ofequal size or of one dominant producer, the

Figure 2 The hypothesized relationships

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monopolized market. In this case concentrationis high such as in the telecommunication industryprior to market liberalization where telecommuni-cation products and services were concentratedon monopoly operator.

The concepts of market concentration includethe most significant industry wide forces in thetelecommunication industry, following Porter’sfive forces framework of industr y analysis.However, as mentioned in the literature review,we emphasise on three main forces, which arecompetitor concentration, buyer concentrationand complementors. Competitor concentrationreflects the number of competitors and internalrivalry in one particular industry. Buying powercapture the changes in demand or market condi-tions as telecommunications and finally thecomplementary product and services constitutethe sub-sector forces, taking into account thestrong linkage of telecommunication industry andother Information Technology (IT) sectors suchas internet, broadcasting etc. Furthermore, weexamine each indicator of the sub sectors men-tioned as complementary products, which areviewed as important variables of market concen-tration.

Task ambiguity refers to the degree of uncer-tainty in the task environments resulting fromderegulation policies, convergence issuescoupled with the development of telecommunica-tions system technology such as the optical fibre,microcomputer etc. Whether the degree of taskambiguity is high or low depends on the techno-logical changes in the telecommunication indus-try development and how firms respond to newtechnology through their strategic investmentand alliances formation. In the case of the tele-communication industry, development of opticalfibre, microcomputer and other telecommunica-tions system technology are vital factors thatinfluence the task ambiguity.

To determine task environment, there are fourpossible combinations of market concentrationand task ambiguity characteristics that can influ-

ence performance; first, high market concentra-tion, and low task ambiguity; second high marketconcentration and high task ambiguity; third, lowmarket concentration and high task ambiguity;and forth, low market concentration and low taskambiguity. The latter is not included in theanalysis since this research only focuses on theperiod from 1991–2000.

In the context of this study, we considered thetelecommunication industry as highly technologyoriented industr y and assumed that only atthe first stage of the industr y development(especially for the developing countries), taskambiguity could be low, while once the industryundergoes the process of deregulation and liber-alization, task ambiguity should remain high.Our argument is that, the process of marketderegulation was an action taken by the telecom-munication authority in each country in respond-ing to the new development of telecommunicationtechnology, for instances, the introduction of sec-ond generation (2G) of mobile cellular technol-ogy and optical fibre optic by mid 1990s in mostAsian countries. Therefore, during those periodtask ambiguity was high and we assume that theindustr y at that time has not yet reached thestage of maturity when the introduced technologybecome routinized or less ambiguous (consider-ing the period of analysis which only covers theperiod of 1991–2000). Apparently, task ambigu-ity and market concentration will be low when thetelecommunication industry reaches the maturitystage of its evolution.

Finally, we incorporate the industry develop-ment model, in which task environment is intrin-sically connected to each stage in explainingfirms’ behaviour and industr y per formance.Our reason for that is to better explain howchanges in task environment affect performanceat the different stage of industry development orevolution. Since our period of analysis does notfocus on the total period of industry life cycle, theconceptual definition for each stage of industrydevelopment is represented by the combination

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of market concentration and task ambiguity asfollow: Stage 1, when high market concentrationand low task ambiguity, Stage 2 when high mar-ket concentration and high task ambiguity andStage 3 when low market concentration and hightask ambiguity.

The hypothesized relationships between combi-nations of market concentration and the taskambiguity category, and performance are as fol-lowing;

a) High market concentration and low task ambi-guity lead to lower performance:

At the first stage, there is usually no element ofcompetition due to monopolized marketcondition. Therefore there is high marketconcentration. Task ambiguity is low becausethe industry is basically at the beginning stage ofdevelopment; prospects for new technology oftelecommunication product development andinnovation are low. Since at the first stage, firmsneed to invest in infrastructure, and networking,the revenue is often less than expenditure, this inturn, leads to lower performance.

b) High market concentration and high taskambiguity lead to moderate performance:

At the second stage, task ambiguity can behigher due to the prospect of new technology.Consumer’s knowledge and expectations arehigher since the products are commonly knownin the market. The performance is basically ataverage level, because the firms have to adapt tonew technology, which requires strategicinvestment. In addition, with a more or lessmonopolized market, the capacity to expand islimited and firms cannot meet the excessive mar-ket demand. Thus, it results in moderate perfor-mance.

c) Low market concentration and high task ambi-guity lead to high performance:

At the third stage, after market liberalization,market concentration is low, since there are more

players in the market. Task ambiguity remainshigh due to changes in consumer preferencesand technological development. At this stagethe typical strategic options constitute the ele-ments of competitive strategy, innovation, and dif-ferentiation to of fer distinctive services to thegrowing and changing market demand.However, in the context of the telecommunicationindustry, we could also observe the mixture ofcooperative and competitive elements. The rea-son is due to the nature of the telecommunicationservices that have strong linkages with other sub-sectors such as the Internet, broadcasting andcomputer industry, and the presence of industryconvergence among the mentioned sub-sectors. Per formance, especially industr yperformance, is expected to be high since themarket is getting more competitive, and the sizeof the pie is getting bigger.

3.2 Research HypothesesH1: The combination of high market concen-

tration and low task ambiguity, which isrepresented at the first stage of industrydevelopment leads to lower performance.

H2: The combination of high market concen-tration and high task ambiguity, which isrepresented at the second stage of indus-try development leads to moderate perfor-mance.

H3: The combination of low market concentra-tion and high task ambiguity, which is rep-resented at the third stage of industry de-velopment leads to higher performance.

4. Empirical Analysis

As mentioned earlier, the research issues andobjectives are first, to illustrate how task environ-ments are likely to be affected by changes in thegeneral environment, second, to define the taskenvironment based on the extent of market con-centration and task ambiguity; and finally, toinvestigate the influences of different combina-

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tions of market concentration and task ambiguityon performance outcomes. The following sec-tions present, first, key concept, variables, dataset constructs and its standardization; second,indexation of market concentration and task am-biguity; third, determination of stages of industrydevelopment representing the task environment,which is derived from a combination of marketconcentration and task ambiguity and finally,their influences on performance.

4.1 Key Concept, Variables, and DatasetConstructs

As mentioned, the key concepts and variablesregard two main concepts, which are task envi-ronment and per formance. Key concepts fortask environment as shown in table 1(a) derivedfrom the analysis of PEST (Political, Economical,

Socio-cultural, and Technological factors), and in-dustr y forces (Competitors, Customers andComplementary Product). PEST variables andindustry forces variables, eventually, determinetask ambiguity and market concentration.Variables for performance, as shown in table 1(b)regard performance at the industry level, mainlymobile operators. However, in some cases datafor only mobile per formance is not disaggre-gated, which leads us to use performance for thewhole telecommunication industry.

The data used is dataset constructed from vari-ous sources for Asia Pacific telecommunicationsindustry for 15 countries (Australia, China, HongKong, India, Indonesia, Japan, Korea, Malaysia,New Zealand, Pakistan, Philippines, Singapore,Taiwan, Thailand, and Vietnam) during theperiod from 1991 to 2000. The Dataset consists

Table 1 (b) Variables for Performance

Table 1 (a) Key Concepts and Variables of Task Environment

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of indicators for the key concepts of industrywide factors of telecommunications in the generalenvironments (PEST). We further operation-alized; I) task environment variables, which arederived from PEST analysis and are divided intomarket concentration and task ambiguity, and II)the industry performance. Since the dataset aretime series and cross-countries, instead of usingpanel data analysis, we standardize each indicatorin order for them to be comparable in the analy-sis of each case. For example we use telecom-munication revenue as a percentage of GDP andcompare the indicator among 15 developed cases(15 countries through out the period of 1991 to2000) of telecommunication industry. E.g. indi-

cator from Japan in 1993 case is comparable withMalaysia in 1996. The reason for doing so isthat we aim to emphasise the importance of taskenvironment, which should be notably differentin each country and by determining the stage ofindustry development; we basically, have takencare of the time dimension. That being said,while standardizing the variables we considersome variables that might be influenced by aspecific countr y’s time dimensions and tr y toeliminate them. As a result, indicators afterstandardization are used in the analysis. Thestandardized indicators for variables and theirsources are shown in table 2, and Table 3 showsthe descriptive statistics of all variables used in

Table 2 Variables and Their Standardized Indicators

Concepts Variables Standardized Indicators Sources

Telecommunication Telecom Revenue Telecom Revenue to GDP

Industry’s Telephone lines and Telephone lines and cellularPerformance cellular subscribers subscribers per 100 population

PerformanceMobile Revenue Mobile Revenue to GDP

Mobile Operators’Mobile penetration, Mobile penetration rate (per

Performancenumber of mobile 100 inhabitants)

subscribers

Number of competitorsNumber of service providers (Mobileoperator) Per million population

Dummy after (before) first newCompetitor Liberalization effect entrant (1 for after liberalization

concentration and 0 for before liberalization)

Effect from emergingDummy for new entrants (0,1)

competition treat

Market Buyer GDP Per Capita Income Constant Price (US$)

Concentration concentration GDP Growth rate GDP Growth rate

Task Internet subscribers Internet users per 100 populationEnvironment (ITU estimates)

ComplementaryPC owner

Personal computers per 100 populationProduct (ITU estimates)

Main line availabilityMain line penetration rate(per 100 inhabitants)

Dummy after (before) privatizationsPrivatizations effect (1 for after privatizations and

0 for before privatizations)Task Ambiguity

Telecom investmentTelecom investment to Telecom Revenue

Level of technology Percentage of digital network

Yearbook of Statistics:Telecommunication Ser-vices (ITU, 2001) forRevenue and penetra-tion rate, InternationalFinancial Statistics (IFS)(IMF,2001) for GDP, andPopulation

Data file of Asia-PacificT e l e c o m m u n i c a t i o n(CIT, various issues)

International FinancialStatistics (IFS) (IMF,2001)

Yearbook of Statistics:Telecommunication Ser-vices (ITU, 2001)Data file of Asia-Pacific

T e l e c o m m u n i c a t i o n(CIT, various issues)

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the analysis.

4.2 Indexation of market concentration andtask ambiguity

In order to determine whether market concen-tration and task ambiguity are high or low, firstwe have to calculate INDEX for market concen-tration (INDEXMC) and task ambiguity(INDEXTA). The principle of calculatingINDEX is to make reference to the units that per-form best in the data set. Before calculatingINDEX for market concentration and task ambi-guity, we have to consider the concepts andvariables that determine them. The market con-centration is determined based on three mainconcepts, which are competitor concentration(C), buyer power (B), and supplementar y(complementary) product availability (S). Asshown in table 2, there is more than one indicatorrepresenting each concept. We, therefore, also

have to index them. Though we did not sepa-rate task ambiguity into sub section, there arevarious indicators to determine INDEXTA, andwe also have to give accurate weights tothem. We start with the procedures to calculateINDEXMC as follow:

Starting with index for competitor concentra-tion INDEXC, adapting methodology fromWagner et al. (2002), assuming that the index forcompetitor concentration will be calculated for kdifferent individual indicators (in the context ofthis paper, k=3).

Let therefore, the indicator k describing com-petitor concentration for case i (in our case, aspecific total of n = 150 cases) be Ck

i. Based onthis, the next step, the mean value for this vari-able is identified over the whole set of countries:

MeanC E(C , ,...n)k ki i= Œ1 2 (1)

Table 3 Descriptive Statistics

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Subsequently, for each case, a new variablewhich is the score given to each case, SCk

i isdefined according to the following equation:

SC C MeanC / MeanCk

ik

ik k= -[ ] (2)

The value taken by this ratio ranges from nega-tive value to positive value. We then transformthe negative value to be positive value by findingout the minimum value for SCk

, and subtract itfrom SCk

i, to get a positive score PSCki for each

indicators.

SC min (SC , ...n)ki i

ki i= Œ1 2 (3)

PSC SC SCki

ki

kmin= - (4)

Prior to calculating the index of competitorconcentration (INDEXC) for each case, it is nec-essary to adjust the contribution PSCk for the het-erogeneities in the individual variables.Otherwise some variables are mistakenly given amuch higher weight than others. In order to ad-just for the differences in the skewedness of dis-tributions, an adjustment factors are calculatedaccording to the following equation:

AdjC Max [Median(PSC )] / PSC..k

j l kk

jk= >-= 1

(5)

For the calculation of INDEXC, the PSCki for

each case i are then multiplied with correspond-ing AdjCk, and intended weight (wCk), which isshown as followed:

w

w

w

w

kCfor C ,or kfor C ,or kfor C ,or k

====

Ï

ÌÔ

ÓÔ

2 111 32

1

2

3 (6)

Finally, the INDEXC is calculated for each caseaccording to equation (7).

INDEXC PSC Adj wC / Adj C wCi i

k k

i

k k k

i

k= ◊ ◊ÂÈ

ÎÍ˘˚̇

◊ ◊ÂÈÎÍ

˘˚̇= =1 1

(7)

We then go on calculating index for buyer

power (INDEXB i), and supplementar y andcomplementary product availability (INDEXSi) bygoing through the same process of calculatingINDEXCi. However, since the number of indica-tors for each index and weight is not the same,let therefore, assuming that the index for buyerpower will be calculated for m different individualindicators (in the context of this paper,m=2). Next the index for supplementary andcomplementary product availability will be calcu-lated for p different individual indicators (in thecontext of this paper, p=3).

ww

w

w

w

w

w

m

p

Bfor B ,or mfor B ,or m

and;

Sfor S ,or pfor S ,or pfor S ,or p

===

ÏÌÔ

ÓÔ

====

Ï

ÌÔ

ÓÔ

1 12 2

1 12 25 3

1

2

1

2

3 (8)

Now, let MCqi represent the indicator q describ-

ing market concentration for case i for q =1..q. In the context of this paper, we know that q= 3 which are competitor concentration, buyerpower, and complementary product availability.Therefore, we derived:

MC

q

q

q

qi

i

i

====

Ï

ÌÔ

ÓÔ

INDEXC forINDEXB forINDEX S for

i

123

(9)

Then the index for market concentration(INDEXMCi) can be calculated following the pro-cedures from equation (5) to (7) with no neces-sity to give score to MCq

i. We also give thesame weight to competitor concentration, buyerpower, and complementary product availability(wMCq = 1).

Then the index for task ambiguity (INDEXTAi)also can be calculated by following the proce-dures from equation (1) to (7). In the context ofthis study, let TAr

i represent the indicator rdescribing task ambiguity for case i for r =1..r. And, that r = 5. Weights are given as follow:

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w

w

w

w

w

w

rTA

for TA ,or rfor TA ,or rfor TA ,or rfor TA ,or rfor TA ,or r

=

- =====

Ï

Ì

ÔÔÔ

Ó

ÔÔÔ

3 12 21 31 41 5

1

2

3

4

5 (10)

In order to determine whether market concen-tration and task ambiguity are high or low, we cal-culate mean value of INDEXMC and INDEXTA, if

INDEXMCi and INDEXTAi are lower than theirmean value, we consider them low, otherwise weconsider them as high.

The index of market concentration(INDEXMA), and task ambiguity (INDEXTA) arepresented in table 4(a), and table 5(a), respect-ively. In addition, the classification of marketconcentration and task ambiguity into high andlow are presented in table 4(b), and table 5(b),

Table 4 (a) Index for Market Concentration (INDEXMC)

Table 4 (b) Classification of Market Concentration

―― 80

respectively.From table 4(a), and 4(b), the higher values of

market concentration index (INDEXMA) repre-sent lower market concentration. In somecountry’s cases, their market concentrations areconsidered as low, or high through out the wholeperiod. While in some countries, their marketconcentrations var y. For example, in case ofMalaysia, the liberalization process star ted in1993, resulted in increasing number of players,and market concentration became low. Followedby the Asian Crisis, some mergers betweenweaker telecommunication firms took place,

which once more resulted in higher market con-centration.

From table 5(a), and 5(b), the higher values oftask ambiguity index (INDEXTA) representhigher task ambiguity. We can notice that thetask ambiguity is high almost through out thewhole period for the cases of the countries withmore developed economy (e.g. Australia, HongKong, New Zealand, Japan, and Singapore.)While, in the case of developing countries, thetask ambiguities were getting higher after theintroduction of new technological development,for example the introduction of second genera-

Table 5 (a) Index for Task Ambiguity (INDEXTA)

Table 5 (b) Classification of Task Ambiguity

81 ――

tion (2G) of digital mobile technology by mid1990s in most developing Asian countries.

4.3 Determination of stages of industr ydevelopment, representing task environ-ment

The combinations of high or low market con-centration and task ambiguity determine thestage of industry development, representing taskenvironment as follow:

Stage of industry development =

123

, when INDEXMC is high, and INDEXTA is low, when INDEXMC is high, and INDEXTA is low, when INDEXMC is high, and INDEXTA is low

Ï

ÌÔ

ÓÔ

(11)

Table 6 shows the classification of stages ofindustry development for each case. As noted,in some special cases, we use other logical indica-tors to determine the stage of industry develop-ment case by case, when there is a combinationof low market concentration and low taskambiguity. For example, in the case of Australiain 1991, and 1992, the task ambiguity and marketconcentration were low, we considered that the

period is during the early period of industr ydevelopment and assign stage 1 to the cases. Inthe case of Taiwan in 2000, we give bias towardshigher task environment, since such a timeperiod should be considered as high task ambigu-ity, and therefore, categorized the case as thirdstage. However, there are only few cases whenboth task ambiguity and market concentrationare classified as low.

4.4 Relationships between task environ-ments and performance

Before illustrating the per formance at theindustry level, we need to categorize whether theperformance is high, moderate or low. Let Ps

i

represents the indicator s which are indicators forperformance for case i for s = 1..s. In this casewe employ Telecom Revenue to GDP (P1

i), Tele-phone lines and cellular subscribers per 100population (P1

i), Mobile Revenue to GDP (P1i),

and Mobile penetration rate (per 100 inhabitants)(P1

i). We calculate (Mean Mean(Ps)), and stan-dard deviation of Ps

(Std(Ps)). Categorization ofperformance, where 1 is low, 2 is moderate, and 3is high, is denoted as follow:

Table 6 Classification of Stages in Industry Development

―― 82

Phl; if Psi Std(Ps)/; if Mean(Ps)Std(Ps.) / }; if Psi Mean(Ps) Std(Ps)/

si◊ =

< { }<

> + { }

Ï

ÌÔ

ÓÔ

1 22 23 2 (12)

As shown in table 7(a), and 7(b), we only chooseone performance indicator4 , which is the telecom

revenue to GDP ratios to give numericalexample. The performances are classified to bemoderate when the values of indicators fall on therange of mean ± standard deviation. In the caseof telecom revenue to GDP, the values of its mean± standard deviation are 1.37 and 3.25,respectively. Therefore, we considered thecases, which their telecom revenue to GDP ratiosis less than 1.37 as low. They are considered asmoderate and high for the cases of which the

4 The analysis in the following section used four dif-ferent indicators of performance

Table 7 (a) Performance Indicator (Telecom Revenue to GDP, in percentage)

Table 7 (b) Classification of Performance (Telecom Revenue to GDP)

83 ――

indicators are between 1.37 and 3.25, and thecases, which the indicators are above 3.25 respec-tively.

Finally, after characterizing task environmentaccording to stages in industry development andperformance variation that are categorized ashigh, moderate, and low, we can now considerthe tool to illustrate whether there is significantrelationship between variation in task environ-ment and industry performance.

We choose to perform a cross tabulation analy-sis to illustrate the relationship between variationin task environment and industry performance.Pearson’s Chi-square value (c2

p) will be computedto test for significant of the relationship. Thecomputation of c2

p is as follow:

LetXi be distinct values of row variable arranged in

ascending order: X1 < X2 < ... < XR;Yj be distinct values of column variable

arranged in ascending order: Y1 < Y2 < ... < YC;fij be count of number of cases in cell (i,j),

and;

cj be the jth column subtotal = Â

=f ij

i 1

R

ri be the ith row subtotal = Â

=f ij

j 1

C

W be the grand total = Â

=c j

j 1

C

= Â

=rij

i 1

R

Eij be expected count = ricj/W

In the context of this study, we assign perfor-mance variables categorized as low, moderate and

(a) Telephone lines and cellular subscribers per 100population (ITU)

Count STAGE Total1 2 3

Low 51 19 2 72

Performance Moderate 13 6 16 35

High 1 7 35 43

Total 65 32 53 150

Person Chi-Square 79.7130 (0.000)

(b) mobile penetration rate (per 100 inhabitants)

CountSTAGE

Total1 2 3

Low 45 16 3 64


High 5 24 29

Total 58 32 53 143


Figure 3 Cross Tabulation Analysis for the Relationship between Variation of Task Environment (represents byStage 1, 2 and 3) and Performance, Using Different Performance Indicators.

―― 84

high, ranging from 1 to 3 as the row variables.And, we assign stages of industry development,also range from 1 to 3 as the columnvariables. Let cj be the total count of caseswithin first, second, and third stage of industrydevelopment for j equal to 1, 2, and 3 respect-ively. As well as, ri, which would be the totalcount of cases within low, moderate, and highperformance for i equal to 1, 2, and 3 respect-ively. Pearson’s Chi-square value (c2

p) is com-puted based on the formula in equation (13).

Pearson’s Chi-Square value =

c 2 2

p ( ) /= Â -f E Eijij

ij ij (13)

In addition to the cross tabulation analysis, wealso perform the box-plot for stages of industrydevelopment which representing the combination

of market concentration and task ambiguity asshown in section 4.3 against the non-categorizedperformance indicators.

The cross tabulation results are shown in fig-ure 3(a) to (d), where dif ferent indicators areused as per formance indicators. PearsonChi-Square (c2

p) values and their p-values (inparentheses) indicate a statistically significantrelationship between the different combination ofmarket concentration and task ambiguityrepresented by different stages of industry devel-opment and the variations in per formanceindicators. Our findings support the hypoth-esized relationships mentioned in section 3.

Fig. 3 (Cont.)

(c) Telecom Revenue to GDP

CountSTAGE

Total1 2 3

Low 37 10 4 51


High 3 14 34 51

Total 65 31 52 148


(d) Mobile Revenue to GDP

CountSTAGE

Total1 2 3

Low 19 13 11 43


High 2 7 18 27

Total 31 25 49 105


Note: The stage of industry development represents task environment as follow: Stage 1, when INDEXMC is high, and

INDEXTA is low, Stage 2 when INDEXMC is high, and INDEXTA is high and Stage 3 when INDEXMC is low, and

INDEXTA is high

85 ――

5 . Discuss ion o f F ind ings andConclusions

The focus of this paper was to examine therelationship between task environment andperformance. In order to investigate that issue,the first task was to identify industry wide fac-tors, and how changes in those factors affect taskenvironment in one par ticular industr y.Subsequently, we identified dimensions of taskenvironment, which captured the market concen-tration and task ambiguity in the case of industryunder study, the telecommunication industry, fol-lowed by categorizing the combination of marketconcentration and task ambiguity determined atdif ferent stages of industr y development, andfinally their influence on performance.

Our interpretations of findings discuss thethree supported hypothesized relationships in theprevious section. At the first stage of industrydevelopment, high market concentration and lowtask ambiguity are significantly related to lowerperformance. In the analysis at the beginning oftelecommunication industry development, thereis usually no element of competition due tomonopolized telecommunication market by thestate owned firms. Task ambiguity is low due tothe lack of knowledge and technology used inproducing telecommunication products andser vices. The introduced telecommunicationtechnologies such as the first generation (1G) ofmobile system prior to 1990 were already stan-dardized and matured. Furthermore, at the firststage, consumers had less expectation due to lim-ited knowledge and less familiarity of the tele-communication services. At this stage, firms putmore emphasis on the investment of infrastruc-ture and networking and less emphasises on theR & D aspect and product innovation. Basically,product innovations are mostly taken care by thetelecommunications equipment manufacturers orcooperative laboratory research between serviceproviders and telecommunication manufacturers.In part, the demand for ef ficient and low cost

telecommunication services is higher than everin today’s service-oriented economy, and monopo-lized firms cannot meet an increasingdemand. Therefore, the interpretation is that inmany countries, when telecommunication ser-vices were provided almost total protection frommarket forces prior to the era of market liberal-ization has resulted in lower performance as com-pared to the second stage and third stage of theanalysis.

In the second stage of the analysis, high mar-ket concentration and high task ambiguity aresignificantly related to moderate performance.As the industry progresses, task ambiguity ormarket uncertainties was getting higher due todevelopment of new technology and higher con-sumer expectation. The per formance wasincreased to the moderate level since the indus-try was growing. The issue of market conver-gence puts pressure for telecommunicationservice providers to collaborate with their coun-terparts and other sub sectors firms such as theInternet service provider. However, the fact thatmarket concentration still remained high gavethe limitation for firms to pursue cooperative alli-ances in order to adapt to new technologicaladvancement. In the reality of telecommunica-tion, there is a coexistence of competition andcooperation, however at this stage firms need tobalance between industry advocacy and pursuingindividual successes to ensure industry growthand survival.

As a final point of the relationship between taskenvironment and performance, after the era ofmarket deregulation, low market concentrationand high task ambiguity lead to high perform-ance. The analysis at this stage shows thatmarket deregulation factors have significantlypositive association with higher industry perfor-mance due to the reduced threat of entr y orincreasing internal rivalry among telecommunica-tion firms in each country under study. As aresult, the market size expanded and the natureof interdependency contributed to a bigger size of

―― 86

the market instead of all players competing overtheir own market share. This result supportsthe notion of higher intensity of market competi-tion leads to higher industry performance.

In conclusion, this paper provided empiricalevidence to support the classical model of theinfluences of industry structure on performance.Our analysis showed statistically significant rela-tionships between task environment and perfor-mance in the case of the telecommunicationindustr y. In addition, our detailed analysis,which emphasized the most influential variablesof telecommunication environmental forces, inview of the deregulation and technological factoroffered significant implications for telecommuni-cation authorities and firms to pay more attentionto these crucial industr y forces that evidentlyaffect performance. Clearly, this research dem-onstrated an attempt to operationalize the con-cepts of task environments and industry forces,which often left abstracts, and tested themempirically. Furthermore, considering other fac-tors that were neglected in the traditional IOmodel that were based on stable industry, emerg-ing issues such as industry convergence betweentelecommunication, computer and broadcastingshould be taken into account for the appropriateconceptual definition of industry boundaries andcompetition.

Our study showed an in depth effort to com-prehend the classical model of industry analysis,which paid little attention to the role of govern-mental authority. In the context of the telecom-munication industr y, the government is theregulator, which profoundly affect industry per-formance through deregulation policies.Moreover, the traditional model of industry analy-sis is mostly qualitative for example, high versuslow entry, or high versus low buyer power, with-out showing how to estimate the probability ofentry, thus it is more suitable to assess industrytrends. Notably, this research attempted to fillthe need for more empirical studies and to quan-tify each of the industry forces, which are crucial

to developing deeper knowledge of key environ-mental and strategic issues affecting the industryin question.

In summary, our contributions were first, defin-ing and measuring key concepts constructed; themarket concentration and task ambiguity; thatwere the subjects of this study, and second incor-porating the most significant industry factors in asingle industry to better capture the concepts oftask environment which had a great impact ontelecommunication performance. We therefore,answered most par t of the question of whatexactly constitutes industry effect in the discus-sion of performance determinants at the industryand firm level factors. Future researches, par-ticularly on telecommunications should integratefactors internal to the firms, which have beenargued in previous research on per formancedeterminants that stress on firm strategy, struc-ture and core resources as critical factors affect-ing firms’ performance.

Finally, due to the complex nature of the tele-communication industr y that involves highgovernment intervention and technological ad-vancement which resulted in changes of taskenvironment, coupled with intensive resourcesrequirement of telecommunications industry interms of its capital expenditure, intrinsicresources and strategic options, it is crucial forfurther researches to investigate firms strategicchanges and capability in responding to the tech-nological innovation and market regulation in thecontinuous changes of telecommunication indus-try landscape.

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＊　本稿は、第11回地域経済システム研究会（2002年11月16日）における概要報告と討議に加え、投稿時に２人の匿名レフェリーに

よる査読という要件を満たしたものである。

task environments and performance

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