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Journal of Air Transport Management 17 (2011) 211e214

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Journal of Air Transport Management

journal homepage: www.elsevier .com/locate/ ja ir t raman

Segmenting industrial competitive markets: An example from air freight

Ming-Chih Tsai a,*, Chih-Wen Yang b, Hsiao-Ching Lee a, Ching-Wei Lien a

aNational Chung Hsing University, 250, KuoKuang Road, Taichung, TaiwanbNational Taichung Institute of Technology, 129, Sanmin Rd, Sec. 3, Taichung, Taiwan

Keywords:Industrial market segmentationDisaggregate modelStrategic group

* Corresponding author.E-mail address: mctsai@nchu.edu.tw (M.-C. Tsai).

0969-6997/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.jairtraman.2011.01.001

a b s t r a c t

This paper applies a disaggregated approach to segment industrial markets under competitive structurestaking the air-freight market for the high-technology product industry in Taiwan as a case study. Datafrom firms is used to examine the structure of the air freight industry and we find that carriers areclustered into two strategic groups, express and forwarder. Pricing is a leading strategy recognized bycustomers for forwarders, whereas service punctuality and freight security are the winning strategies forexpress. The high-tech freight market is classifiable by shipment destination and size.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Market segmentation identifies customer groups by needs andwants and is used determine the manner and intensity of whichcustomers and needs are addressed. By limiting redundant effort,market segmentation reduces operational costs and helps to effec-tively allocate a firm’s resources to target markets. In a competitivemarket, both sellers’ strategies and innovations and customers’changing needs have reciprocal effects but exploration of these islimited.

Here we develop a combined disaggregated approach tosegment industrial markets under a competitive structure usinga nested logit model (NL) to cluster strategic groups of supplier inthe air freight market for Taiwanese high-tech industry accordingto customer evaluations, with a latent class model (LCM) deployedto segment the consumer market; high-tech products constitute44.6% Taiwan’s exports.

2. Method

2.1. Model structure

Most disaggregate models target user decision-making acrossa range of service alternatives but they inevitably assume consumesare homogeneous; market segmentation thus is thus impossible.LCM can capture heterogeneity across segments in a target marketand also act as a segment-level analyticalmodel (Wen and Lai, 2010).As a segmentation tool, it is designed to conduct both segmentationand segment-level parameter estimation simultaneously and unlike

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other types of segment-levelmodels, it is inherently data-driven andhence practical (Oh et al., 2003).

Markets, however, have becoming increasingly open to newentrants withmoremany suppliers, andwith themmore attributes,available in a market. Correlating every service alternative tocustomer segments is thus neither practical nor methodologicallyfeasible and as a result, clustering service alternatives has becomenecessary prior to LCM operation. NL models can be used to clusterservice alternatives into a competition structure and then LCapplied to identify market segments. Fig. 1 indicates the modelstructure applied to air freight, high-tech firms in Taiwan (Fig. 1).

2.2. Nested logit model

For a general two-level NL model with groups of air carrieralternatives and Nm alternatives in each group, the utility functionof the NL model assumes:

Uiq ¼ Viq þ eiq ¼ ai þ b0Xiq þ eiq (1)

where, Viq the observed utility iq associated with individual high-tech firm q to air carrier alternative i (i¼ 1,2, I); eiq is the unobservedutility for individual high-tech firm q to air carrier alternative i; Xiq

is vector of micro variables for individual high-tech firm q to aircarrier alternative i; a and b are the constant and the parametervector to be estimated.

The probability that air carrier i in group m is measured as

Pi ¼ Pijm$Pm ¼ expðVi=mmÞPi0˛Nm

exp�Vi0=mm

�$ P

i0˛Nm

exp�Vi0=mm

�!mm

Pm

Pi0˛Nm

exp�Vi0=mm

�!mm(2)

Fig. 1. The model structure.

M.-C. Tsai et al. / Journal of Air Transport Management 17 (2011) 211e214212

where, pi/m is the probability of choosing air carrier alternative iconditional on choosing nest m, pm the marginal probability ofchoosing nest m of which i is a member, Nm is the set of all aircarrier alternatives included in nest m, and mm is the log-sum orinclusive value parameter for a nest.

In practice, the business air carriers are classified as either,forwarders and express service providers. The NL model examinesthis competitive structure based on customer evaluations of theservice strategies provided by a selected carrier. In model testing,the NL model is consistent with utility maximization if the condi-tions, 0< mm� 1 are satisfied for all mm, i.e if mm for all “m”s does notdiffer statistically from unity, then the NL model collapses and triesan alternative structure. After examining all possible nest struc-tures and none is satisfactory, structure allows one to treat the NLmodel as an MNL model.

2.3. Latent class model

A latent class model assumes that the market can be groupedinto several latent class segments on the basis of their preferencesfor observable attributes; it is an analytical method for groupingidentical customers by segmentation variables. The underlyingtheory posits that individual behavior depends on observableattributes and on latent heterogeneity that varies with unobservedfactors. With fixed number of classes, s, the model estimates consistof the class specific parameters and, for each individual high-techfirm, a set of probabilities defined over the classes. The probabilityof alternative i being chosen by high-tech firm q, is thus

PqðiÞ ¼Xss¼1

Pq

�i.s�� HqðsÞ (3)

The probability comprises component pq(i/s) representing theprobability of alternative choice within a segment s and Hq(s) thatcalculates the marginal probability of segment s. As with the utilityfunction of Equation (1), the probability of alternative i withinsegment s being chosen is given by.

Pq

�i.s�

¼ exp�ais þ b0sXiq

��Xi˛C1

exp�ai0s þ b0sXiq

�(4)

In term of segment probability, Hq(s) represents the probabilityof individual q belonging to segments and its formulation can beexpressed as.

HqðsÞ ¼ exp�r0sZq

��Xsexp

�r0sZq

�(5)

s¼1

Where Zq is a vector of segmentation variables consisting of high-tech firm characteristics and rs0 is a vector of parameter for segments, (s ¼ 1.,2,.,S).

3. The data

3.1. Choice set and variables

Taiwan is the leading manufacturer of certain high-tech prod-ucts such as dedicated semiconductor foundries, large-size (over 10feet) TFT-LCD (thin film transistor-liquid crystal display) panels,and IC testing. Due inventory costs of holding high-tech products,firms in Taiwan use air transport for product delivery. The annualmarket was close to 1.8 million tons in 2009 with an annual growthrate of 8% over past 20 years. In terms of destinations, Asia accountsfor 59.6% of the market, followed by North America, 27.2% andEurope, 9.2%.

Express and freight forwarder are options within the air freightmarket but because of freer markets the market structure isbecoming less clear (Lobo and Zairi, 1999). Express firms, includingFedex, UPS and DHL, that handle shipments from origin to desti-nation and deal directly with customers, thus circumventing thetraditional forwarders “own” all their assets, including physicalassets such as trucks and airplanes, labor assets, and informationassets, have engaged in vertical integration (Forster and Regan,2001) and started to encroach on heavier cargo markets (Bowenand Leinbach, 2004). Forwarders provide services through thecoordinated efforts of scheduled airlines and other elements in thelogistics supply chain, mediating between airports and consignorstaking care of ground transport, securing space from an airline, andseeing to customs clearance. Because of lower barriers for marketentry, there are roughly 900 forwarders in Taiwan and these can beclassifies into global and regional providers (Hsu et al., 2005). Someforwarders have, however, changed their structure using IT andmergers and acquisitions to become internationalized andmorphed into integrated logistics service providers that competewith express delivery firms.

To initially identify the strategic and segmentation variablesa focus-group meeting was used. Five senior managers fromforwarders and express firms with long involvement in the high-techmarket were invited. Repeated discussion reconciled divergentopinions and after several iterations of the feedback process sixstrategic variables and five firm-demographic variables definingdifferent levels of categorization were agreed on (Table 1).

3.2. Data collection

The survey instrument consisted of two sections. The firstassessed customer satisfaction levels on strategic variables linkedto the five carrier alternatives, using a five-point Likert scale with“1 ¼ very dissatisfied” and “5 ¼ very satisfied”. The second cali-brated values of the five-macro segmentation variables. A recentshipment contract was identified for survey. To ensure a reasonabledesign, a pre-test study was carried out and minor revisions weremade to the wordings and format.

On December 31, 2008, 613 high-tech firms in the science parkwere contacted by phone to be possibly enrolled as willing toaccept an e-mail survey. The respondent titles included divisionchiefs of logistics management (38.7%), of transportation, (16.0%),and of material and global logistics (34.0%) and deputy generalmanager or above (11.3%). The average work experience in high-

Table 1Alternative nest and segmentation variables.

Sources

Alternatives Express nest: Fedex, DHL, UPS Bond and Morris (2003)Forwarder nest: Global forwarder, Regional forwarder

Strategic variables Pricing strategy Menon et al. (1998)Service strategy e damage claimService strategy e punctualityService strategy e securityService strategy e exceptional managementChannel strategy

Firm demographicsvariables

Shipment destination North America Witlox and Vandaele (2005)EuropeAsia

Shipment Size Small (<100 kg) Tsai et al. (2007);Witlox and Vandaele (2005)Heavy (�100 kg)

Frequency of shipment Less-frequent (<5 times per month) Bond and Morris (2003)Frequent (�5 times, per month)

Time in transit Speedy service (<3 days) Murphy and Wood (2004)Ordinary-service (�3 and <5days)Slower-service (�5days)

Product status Work in process Danielis et al. (2005);Witlox and Vandaele (2005)Final goods

M.-C. Tsai et al. / Journal of Air Transport Management 17 (2011) 211e214 213

tech industry was 8.2 years. Of the 498 firms that agreed to fill outthe survey form, 357 firms returned the questionnaire, of which 75were rejected because of insufficient information, leaving 282 validresponses.

4. Results

4.1. Competitive structure: nested logit model

Two carrier nests consisting of the five alternatives in an NLstructure were examined. At the convergence of NL model, the loglikelihood function value is �141.2 and likelihood ratio index, 0.69indicating a goodmodel fit and rejecting the null hypothesis that allparameters are zero at the 5% level of significance. The corre-sponding log-sum values for the two nests assumed are 0.65 and0.67 and because they are statistically different from one, thehypothesis of mm ¼ 1 is rejected. The results indicate that theassumption of identical distribution associated with the randomcomponents between five air carrier alternatives is inappropriate.A classification of express (Fedex, DHL and UPS) and forwarder(global and regional forwarder), is thus the best describer of thecompetitive structure.

All strategic variables exercise a positive influencing on thechoice of carrier alternatives with pricing being is the most influ-ential factor (0.92), followed by service strategy on damage claim

Table 2Results of LCM model.

Segment 1 Segment 2

Coefficient Coefficient

Segment variable:

Constant 6.84** Fixed parameterHeavy size (�100 kg) �6.27**Asia destination �5.32**Alternative choice:Express nest 4.92** �6.67**Forwarder nest (base) e e

Log-likelihood at convergence �46.0R-squared 0.8

Note: * Statistically significant at the 0.1 level; ** Statistically significant at the 0.05level.

and punctuality. Freight security, exceptional management andchannel strategy all have similar effects (0.51). The proportionateimportance of each strategy within a group, reflecting customers’relative satisfactions by strategy within a strategic nest, enablesthestrategic properties between the nests (Hu and Hiemstra, 1996).The relative importance of forward and express nest on service rateis 25% and 20%, implying that the forwarder nest focuses more onpricing strategy than express service that tends to use servicestrategies such as delivery punctuality, freight security, and damageclaim than forwarders. The two remaining strategies, exceptionalmanagement and channel strategy (13%) have similar effects onexplaining strategic groups.

4.2. Market segmentation: latent class model

The determination of the number of segments is required priorto LCM and is made by using both Akaile information (AIC) andBayesian information (BIC) (Wen and Lai, 2010). The values asso-ciated with segments were measured sequentially and the bestsolution determined where convexity occurs. For one, two andthree segments, the respective AIC and BIC values are 330.2, 106.6,113.9 and 310.2, 97.9, 100.9, indicating that two segments offer thebest solution.

Given the number of segments, LCMwas applied and the resultsseen in Table 2; the model fits is good. Of the five-macro variablesshipment destination and size are effective, while the others, have

Fig. 2. Segment properties and choice probabilities.

M.-C. Tsai et al. / Journal of Air Transport Management 17 (2011) 211e214214

lost their influence in the segmenting of the market. Further, thet-statistics associated with the two effective variables indicate thatEurope andNorth America do not significantly affect the results andare thus combined as non-Asia destinations.

Based on the parameters calibrated, the component, segmentsize and choice probability are measured using Equations (3)e(5).As indicated in Table 2, the negative coefficient values of heavy sizeand Asia destinations indicate that customers for Segment 1carriers are more likely to be concerned with small shipments andnon-Asia destinations. Conversely, Segment 2 tends to for heavyshipments and Asian destinations. Shipment size is more effectivethan shipment destination in affecting the components ofa segment. In terms of segment size; Segment 1 accounts for 59.4%of the market. For choice probability, a positive value of alternativechoice constant for the express nest indicates with a near choice92% probability that Segment 1 prefers using express to forwarder.Conversely, a larger negative value of alternative choice constantindicates that Segment 2, heavy size and Asia destinationcustomers, use forwarders (88.2%) far more frequently than Express(Fig. 2.)

5. Conclusions

But the effect of competition on segmentation remains a signif-icant omission in the study of air freight transport. Our evidencefrom Taiwan shows that the air carriers remain in two strategiccluster groups, express and forwarder, based on customer assess-ment with pricing a leading strategy recognized by customers forforwarders and service punctuality and freight security for express.The differences, however, are not large.

Acknowledgment

Special thanks to the National Science Council of Taiwan forgrant (No. 98-2410-H-005-009-MY3).

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