analysis of third party logistics performance

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An analysis of third-party logistics performance and service provision

Chiung-Lin Liu a,, Andrew C. Lyons b

a Bafang College of Logistics, Fuzhou University, 2 Xueyuan Road, Fuzhou 350108, PR Chinab University of Liverpool Management School, Chatham Street, Liverpool L69 7ZH, UK

a r t i c l e i n f o

Article history:Received 5 June 2009

Received in revised form 29 May 2010

Accepted 26 August 2010

Keywords:

Third-party logistics

Service capabilities

Performance

a b s t r a c t

The aim of the research described in this paper is to evaluate the relationship between theservice capabilities and performance of UK and Taiwanese third-party logistics (3PL) pro-

viders. A study is presented based on a recent survey. The results identify the most impor-

tant services offered by 3PLs and the most important aspects of 3PL operational

performance. The results also suggest that excellence in operations is more important than

wide-ranging service provision. Furthermore, the research suggests that the range of ser-

vice provision offered by 3PLs does not directly influence the 3PLs financial performance.

However, 3PL providers with service capabilities that correspond to the key priorities of

customers will gain superior financial performance through a better operational perfor-

mance. Similarities and differences between logistics practices in the UK and Taiwan are

highlighted.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

The pursuit of improved efficiency performance in logistics operations is a constant business challenge (Bowersox et al.,

2007). One initiative that is proving productive and allows businesses to concentrate on their core competencies is the out-

sourcing of the logistics function to partners, known as third-party logistics (3PL) providers (Hong et al., 2004; Lieb and

Bentz, 2005a). 3PL providers provide an opportunity for businesses to improve customer service, respond to competition

and eliminate assets (Handfield and Nichols, 1999). Many 3PL providers have broadened their activities to provide a range

of services that include warehousing, distribution, freight forwarding and manufacturing (Lieb and Randall, 1999). Extending

service provision has intensified competition amongst 3PL providers, yet Lieb and Bentz (2005b) reported that very few

large, US manufacturers specifically use their 3PL providers for contract manufacturing, purchasing, or financial services de-

spite the shift of many of them into non-traditional activities.

To formulate appropriate strategies for leveraging their full business potential and for mitigating investment risks, prac-

titioners would benefit from understanding any correlation that exists between 3PL performance and different types of ser-vice provision. Previous research studies (e.g., Arroyo et al., 2006; Sohail and Al-Abdali, 2005) have examined the factors

affecting 3PL provider selection and the extent of 3PL use. However, Murphy and Poist (2000) suggested that there has been

relatively little attention given to empirical studies of providers and customers. Provider in this context indicates the com-

pany that provides logistics services for its customers while the customer is the service user. Moreover, as logistics is often

international, 3PL providers with different service capabilities encounter varying types of opportunity for service provision

and access to customers. Empirical studies that have been undertaken, have usually concentrated on logistics management

in a single region, while multi-region studies have received limited attention (Luo et al., 2001). This is particularly true of

comparative logistics studies between Western and non-Western practices (Luo et al., 2001). Despite many studies

1366-5545/$ - see front matter 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.tre.2010.11.012

Corresponding author. Tel.: +886 983908146 (Taiwan).

E-mail addresses: [email protected] (C.-L. Liu), [email protected] (A.C. Lyons).

Transportation Research Part E 47 (2011) 547570

Contents lists available at ScienceDirect

Transportation Research Part E

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / t r e
http://dx.doi.org/10.1016/j.tre.2010.11.012mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.tre.2010.11.012http://www.sciencedirect.com/science/journal/13665545http://www.elsevier.com/locate/trehttp://www.elsevier.com/locate/trehttp://www.sciencedirect.com/science/journal/13665545http://dx.doi.org/10.1016/j.tre.2010.11.012mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.tre.2010.11.012


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demonstrating that logistics capabilities are positively associated with performance (Shang and Marlow, 2005), there is still

insufficient evidence to conclude that outsourcing practices in a Western country such as the UK have exactly the same effect

in a non-Western country such as Taiwan. As has been pointed out: To establish more firm conclusions, studies must con-

duct parallel (multi-region) studies, with the same sample design and questionnaire. Such studies will be very important for

understanding how context influences the outsourcing practice and shapes 3PL services (Arroyo et al., 2006).

Existing research on the relationship between service capabilities and performance has made only a limited contribution

to the correlation that exists between 3PL performance and different forms of service provision. Moreover, there has been

relatively little attention given to empirical studies of both providers and customers. This research has set out to address

these gaps by empirically exploring the relationships between service capabilities and performance from both a provider

and customer perspective.

Key questions posed by the research are:

Do different forms of service provision lead to discernible profitable contributions for 3PLs?

Does operational performance have a significant effect on the financial performance of 3PLs?

If the service capabilities that correspond to the key priorities of customers and the operational performance of 3PLs do

have positive effects on financial performance, how can this be established, measured, and evaluated?

Furthermore, this research attempts to provide a comparison of logistics activities in two regions: the UK and Taiwan. The

comparison between the UK and Taiwan is appropriate as the two island-based countries are similar in terms of their econ-

omies, as shown in Table 1 (Central Intelligence Agency, 2009). In addition, they have stable economic growth and are highly

dependent on foreign trade. This, in turn, may significantly impact the characteristics and structure of their logistics sectors.

Such a comparative analysis allows another question to be posed:

What are the contextual implications of any identified differences between the 3PL practices in the UK and Taiwan?

This research aims to inform the decision-making processes of practitioners by providing useful policy information con-

cerning the impact of different forms of service provision, fill a series of gaps in the literature and extend the prevailing the-

ory by exploring the relationship between service capabilities and performance from a provider and customer perspective

within a bi-regional context. Service capabilities encapsulate all aspects of service provision and represent the process of

delivering products, in a way that creates added value to customers (van der Veeken and Rutten, 1998), such as warehous-

ing, distribution, freight forwarding and manufacturing.

2. Literature review and research hypotheses

2.1. Resource-based view

The resource-based view (RBV) of the firm (Barney, 2001; Eisenhardt and Schoonhoven, 1996; Hart, 1995; Mahoney and

Pandian, 1992; Peteraf, 1993; Priem and Butler, 2001; Wernerfelt, 1984), has attempted to look at firms in terms of their

resources rather than in terms of their products (Wernerfelt, 1984). A firms resources have been defined as tangible and

intangible assets, such as brand names, in-house knowledge of technology, employment of skilled personnel, trade contacts,

machinery, efficient procedures, capital (Wernerfelt, 1984). In recent years, the RBV has been employed in logistics-related

research to assess the contribution made by logistics activities on firm performance (Lai, 2004; Lu and Yang, 2009; Shang,

2009; Shang and Marlow, 2005; Yang et al., 2009). A firms resources have been defined as tangible ( Lai, 2004; Lu and Yang,

2009) or intangible assets (Shang, 2009; Shang and Marlow, 2005). For example, Lai (2004) examined the variation in service

performance for different types of logistics service provider. Resources, in this case, were defined as a bundle of service capa-

bilities. The results of this study revealed that full service providers had the best firm performance. Lu and Yang (2009)

examined the performance differences of different types of international distribution center operators. Resources were once

again defined as a bundle of service capabilities. The results indicated that firms with a high level of customer responsiveness

and innovation capabilities had the highest level of overall service performance. Shang and Marlow (2005) explored the link-

age between logistics capabilities and logistics and financial performance. Resources were defined as a bundle of behaviour-

based capabilities including information systems-related capabilities and benchmarking and flexibility expertise. The results

Table 1

The UK and Taiwanese economy in 2007.

UK Taiwan

GDP: per capita (purchasing power parity, PPP) ($) 36,500 31,100

GDP: composition by sector (%)

Agriculture 1.3 1.7

Industry 24.2 25.1

Services 74.5 73.2

548 C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547570
http://www.elsevier.com/locate/tre


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indicated that information systems-related capabilities enhanced the firms logistics performance and indirectly impacted

on financial performance. Shang (2009) explored the relationships between integration capabilities, organizational learning

capabilities, service performance, and financial performance for forwarder-based 3PLs. Resources were defined as a bundle of

behavior-based capabilities including integration and organizational learning capabilities. The results indicated that organi-

zational learning enhanced the firms financial performance.

It is evident from such studies that researchers have found logistics-related capabilities to be positively related to oper-

ational and/or financial performance. The use of RBV has provided a theoretical foundation for studies that attempt to assess

the relationships between service capabilities and firm performance for 3PLs.

2.2. Service capabilities

Service capabilities represent the process of delivering products in a way that creates added value to customers (van der

Veeken and Rutten, 1998). A number of previous studies have identified examples of logistics service capabilities. These are

summarized in Tables 2 and 3. The level of the providers service capabilities should meet the customers requirements.

Therefore, the review of the service capabilities is based on both the providers and customers perspectives.

There is only one logistics-related publication which has discussed service capabilities from both a provider and customer

perspective. Murphy and Poists (2000) study indicated there was a lack of consistency between the top services provided

and used. However, there are a number of publications which have discussed service capabilities related to 3PLs from either

a provider or customer perspective. In terms of provider-related studies, for example, Stefansson (2006) classified third-

party service providers into three groups: carriers, logistics service providers (LSPs) and logistics service intermediaries

(LSIs). All providers have different roles and provide diverse services in the logistics industry. From a customer perspective,

several studies have identified and provided a list of logistics services used by customers ( Rao and Young, 1994; Rao et al.,

1993; Sink et al., 1996). Researchers have also examined the most frequently used logistics services in different countries

(e.g., Bhatnagar et al., 1999; Dapiran et al., 1996; Sohail and Sohal, 2003).

Based on the RBV theory, 3PLs can develop various logistics capabilities to provide a range of services for different cus-

tomer requirements (Lai, 2004). This implies that 3PLs will have different types of service provision. In addition, based on the

logic of RBV theory and the reviewed literature, logistics capability can be seen to be a key source of leading superior per-

formance (Lu and Yang, 2009). Thus, wide-ranging service provision may have a positive impact on the financial perfor-

mance of 3PL providers. Moreover, 3PLs with a broader service provision may be in a better position to meet the needs of

customers and, therefore, achieve better performance than competitors. Such discussions led to the following two

hypotheses:

H1. 3PL providers can be clustered in terms of service provision. The range of service provision offered by 3PL providers is

positively related to their financial performance.

H2. 3PL providers can be clustered in terms of service provision. The range of service provision offered by 3PL providers is

positively related to their operational performance.

2.3. Operational performance

Operational performance concerns the measurable points of the outcomes of an organizations processes, such as reliabil-

ity, speed of delivery and quality of service (Voss et al., 1997). Aside from minor differences in semantics, there is broad con-

sensus that operational performance can be expressed through a combination of cost, quality, flexibility, delivery and

innovation (da Silveira and Cagliano, 2006; Hill, 2005; Narasimhan and Jayaram, 1998; Slack et al., 2004). Many empirical

studies have used different indices to measure these five elements of operational performance (e.g., Brooks, 1999a; Fawcett

and Smith, 1995; Panayides, 2007). These are summarized in Tables 4 and 5. As indicated in Table 4, there is only one logis-

tics-related publication which has discussed operational performance from both a 3PL provider and a 3PL customer perspec-

tive. Brooks (2000) study indicated there was remarkable consistency on the key aspects of operational performance

between customers and providers.

Based on the RBV theory and reviewed literature, operational performance also can be seen to be a key source of leading

superior financial performance. For example, in the logistics literature, Shang and Marlow (2005) asserted that operational

performance (such as logistics performance) affects financial performance in manufacturing firms. Moreover, some research-

ers found that operational performance had a positive influence on financial performance for 3PLs (Huo et al., 2008; Yeung

et al., 2006). It has been shown that 3PLs adhering to the combination-strategy comprising cost and differentiation have the

best financial performance (Yeung et al., 2006). 3PLs with high operational performance are expected to have good market

share performance as customer retention is high (Huo et al., 2008). Thus, operational performance may have a positive im-

pact on financial performance for 3PL providers. Stated in a hypothesis form, we have

H3.3PL providers whose operational performance is high have better financial performance compared to those with a lower

operational performance.

C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547570 549


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Table 2

Key service capabilities (provider perspective).

Title/item Lieb and

Randall

(1996)

Lieb and

Randall

(1999)

Murphy and

Poist(2000)avan Hoek

(2000a)

van Hoek

(2000b,

2001)

Larson and

Gammelgaard

(2001)

Lieb a

Kendr

(2003

Transportation-related

Inbound transportation w

Outbound distribution w

Overseas sourcing w

Overseas distribution w

Merge in transit/freight (de)consolidation w w w w

Direct transportation service

Expedited delivery w

Emergency transport

Transportation planning and management related

Fleet operation and management w w w

Route and network optimization/shipment planning

Rate negotiation w w w

Carrier selection w w w

Freight forwarding/freight brokering w

Warehousing/inventory-related

Warehousing/storage with good reception w w w w

Customer spare parts w w w

Storage of products with special requirements (e.g.,

preparation for freezing and thawing)

Inventory management/inventory replenishment w w w w w

Bonded warehousing w

Pick and pack w w

Order processing w w w w

Order fulfilment w w

Cross-docking w

Product testing/inspection/quality control w w w w

Product returns w w w w w w

Reverse logistics w

Value-added services

Labelling/marking w

Packaging w w w w

Relabelling/repackaging w

Kitting

Assembly/re-assembling/installation w w w w w

Production/selected manufacturing activities/customization

w w w w w w

Repair w w w

Information technology

Bar code scanning

RFID

Electronic commerce w w

Tracking and tracing shipment information w

Logistics information systems w w w w

Order entry/management systems

Selection of software

Interfacing with ERP systems; e.g., SAP


5/24

Product design and marketing support-related

Packaging design

Product configuration/product design w w

Promotional support w

Exhibition w w

Finance-related

Invoicing/billing function w

Freight bill auditing/payment w w

Billing the final customer w w

Factoring/financing service w w w

Insurance service w

Consulting services

Logistics planning w w w

Supply chain design w

Other customer service

Customs brokerage w w

Call center operation/after sales service w

Management/performance reports w w

Procurement of materials (e.g., purchase of lower-

level materials, packaging materials and

inventory)

w w w

a From both a 3PL provider and a 3PL customer perspective.


6/24

Tab

le

3

Key

servicecapabilities(customerperspective).

T

itle/item

Raoetal.

(1993)

RaoandYoung

(1994)

Sink eta

l.

(1996)

Dapiran

etal.

(1996)

Sink

andLangley

(1997)

Gooley

(1997)

Millen

etal.

(1997)

Murphy

andPoist

(1998)

Peters

etal.

(1998)

Boyson

etal.

(1999)

Bhatnagar

etal.

(1999)

Murphy

andPoist

(2000)a

vanLaarhoven

etal.

(2000)

Sohail

andSohal

(2003)

Knemeyer

etal.

(200

3)

Sohail

etal.

(2004)

Hong

etal.

(2004)

Wilding

andJuriado

(2004)

LiebandBentz

(2005b)

Vaidyanathan

(2005)

Sohail

andAl-

Abdali

(2005)

Knemeyer

andMurphy

(2005)

Arroyo

etal.

(2006)

T

ransportation-related

Inboundtransportation

w

w

w

w

w

w

O

utbounddistribution

w

w

w

w

w

w

w

w

w

O

verseassourcing

w

w

O

verseasdistribution

w

w

M

ergeintransit/Freight

(de)consolidation

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

D

irecttransportationservice

w

w

w

w

E

xpediteddelivery

w

w

w

w

E

mergencytransport

w

T

ransportationPlanning&

Management-Related

F

leetoperationandmanagement

w

w

w

w

w

w

w

w

w

w

w

w

w

w

R

oute&networkoptimization/

Shipmentplanning

w

w

w

w

w

w

R

atenegotiation

w

w

w

w

w

w

w

w

w

w

w

w

w

w

C

arrierselection

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

F

reightforwarding/Freight

brokering

w

w

w

w

w

w

w

w

W

arehousing/Inventory-Related

W

arehousing/Storagewithgood

reception

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

C

ustomerspareparts

w

w

w

w

w

w

w

w

w

S

torageofproductswithspecial

requirements(e.g.,preparation

forfreezingandthawing)

w

Inventorymanagement/Inventory

replenishment

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

B

ondedwarehousing

w

w

P

ickandpack

w

w

w

w

w

w

O

rderprocessing

w

w

w

w

w

w

w

w

w

w

w

w

w

O

rderfulfilment

w

w

w

w

w

w

w

w

w

w

w

w

C

ross-docking

w

w

w

P

roducttesting/inspection/quality

control

w

w

w

P

roductreturns

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

R

everselogistics

w

w

w

V

alue-A

ddedServices

L

abelling/Marking

w

w

w

w

w

P

ackaging

w

w

w

w

w

w

w

w

R

elabelling/Repackaging

w

w

w

w

K

itting

w

A

ssembly/Re-assembling/

Installation

w

w

w

w

w

w

w

w

w

w

w

w

w

w

P

roduction/Selectedmanufacturing

activities/Customization

w

w

w

w

w

w

w

R

epair

w

w

w

InformationTechnology

B

arcodescanning

R

FID

E

lectroniccommerce

w

w

w

w

T

rackingandtracingshipment

information

w

w

w

w

L

ogisticsinformationsystems

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

O

rderEntry/managementsystems

w

w

w

w

w

w

S

electionofsoftware

w



7/24

InterfacingwithERPsystems;e.g.,

SAP

P

roductDesignandmarketingsupport-related

P

ackagingdesign

w

P

roductconfiguration/Product

design

P

romotionalsupport

E

xhibition

F

inance-Related

Invoicing/Billingfunction

w

w

w

F

reightbillauditing/payment

w

w

w

w

w

w

w

w

w

w

w

w

w

w

w

B

illingthefinalcustomer

F

actoring/Financingservice

Insuranceservice

C

onsultingServices

L

ogisticsplanning

w

w

w

w

w

w

w

S

upplychaindesign

w

O

therCustomerService

C

ustomsbrokerage

w

w

w

w

w

w

w

w

C

allcenteroperation/Aftersales

service

w

w

M

anagement/performancereports

w

w

w

w

w

P

rocurementofmaterials(e.g.,

purchaseoflower-level

materials,packagingmaterials

andinventory)

w

w

w

a

frombotha3PLprovideranda3PLcustomerperspective.



8/24

Table 4

Key items of operational performance (provider/customer perspective).

Title/item Provider perspective

Brook

(1999a,b,

2000a)

van

Hoek

(2001)

Stank

et al.

(2003)

Lai and Cheng (2003)

and Lai et al. (2004)

Lai

(2004)

Panayides

and So

(2005)

Brah

Lim

Delivery

To deliver expedited shipments w w

To offer short delivery lead-time w w

To offer greater proportion of on time and accurate delivery w w w w

Quality

To provide higher customer satisfaction ratings

To enhance customer success (e.g., helping customers in

value analysis, cost reductions, problem solving, etc.)

w w w

Lower customer complaints (percentage of total sales) w To deliver goods in an undamaged state w w w

Flexibility

To accommodate special or non-routine requests w

To handle unexpected events w

To provide quicker response to customers w w w w

Cost

To operate with low overall operating cost as a percentage of

sales

w

To improve the rate of utilization of facilities/equipment/

manpower in providing the services

w w w

Innovation

Aggressiveness in increasing the value-added content of

services

Aggressiveness in the reduction of order cycle time w

To provide new and better services

a From both a 3PL provider and a 3PL customer perspective.


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Table 5

Key items of operational performance (supply chain outputs (first party) perspective).

Title/item Sharma

et al.

(1995)

Daugherty

and

Pittman

(1995)

Fawcett and

Smith (1995),

Fawcett et al.

(1997)

Morash

et al.

(1996a)

Morash

et al.

(1996b)

Fawcett

et al.

(1996)

Morash

and

Clinton

(1997)

Fawcett

et al.

(2000)

Zhao

et al.

(2001)

Ellinger

et al.

(2002)

Delivery

To deliver expedited shipments w w w

To offer short delivery lead-time w

To offer greater proportion of on time

and accurate delivery

w w w

Quality

To provide higher customer

satisfaction ratings

w w w w

To enhance customer success (e.g.,

helping customers in value

analysis, cost reductions, problem

solving, etc.)

w w

Lower customer complaints

(percentage of total sales)

w w

To deliver goods in an undamagedstate

Flexibility

To accommodate special or non-

routine requests

w w w

To handle unexpected events w w

To provide quicker response to

customers

w w w w w

Cost

To operate with lowoverall operating

cost as a percentage of sales

w w w w w

To improve the rate of utilization of

facilities/equipment/manpower in

providing the services

w

Innovation

Aggressiveness in increasing the

value-added content of services

w w

Aggressiveness in the reduction of

order cycle time

w

To provide new and better services w w w


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2.4. Financial performance

In the literature concerning logistics services, different kinds of constructs are used to measure the financial performance

of the 3PL. It is noticeable that different measures do not coincide in different studies. Panayides and So (2005) measured

performance using only a single item: market share. Most researchers, however, believe that the financial performance of

a firm is better assessed through the use of a multi-dimensional construct. Ellinger et al. (2003) used the following eight

measures of financial performance: gross profit margin, return on sales (ROS), operating profit margin, return on assets

(ROA), return on equity (ROE), accounts receivable turnover, current ratio, and debt ratio. Other measures that have been

used include return on investment (ROI) (Panayides, 2007), sales growth (Panayides, 2007), and sales volume (Panayides,

2007; Yeung et al., 2006).

2.5. The relationship between financial performance, operational performance and service capabilities

A review of the literature in the area reveals that the conceptual model presented in Fig. 1 is a representative view. The

model suggests that service capabilities affect both operational performance and financial performance. Operational perfor-

mance is cast in a mediating role between financial performance and service capabilities. Although the possible mediating

effects of operational performance on the relationship between financial performance and service capabilities have not been

tested previously, operational performance has long been identified as an important factor in empirical studies in logistics.

Moreover, Karkkainen and Elfvengren (2002) indicated the significance of understanding customer requirements for the

success of service development. Meeting a key priority (i.e., a set of important customer requirements) is essential in helping

firms to gain a competitive advantage ( Jiao and Chen, 2006). On the other hand, many studies have provided useful infor-

mation for 3PL providers on the most essential services to enhance 3PL providers responsiveness, competitiveness and per-

formance. Thus, the service capabilities of 3PL providers which correspond to the key priorities of customers may have a

positive impact on the financial performance of the 3PL providers. Therefore, we propose the following additional

hypothesis:

H4. For 3PLs, the relationship between service capabilities corresponding to customers key priorities and financial

performance is mediated by operational performance.

3. Methodology

3.1. Methods of research

The analytical steps of the methodology are shown in Fig. 2. The first step was the selection of the performance and ser-

vice capabilities of 3PL providers. This consisted of a review of previous studies, a questionnaire survey, personal interviews

and a validity test. Cluster analysis was used to distinguish 3PLs in terms of service provision. In recent studies of logistics

providers (Lai, 2004), the application of cluster analysis was widely used to cluster logistics providers in terms of the service

provision. A one-way analysis of variance (ANOVA) was used to test whether there are significant differences in 3PL oper-

ational and financial performance (Lai, 2004). Simple regression analysis was utilized to evaluate the relationship between

operational performance and financial performance for 3PL providers. A combination of simple and multiple regression anal-

ysis was conducted to determine whether operational performance plays an intermediary role between service capabilities

corresponding to customers key priorities and financial performance for 3PL providers. All analyses were carried out using

the SPSS 15.0 for Windows (2007).

3.2. Questionnaire design

A postal questionnaire was employed as the main method of data collection for this study. The stages of the development

of the questionnaire followed the seven stages outlined by Dillman (2007). Formulating each survey question was followed

Fig. 1. The conceptual model.



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by choosing the appropriate question structure, writing survey questions, ordering the questions, constructing questionnaire

pages, designing the appearance of the questionnaire and pre-testing. A preliminary survey was pre-tested in both the UK

and Taiwan by interviewing practitioners and experts with a logistics remit. Interviewees were asked to comment on and

criticise any aspects of the questionnaire. The questionnaire consisted of four parts: financial and operational performance,

service capabilities, business background information and respondent characteristics. The final measurement items em-

ployed for evaluating performance and service capabilities are presented in Appendix A.

Financial performance was measured on a two-item scale: gross profit margin and sales growth, partly adopted from

Ellinger et al. (2003) and Panayides (2007). On the basis of the preliminary study, it was assumed that these two items suf-

ficiently reflected the financial performance of the 3PL. Previous studies have indicated that there are objective and subjec-

tive approaches to measure firms financial performance (Panayides, 2007). Objective approaches use the absolute values of

quantitative performance measures such as return on investment (ROI) and return on assets (ROA). In contrast, the subjec-tive approaches use subjective measures of performance, where respondents are asked to indicate how well their company

performs, compared to their major competitors, in terms of appropriate financial metrics (e.g., Panayides and So, 2005;

Yeung et al., 2006).

In this study, the subjective approach was employed as the main method to evaluate 3PL financial performance as sec-

ondary data on most of the 3PL providers was not readily available. The percentage of the available secondary data for Tai-

wanese and UK 3PLs was about 5% and 30%, respectively. There is some positive correlation between self-reported and

objective financial performance measures. Moreover, previous researchers have indicated that objective and subjective mea-

sures of performance are closely correlated (Covin et al., 1994; Dawes, 1999).

The fifteen indicators for operational performance were developed by referring to previous logistics research (Ellinger

et al., 2002; Fawcett and Smith, 1995; Fawcett et al., 1997) and from discussions with logistics academics and practitioners.

Compared with the 3PLs questionnaire, two items (o11, o12) were omitted in the customers questionnaire because they

were only used to assess the cost aspect of operational performance for 3PLs. Respondents were asked to provide a perfor-

mance rating relative to perceived industry averages.The 32 service capability indicators were proposed by referring to previous logistics research (Lai, 2004; Murphy and Po-

ist, 2000; Stefansson, 2006) and by conducting personal interviews with providers and customers executives in the UK and

Taiwan.

In this study, a content validity test of the questionnaire was conducted through a theoretical review and a pilot test.

Questions were based on the literature review and discussions with a number of logistics experts. Therefore, the question-

naire could be accepted as possessing content validity.

3.3. Selection of the sample

For 3PLs, in Taiwan, 329 3PL firms were identified from the Logistics Information Network database (Ministry of Eco-

nomic Affairs, ROC, 2007) and China Credit Information Service database (China Credit Information Service Ltd., 2007). In

the UK, the 621 companies used within the survey were obtained from the FAME (Bureau van Dijk, 2007) and Kompass dat-

abases (Reed Business Information, 2007). All of the firms provided transportation and warehouse-related services.

Step 1

Selection of the performance and service capabilities of 3PL providers

Review related literature

Questionnaire development

Interviews

Validity test

Step 2

Identification of 3PL providers in terms of service provision and their

differences in operational and financial performanceCluster analysis

ANOVA analysis

Step 3

Evaluation of the relationship between operational performance and financial

performance for 3PL providers

Simple regression analysis

Step 4

Evaluation of the relationship between service capabilities which corresponds to

customers' key priorities, operational performance and financial performance for

3PL providers

Simple regression analysis

Multiple regression analysis

Fig. 2. Analytical steps of the methodology.



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For customers in Taiwan, the 500 largest manufacturing firms were identified from an annual report, entitled The Top

5000: the Largest Corporations in the Republic of China, published by the China Credit Information Service Ltd. (2006). In

the UK, the 595 largest manufacturing companies surveyed were obtained from the FAME (Bureau van Dijk, 2007) database.

3.4. Response rate analysis

3.4.1. Taiwan

In order to improve the response rate, we followed Dillmans (2007) comprehensive Tailored Design Method to survey

implementation. Four contacts were made with first class mail: (1) the pre-notice letter; (2) the questionnaire with a token

incentive; (3) a follow-up letter; and (4) a second questionnaire. First, respondents were sent a pre-notice letter informing

them of the study and requesting their co-operation in completing a questionnaire to be mailed later. After one week, the

survey questionnaire was mailed to the respondents including a covering letter, a return envelope with a first-class stamp,

and 100 New Taiwan dollars ($ 3USD) paper money. About 10 days later a reminder postcard was sent to non-respondents,

encouraging those who had not responded to do so. Nearly two weeks after the mailing of this postcard, a new covering let-

ter, a replacement questionnaire and a return envelope with first-class stamp, were sent to non-respondents.

For 3PLs, the effective population size was reduced to 286 as 18 respondents indicated that their companies only provided

services for internal users, 17 service providers did not provide any transportation/warehousing or value-added-related ser-

vices and eight of the respondents did not provide services for manufacturing. The total usable number of responses was 92.

Therefore, the overall response rate was 32.1% (92/286). For customers, 239 usable questionnaires were obtained. The total

response rate was thus 47.8% (239/500).

3.4.2. UK

The survey questionnaire was mailed to the respondents including a covering letter and return envelope with pre-paid

postage. About one month later, a new covering letter, replacement survey and a return envelope with pre-paid postage

were sent to non-respondents.

For 3PLs, the effective population size was reduced to 513 as 93 respondents indicated that their companies only provided

services for internal users, 11 service providers did not provide any transportation/warehousing or value-added related ser-

vices and four of the respondents did not provide services for manufacturing. The total usable number of responses was 112.

Therefore, the overall response rate was 21.8% (112/513). For customers, 168 usable questionnaires were obtained. The total

response rate was thus 28.2% (168/595).

3.5. Data representativeness and non-response bias test

In this study, we conducted two preliminary analyses to check the representativeness of the final sample. First of all, for

3PLs, an independent-sample ttest was conducted to assess the characteristics of the firms that responded to the survey and

those that did not including age of firms, total sales volume and full-time employees (in Taiwan, the total sales volume and

full-time employees were not tested due to only 5% data were available). For customers, the distribution of the firms indus-

tries was tested between those that responded to the survey and those that did not, using a Chi-square test. No significant

difference was found at the 0.05 level either in the UK or Taiwan, indicating the absence of non-response bias.

Another common non-response bias test was also conducted (Lambert and Harrington, 1990), comparing the first and

second waves as recommended by Armstrong and Overton (1977): the late respondents can be assumed to be similar to

non-respondents. Non-response bias is assumed non-existent if no significant differences exist in the survey variables. First,

we compared the level of all of the Likert ratings in the first and second waves, using an independent-sample ttest analysis.

Second, a Chi-square test was conducted on each survey question in terms of the firms characteristics (i.e., age, total sales

volume, full-time employees and industry type (only relevant to customers)) between the respondents from the two waves

of the mailing.

In Taiwan, to check for differences between respondents and non-respondents, the late returned responses (later than the

day a reminder postcard was sent) were compared with early survey participants. For 3PLs, there were no significant differ-

ences (at p < 0.05) with regards to all items analyzed. For customers, most items were not statistically significant at the 0.05

level, with the exception of three aspects of operational performance (o1, o2, o14) and four service capabilities (s5, s6, s7,

s10) on the importance rating. In the UK, for 3PLs, only 1 operational performance (o8) and 2 service capabilities (s4, s22)

were statistically significant at the 0.05 level. For customers, there were no significant differences (at p < 0.05) with regards

to all items. The findings, therefore, also suggested that non-response bias was not a problem either in the UK or Taiwan.

3.6. Assessment of common method variance

On the basis of the preliminary study, we found that generally only one person, such as the general manager, had the

necessary knowledge to provide reliable information. However, using self-reported, perceptual measures and measuring

multiple constructs from the same respondent could lead to the common method variance problem (Podsakoff et al.,

2003; Podsakoff and Organ, 1986). To deal with this potential weakness, we utilized the procedural remedies that Podsakoff

et al. (2003) suggest, that is, the use of an anonymous questionnaire and simple, specific items to respond to. Moreover,



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Harmans single-factor test (Harman, 1967) was conducted to check whether common method variance was present for the

respondents. The same test has been used in operational-related research (e.g., Kathuria (2000)). If common method variance

is a problem, either a single factor will emerge or one general factor will account for the majority of the covariance in the

independent and criterion variables (Podsakoff and Organ, 1986). The three scales with 50 items used to measure financial

performance, operational performance and service capabilities were factor analyzed by using principal components analysis

where the unrotated factor solution was examined (Podsakoff and Organ, 1986).

In Taiwan, the results for the Harman test indicated the existence of 11 factors with eigenvalues greater than one. These

eleven factors explained 75% of the variance among the 50 items, while the first factor accounted for only 34% of the vari-

ance. In the UK, the Harman test generated 13 factors explaining 72% of the variance with eigenvalues greater than one. The

first factor explained only 23% of the variance. Since several factors and the first factor did not explain the majority of the

variance in Taiwan and the UK, common method variance did not appear to be a serious threat to validity.

3.7. Reliability test

The reliability of a questionnaire is concerned with the consistency of responses to questions (Saunders et al., 2003). Typ-

ically, reliability is assessed in three forms: testretest, alternate-form and internal consistency. Reliability is usually ex-

pressed on the basis of the Cronbachs alpha coefficient. Levels of 0.70 or more are generally accepted as representing

good reliability (Hair et al., 2006). All of the usable questionnaires in this study were used to calculate internal consistency

reliability. These are summarized in Table 6. With the exception of financial performance for the UK 3PLs (0.605), all of the

reliability scores exceeded the minimum reliability standard of 0.70.

3.8. Missing data analysis

Missing data are a very common occurrence in most datasets (Downey and King, 1998; Lepkowski et al., 1987). The pair-

wise approach was used for addressing missing data in this study in order to maximize the use of valid data (McKnight et al.,

2007).

4. Results of the analyses

4.1. The importance of 3PL service capabilities to customers

For Taiwanese customers, an evaluation of the customers aggregated perceptions of the importance of each item revealed

all 32 service capabilities were perceived as important (mean scores were all over 4.0) (see Table 7). The most important

Table 6

Reliability test.

Cronbachs alpha

Taiwan UK

Provider Customer Provider Customer

Financial performance 0.890 0.605

Operational performance 0.936 0.916 0.849 0.879

Service capabilities 0.947 0.952 0.916 0.920

Table 7

Importance of 3PL service capabilities to customers.

Taiwan UK

Rank Service capabilities Mean Rank Service capabilities Mean

1 s22. Tracking and tracing 6.296 1 s2. Outbound distribution 6.288

2 s2. Outbound distribution 6.119 2 s11. Order fulfilment 6.119

3 s23. Logistics information systems 6.066 3 s4. Rate negotiation 5.966

4 s11. Order fulfilment 6.053 4 s22. Tracking and tracing 5.774

5 s4. Rate negotiation 6.037 5 s26. Interfacing with ERP systems 5.770

6 s24. Order management systems 6.036 6 s23. Logistics information systems 5.690

7 s1. Inbound transportation 5.935 7 s7. Storage 5.689

8 s18. Bar code scanning 5.824 8 s32. Management reports 5.669

9 s7. Storage 5.741 9 s9. Inventory management 5.593

10 s26. Interfacing with ERP systems 5.710 10 s29. Billing the final customer 5.590

Note: Boldface indicates coincident services identified by both Taiwan and UK customers.



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service capability was tracking and tracing, followed by outbound distribution, logistics information systems, order fulfil-

ment and rate negotiation (mean scores were over 6.0, derived from a seven-point scale where 1 represented very unimpor-

tant and 7 signified very important). For UK customers, outbound distribution was viewed as the most important service

capability by respondents, followed by order fulfilment, rate negotiation, tracking and tracing and interfacing with ERP sys-

tems. Table 7 highlights the ten services identified as being the most important for Taiwanese and UK customers. The results

suggest a match in that seven of the 10 services coincide: outbound distribution, order fulfilment, rate negotiation, tracking

and tracing, interfacing with ERP systems, logistics information systems and storage.

4.2. Importance of 3PL operational performance to customers

For Taiwanese customers, the results in Table 8 indicate that on time and accurate delivery was the most-important item,

followed by undamaged state delivery and higher customer satisfaction. For UK customers, on time and accurate delivery

was viewed as the most important aspect of operational performance by respondents, followed by undamaged state delivery,

and higher customer satisfaction. Table 8 presents an importance ranking and highlights the top five most-important items.

The results suggest a match in that four of the five items coincide: on time and accurate delivery, undamaged state delivery,

higher customer satisfaction and to deliver expedited shipments/speed of delivery.

4.3. Analysis of the types of 3PL service provision

In order to classify the 3PLs according to their service capabilities, a cluster analysis was undertaken using the 32 items

using their original raw values as shown in Appendix A. There are two approaches that are most-widely used for this pro-

cedure, namely, the hierarchical method and the non-hierarchical method. A combination method using a hierarchical ap-

proach followed by a non-hierarchical approach was strongly suggested by researchers ( Cagliano et al., 2003; Frohlich and

Dixon, 2001; Hair et al., 2006; Ketchen and Shook, 1996; Lai et al., 2007). The hierarchical method determines the most suit-

able number of clusters and profile cluster centers that serve as initial cluster centers in the non-hierarchical method, while

the non-hierarchical method assigns the respondents into the most appropriate clusters (Hair et al., 2006). In this research, a

hierarchical cluster analysis, by way of Wards (1963) partitioning technique and the Squared Euclidean Distance-method,

was used, and allowed the determination of the most suitable number of clusters. All responding firms were assigned ini-

tially to these clusters. A non-hierarchical technique, namely, K-means cluster analysis was subsequently used to re-assign

the respondents into the most appropriate clusters through an iterative process.

4.3.1. Taiwan

Through the combination of the hierarchical and non-hierarchical approaches, the 83 responding firms (in order to con-

duct a K-means cluster analysis, nine of the 92 were excluded due to missing data) were assigned to three clusters: 30 incluster 1, 34 in cluster 2 and 19 in cluster 3. One-way ANOVA was used to examine which of the service capabilities differed

across the clusters. All 32 items were found to significantly differ across the three clusters.

The results for the three-cluster solution are shown in Fig. 3. The relative magnitude of the 32 service capabilities across

the three clusters is interpreted as high (meanP 5), medium (meanP 3 and


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4.3.2. UKIn order to classify the 3PLs according to their service capabilities, the cluster analysis was repeated. The 92 responding

firms (in order to conduct a K-means cluster analysis, 20 of the 112 were excluded due to missing data) were assigned to

three clusters: 36 in cluster 1, 21 in cluster 2 and 35 in cluster 3. One-way ANOVA was used to examine which of the service

capabilities differed across the three clusters. 27 items were found to significantly differ. Only five items (s1: inbound trans-

portation, s2: outbound distribution, s6: freight forwarding, s29: billing the final customer and s30: insurance service) did

not significantly differ across the three clusters.

The results for the three-cluster solution are shown in Fig. 4. The first type (cluster 1, n = 36) accounts for 39.1% of the

sample. These types of 3PL achieved a medium-level of capability concerning transportation-related services (s1, and s3

s6), warehousing-related (s9s13), value-added services (s14s17), information technology (s21s25), finance-related

(s27s30) and other services (s31 and s32). Compared to cluster 3, they have a much higher capability in warehousing-re-

lated, valued-added and information technology-related services. It appears that firms of this type are making efforts to im-

prove their competitiveness in these areas.

The second type (cluster 2, n = 21, 22.8%) possesses a high level of capability in most of the 32 logistics service items. Thissuggests that they are comprehensive 3PLs.

The final type of 3PL (cluster 3, n = 35, 38.0%) possesses a medium-level of capability in carrying out the three aspects of

transportation-related service (s1, s2 and s4), the one aspect of warehousing-related (s7), all of the finance-related services

(s27s30) and one aspect of other services (s32). This type of 3PL was weak in most warehousing-related, value-added, and

information technology aspects of provision. It appears that firms of this type are traditional transportation companies.

4.4. Analysis of the performance of 3PL service provision

To determine if 3PL clusters differ in financial and operational performance, a one-way analysis of variance (ANOVA) was

performed. The ANOVA results reported in Table 9 indicate that statistically significant differences, that is, p < 0.05, existed

among the three 3PL clusters in some of the operational performance items. In Taiwan, all of the financial performance items

and one aspect of operational performance (o11: to operate with low overall operating cost as a percentage of sales) did not

significantly differ across the three clusters. Thus, results do not support H1 and partially support H2. In the UK, all of the

Fig. 3. Cluster analysis solution for 3PLs in Taiwan. 1. Inbound transportation, 2. Outbound distribution, 3. Merge in transit, 4. Rate negotiation, 5. Carrier

selection, 6. Freight forwarding, 7. Storage, 8. Storage of special requirements, 9. Inventory management, 10. Pick and pack, 11. Order fulfilment, 12. Cross-

docking, 13. Product returns, 14. Labelling/marking, 15. Packaging, 16. Relabelling/repackaging, 17. Simple processing, 18. Bar code scanning, 19. RFID20.

EDI capability, 21. Electronic commerce, 22. Tracking and tracing, 23. Logistics information systems, 24. Order management systems, 25. Selection of

software, 26. Interfacing with ERP systems, 27. Invoicing/billing function, 28. Freight bill auditing/payment, 29. Billing the final customer, 30. Insurance

service, 31. Consulting services, 32. Management reports.



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financial performance items and nine operational performance items (o1o3, and o611) did not significantly differ across

the three clusters. Thus, these results do not support H1 but partially support H2.

Table 10 shows chi-square test results for the characteristics of respondents firms in terms of age, total sales volume and

number of employees. In Taiwan, nearly 15.7%, 10.8%, and 9.6% of respondents firms in clusters 1, 2, and 3, respectively, had

been operating for more than 20 years. Furthermore, 4.8%, 12%, and 7.2% of respondents firms in clusters 1, 2, and 3, respec-

tively, had been operating for 10 years or less. Table 10 also shows that 14.8% of respondents in cluster 1 reported that their

firms total sales volume was over 10 Million UK Pounds, compared with 17% and 21% of respondents in clusters 2 and 3,

respectively. Furthermore, 19.8% of respondents in cluster 1 reported that their firms total sales volume was below 1 Million

UK Pounds compared with 12.4% in cluster 2 and 9.8% in cluster 3. With respect to number of employees, nearly 7.3%, 4.8%,

and 4.9% of respondents firms in clusters 1, 2, and 3, respectively, had 201 or more employees. Moreover, 28%, 23.2%, and

13.4% of respondents firms in clusters 1, 2, and 3, respectively, had 100 or less employees. Results of the chi-square analysis

revealed that the number of employees significantly differed across the three clusters at the p < 0.05 significance level.

In the UK, nearly 24.4%, 17.8%, and 17.8% of respondents firms in clusters 1, 2, and 3, respectively, had been operating formore than 20 years. Furthermore, 6.6%, 2.2%, and 8.8% of respondents firms in clusters 1, 2, and 3, respectively, had been

operating for 10 years or less. Table 10 also shows that 15.4% of respondents in cluster 1 reported that their firms total sales

volume was over 10 Million UK Pounds, compared with 13.2% and 7.7% of respondents in clusters 2 and 3, respectively. Ele-

ven per cent of respondents in cluster 3 reported that their firms total sales volume was below 1 Million UK Pounds com-

pared with 3.3% in cluster 1 and 0% in cluster 2. With respect to number of employees, nearly 9.8%, 9.8%, and 3.3% of

respondents firms in clusters 1, 2, and 3, respectively, had 201 or more employees. 23.9%, 7.6%, and 23.5% of respondents

firms in clusters 1, 2, and 3, respectively, had 100 or less employees. Results of the chi-square analysis revealed that total

sales volume and number of employees significantly differed across the three clusters at the p < 0.05 significance level.

4.5. An analysis of the relationship between operational and financial performance

Does enhanced operational performance of 3PLs lead to improved financial performance? The following regression equa-

tion provides insight into this relationship:

Fig. 4. Cluster analysis solution for 3PLs in the UK. 1. Inbound transportation, 2. Outbound distribution, 3. Merge in transit, 4. Rate negotiation, 5. Carrier

selection, 6. Freight forwarding, 7. Storage, 8. Storage of special requirements, 9. Inventory management, 10. Pick and pack, 11. Order fulfilment, 12. Cross-

docking, 13. Product returns, 14. Labelling/marking, 15. Packaging, 16. Relabelling/repackaging, 17. Simple processing, 18. Bar code scanning, 19. RFID, 20.

EDI capability, 21. Electronic commerce, 22. Tracking and tracing, 23. Logistics information systems, 24. Order management systems, 25. Selection of

software, 26. Interfacing with ERP systems, 27. Invoicing/billing function, 28. Freight bill auditing/payment, 29. Billing the final customer, 30. Insurance

service, 31. Consulting services, 32. Management reports.



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Table 9

ANOVA analysis of performance differences across the three clusters.

Items Taiwan clusters UK clu

1

(n = 30)

2

(n = 34)

3

(n = 19)

F Scheffe

testb1

(n = 36

f1. Gross profit margin 3.72 3.94 4.56 3.009 4.57

f2. Sales growth 3.87 4.15 4.21 0.480 4.36

o1. To deliver expedited shipments/speed of delivery 4.85 4.94 6.11 10.403* (1, 3) (2, 3) 5.26

o2. To offer short delivery lead-time 5.00 5.03 5.95 5.844* (1, 3) (2, 3) 5.50

o3. On time and accurate delivery 5.21 5.15 6.11 5.293* (1, 3) (2, 3) 5.64

o4. Higher customer satisfaction ratings 5.21 5.35 6.26 7.588* (1, 3) (2, 3) 5.61

o5. To enhance customer success 4.85 4.79 6.00 6.761* (1, 3) (2, 3) 5.31

o6. Lower customer complaints (percentage of total sales) 5.13 5.06 6.05 6.347* (1, 3) (2, 3) 5.53

o7. To deliver goods in an undamaged state 5.30 5.15 6.05 4.245* (2, 3) 5.77

o8. To accommodate special or non-routine requests 4.93 5.15 5.95 4.240*

(1, 3) 6.11o9. To handle unexpected events 5.30 5.47 6.16 4.960* (1, 3) (2, 3) 6.00

o10. To provide quicker response to customers 5.20 5.35 6.21 5.680* (1, 3) (2, 3) 5.89

o11. To operate with low overall operating cost as a percentage of sales 4.10 4.22 4.89 2.885 4.67

o12. To improve the rate of utilization of facilities/equipment/manpower in providing the

services

4.43 4.76 5.63 7.685* (1, 3) (2, 3) 4.86

o13. Aggressiveness in increasing the value-added content of services 4.46 5.06 6.00 12.884* (1, 3) (2, 3) 4.63

o14. Aggressiveness in the reduction of order cycle time 4.33 4.88 5.56 7.160* (1, 3) 4.57

o15. To provide new and better services/speed of introduction for new services 4.43 5.09 6.16 15.708* (1, 3) (2, 3) 5.00

Overall operational performancea 4.85 5.03 5.94 13.789* (1, 3) (2, 3) 5.36

Note: Boldface indicates the highest values across the three clusters.a Means the average of all aspects of operational performance.b Pairwise differences shown are significant at the 0.05 level.

*

Represents significant level p < 0.05.


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Financial Performance b0 b1 Operational Performance

Within this regression equation, operational performance represents a measure of the 3PLs operational performance in

the important areas of cost, quality, delivery, flexibility, and innovation. An unweighted linear average of item mean scores

was used to calculate operational performance. The results of the regression analysis are shown in Table 11.

Both in the UK and Taiwan, the overall model F-values are over 12, which are significant at thep = .01 level. Moreover, all

of the parameter estimates for operational performance are significant atp = .01. These results provide significant support for

the hypothesis that developing a strong operational performance for 3PLs can lead to enhanced financial performance. Over-

all, the regression results demonstrate a strong relationship between 3PLs abilities to establish a better operational perfor-

mance and their abilities to achieve high levels of financial performance both in the UK and Taiwan. Thus, the results support

H3.

4.6. Results of mediator multiple regression analysis

There is a lack of support for the relationship between service capabilities and financial performance, but a significant

relationship exists between operational performance and financial performance. The results imply that service capabilities

may influence operational performance and, in turn, indirectly affect the financial performance of 3PLs. A further analysis

was conducted to determine whether operational performance plays an intermediary role.

A three-step regression analysis was used to assess the potential mediating influence of operational performance on the

service capabilities-financial performance relationship, suggested by Baron and Kenny (1986): (1) regressing the mediator

on the independent variable; (2) regressing the dependent variable on the independent variable; (3) regressing the

dependent variable on both the independent variable and on the mediator. Complete mediation means the regression coef-

ficients are significant in steps (1) (condition 1) and (2) (condition 2), and in the step (3), the mediator is significantly asso-

ciated with the dependent variable but the independent variable is not significantly associated with the dependent variable

(condition 3) (Baron and Kenny, 1986; da Silveira and Cagliano, 2006).

Table 10

Chi-square tests results for the characteristics of respondents firms in clusters 13.

Characteristics Taiwan clusters UK clusters

1 2 3 Chi-square

value (df)

1 2 3 Chi-square

value (df)Firms %a Firms % Firms % Firms % Firms % Firms %

Age of firm

Less than

5 years

2 2.4 1 1.2 3 3.6 10.73 (8) 2 2.2 2 2.2 4 4.4 8.447 (8)

510 years 2 2.4 9 10.8 3 3.6 4 4.4 0 0.0 4 4.4

1115 years 8 9.6 7 8.4 4 4.8 4 4.4 2 2.2 6 6.7

1620 years 5 6.0 8 9.6 1 1.2 4 4.4 0 0.0 4 4.4

More than

20 years

13 15.7 9 10.8 8 9.6 22 24.4 16 17.8 16 17.8

Total 83 90 (missing data = 2)

Total sales volume (million UK pounds)

Less than 0.25 6 7.4 5 6.2 1 1.2 18.08 (14) 0 0.0 0 0.0 4 4.4 25.03*(14)

0.250.5 8 9.9 3 3.7 3 3.7 1 1.1 0 0.0 2 2.2

0.51 2 2.5 2 2.5 4 4.9 2 2.2 0 0.0 4 4.4

12 2 2.5 6 7.4 1 1.2 6 6.6 1 1.1 8 8.8

210 8 9.9 11 13.6 4 4.9 13 14.3 7 7.7 10 11.0

1020 2 2.5 2 2.5 4 4.9 5 5.5 3 3.3 3 3.3

20100 1 1.2 4 4.9 1 1.2 6 6.6 2 2.2 2 2.2

Above 100 1 1.2 0 0.0 0 0.0 3 3.3 7 7.7 2 2.2

Total 82 (missing data = 1) 91 (missing data = 1)

Full-time

Employees

Less than 101 23 28.0 19 23.2 11 13.4 15.94* (8) 22 23.9 7 7.6 29 31.5 22.76*(8)

101200 0 0.0 11 13.4 4 4.9 5 5.4 5 5.4 3 3.3

201500 4 4.9 2 2.4 4 4.9 4 4.3 1 1.1 0 0.0

5011000 2 2.4 1 1.2 0 0.0 3 3.3 1 1.1 1 1.1

Above 1000 0 0.0 1 1.2 0 0.0 2 2.2 7 7.6 2 2.2

Total 82 (missing data = 1) 91 (missing data = 1)

Note: One UK Pound equals approximately 60.0 New Taiwanese (NT) dollars in 2006.a % of total.

* Represents significant level p < 0.05.



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Operational performance and financial performance are the same variables used in the previous regression. Service capa-

bilities (i.e., service capabilities importance in Tables 12 and 13) were calculated using a weighted linear value. The impor-

tance of 3PL service capabilities to customers according to Table 7 is used to determine the weighting for all of the service

capabilities.

Tables 12 and 13 present the results of the three-step regression. Both in the UK and Taiwan, the significant coefficients in

model 1 support condition 1, while model 2 indicates the satisfaction of condition 2. Model 3 reveals the insignificant effect

of service capabilities importance on financial performance when operational performance is included. That is, 3PLs that

foster service capabilities which correspond to customers key priorities can influence the financial performance of 3PLs

through enhanced operational performance. Thus, the results support H4.

5. Conclusions and recommendations

Many 3PL providers have broadened their activities to provide an extended range of services. The objective of this study

was to evaluate the relationship between 3PL performance and service provision in order to provide guidance for 3PLs to be

able to take advantage of the full business potential afforded to them and for mitigating investment risks. The research con-

cerned the undertaking of a comparative analysis between the UK and Taiwan form both a customers and providers per-

spective. The main findings and response to hypotheses can be seen in Table 14. It is noted that the hypotheses results

are the same for both the UK and Taiwan.

A positive and significant relationship was found between operational performance and the 3PLs financial performance

(H3) in both countries. This finding suggests that if 3PL administrators can improve their operational performance, they will

increase the financial performance of the firm. It implies that customers will be more willing to use their services. The find-

ings are consistent with a previous study (Yeung et al., 2006). The influences of service provision on 3PL operational perfor-

mance (H2) were partially supported both in Taiwan and the UK. It appears that 3PL clusters with a wide range of service

provision generally have better operational performance. Results showed the ratings differed significantly in 14 of the 15

aspects of operational performance in Taiwan and six of the 15 of those in the UK. In contrast to Taiwans 3PLs, aligning high

levels of operational performance with quality (i.e., o4 and o5) and innovation (i.e., o13o15) is a necessary strategy for the

UKs 3PLs.

Although the impact of service provision on the 3PL providers financial performance (H1) was not supported either in

Taiwan or the UK, the relationship between service capabilities which correspond to the key priorities of customers and

financial performance is mediated by operational performance for 3PLs (H4). To sum up, the range of service provision of-

fered by 3PLs cannot directly influence the 3PLs financial performance. Through a better operational performance, 3PL pro-

viders with a broader range of service provision that correspond to the key priorities of customers will gain superior financial

performance both in Taiwan and the UK.

Table 11

Regression results for the effect of operational performance on 3PLs financial performance.

Dependent variables

Taiwan UK

Independent variable Gross profit margin Sales growth Gross profit margin Sales growth

Operational performance (p) 0.591 (0.000) 0.474 (0.000) 0.376 (0.000) 0.315 (0.001)

Model F (p) 46.189 (0.000) 26.057 (0.000) 17.987 (0.000) 12.138 (0.001)

R2 0.349 0.225 0.142 0.099

Standardized beta-coefficients are reported with p-values in parentheses.

Table 12

Results of mediator multiple regression analysis in Taiwan.

Dependent variables

Gross profit margin considered Sales growth considered

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

Independent variable Operational

performance

Gross profit

margin

Gross profit

margin

Operational

performance

Sales

growth

Sales growth

Service

capabilities importance

(p)

0.563 (0.000) 0.324 (0.002) 0.013 (0.905) 0.563 (0.000) 0.218

(0.037)

0.072

(0.526)

Operational performance (p) 0.598 (0.000) 0.514

(0.000)

Model F (p) 41.718 (0.000) 10.089 (0.002) 22.837 (0.000) 41.718 (0.000) 4.480

(0.037)

13.154

(0.000)

R2 0.317 0.105 0.350 0.317 0.047 0.228




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Several contributions have been made by this study to both the theory and practice of logistics management. Firstly, this

study provides a theoretical framework to link service capability, operational performance and financial performance for the

3PLs. Secondly, researchers suggested that there has been relatively little attention given to empirical studies of 3PLs and

their customers (Murphy and Poist, 2000). This study not only assesses the relationship between service capabilities and per-

formance for 3PLs but also investigates the impacts of the service capabilities of 3PL providers which correspond to custom-

ers key priorities on financial performance for 3PL providers. Thirdly, while multi-region studies have received limited

attention in the logistics field (Luo et al., 2001), this study provides the results of a comparative analysis between Taiwan

and the UK and reveals that both countries have certain fundamental similarities but also some clear differences in their

logistics practices. Compared with Taiwans 3PLs, aligning high levels of operational performance with quality and innova-

tion is a necessary strategy for the UK. As researchers have pointed out: To establish more firm conclusions, studies must

conduct parallel (multi-region) studies, with the same sample design and questionnaire. Such studies will be very important

for understanding how context influences the outsourcing practice and shapes 3PL services (Arroyo et al., 2006).

The study findings have implications for practice and research. First, the results are of benefit to current customers as the

list of 13 aspects of operational performance and 32 different service capabilities can help them identify what they can ex-

pect from 3PLs. Second, the results suggest that excellence in operations is more important than wide-ranging service pro-

vision. Through better operational performance, 3PL providers with a broader range of service provision which correspond to

customers key priorities will gain superior financial performance. Finally, logistics providers could use the study results to

modify their current strategies to more accurately meet customers needs.

The study findings, however, suffer from several limitations. First, this research was limited to the study of logistics

markets in the UK and Taiwan. Secondly, the research sample for customers was drawn from large manufacturing firms

in Taiwan and the UK. Therefore, the conclusions inferred can only be generalized to include large manufacturing firms in

Taiwan and the UK. Thirdly, the actual financial performance data of 3PLs were difficult to obtain due to the fact that the

majority of such companies are not publicly listed. Therefore, this study used perceptual measures to measure the 3PLs

financial performance. Finally, the respondent firms were asked to evaluate their perceived performance and service capa-

bilities in logistics at a single point in time. The expectations could change over time and the related measurement should

also change (Brooks, 2000).

Several important issues for further research are suggested and are detailed below: first, the resource-based view (RBV)

established a theoretical base for this study. However, the core criteria of resources, namely, valuable, rare and imperfectly

imitable were not well considered in this research. Researchers indicated that in order to be a source of competitive advan-

tage and above-average performance, resources must meet these three criteria (Combs and Ketchen, 1999; Powell, 1992;

Rindova and Fombrun, 1999). Future research could attempt to identify the resources with these criteria and examine their

Table 13

Results of mediator multiple regression analysis in the UK.

Dependent variables

Gross profit margin considered Sales growth considered

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

Independent variable Operational

performance

Gross profit

margin

Gross profit

margin

Operational

performance

Sales

growth

Sales

growth

Servicecapabilities importance (p)

0.471 (0.000) 0.203 (0.033) 0.033 (0.743) 0.471 (0.000) 0.231(0.014)

0.106(0.302)

Operational performance (p) 0.361 (0.001) 0.265

(0.011)

Model F (p) 31.338 (0.000) 4.486 (0.033) 8.974 (0.000) 31.338 (0.000) 6.210

(0.014)

6.610

(0.002)

R2 0.222 0.041 0.143 0.222 0.053 0.108


Table 14

Summary of results.

Hypotheses Supported/not-support

Taiwan UK

H1 3PL providers can be clustered in terms of service provision. The range of service provision offered by 3PL

providers is positively related to their financial performance

Not

supported

Not

supported

H2 3PL providers can be clustered in terms of service provision. The range of service provision offered by 3PL

providers is positively related to their operational performance

Partially

supported

Partially

supported

H3 3PL providers whose operational performance is high have better financial performance compared to those

with a lower operational performance

Supported Supported

H4 For 3PLs, the relationship between service capabilities corresponding to customers key priorities and financial

performance is mediated by operational performance

Supported Supported



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effects on the 3PLs financial performance. Second, future research could concern qualitative case studies to deeply under-

stand the development of service capabilities and operational performance for 3PLs. Third, structural equation modelling

(SEM) can examine a series of dependence relationships simultaneously (Hair et al., 2006). This approach could be used

to understand if there are any cause and effect relationships between the service dimensions and performance. Fourth, fu-

ture research could examine the differences between the UK and Taiwanese samples along the 3PL dimensions. Fifth, this

study was limited to assess the 3PLs within a particular national industry. Future research could undertake a broader study

to enhance the generic applicability of the results. Finally, in this research the data was collected at one point in time, future

empirical efforts in the area might consider the use of longitudinal research design to reveal how perceptions of service capa-

bilities and operational performance change over time.

Acknowledgements

We thank the three anonymous reviewers for corrections and helping to improve the manuscript.

Appendix A. Items used for developing scales

A.1. Financial performance

For the 3PLs questionnaire: Respondents were asked to provide a rating of the companys performance relative to the

industry average using a seven-point Likert scale anchored by 1 = much worse and 7 = much better.

For the customers questionnaire: N/A

f1. Gross profit margin

f2. Sales growth

A.2. Service capabilities

For the 3PLs questionnaire: Respondents were asked to provide a rating of a companys satisfaction level with the service

capabilities using a seven-point Likert scale anchored by 1 = much worse and 7 = much better.

For the customers questionnaire: Respondents were asked to rate the appropriate number, for B2B services, how impor-

tant each item was considered to make the selection of a third party when their companies decided to outsource logistics.

Attitudes to each of the variables were assessed by using a seven-point Likert-type scale, 1 being very unimportant and 7

very important.

s1. Inbound transportation

s2. Outbound distribution

s3. Merge in transit

s4. Rate negotiation

s5. Carrier selection

s6. Freight forwarding

s7. Storage

s8. Storage of special requirements

s9. Inventory management

s10. Pick and pack

s11. Order fulfilment

s12. Cross-docking

s13. Product returnss14. Labelling/marking

s15. Packaging

s16. Relabelling/repackaging

s17. Simple processing

s18. Bar code scanning

s19. RFID

s20. EDI capability

s21. Electronic commerce

s22. Tracking and tracing

s23. Logistics information systems

s24. Order management systems

s25. Selection of software

s26. Interfacing with ERP systems



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s27. Invoicing/billing function

s28. Freight bill auditing/payment

s29. Billing the final customer

s30. Insurance service

s31. Consulting services

s32. Management reports

A.3. Operational performance

For the 3PLs questionnaire: Respondents were asked to provide a rating of the companys performance relative to the

industry average using a seven-point Likert scale anchored by 1 = much worse and 7 = much better.

For the customers questionnaire: Compared with the 3PLs questionnaire, two items (o11, o12) were omitted because

they were only used to assess the cost aspect of operational performance for 3PLs. Respondents were asked to rate the appro-

priate number, for B2B services, how important each item was considered to make the selection of a third party when their

companies decided to outsource logistics. Attitudes to each of the variables were assessed by using a seven-point Likert-type

scale, 1 being very unimportant and 7 very important.

o1. To deliver expedited shipments/speed of delivery.

o2. To offer short delivery lead-time.

o3. To offer greater proportion of on time and accurate delivery.

o4. To provide higher customer satisfaction ratings.o4. Higher customer satisfaction ratings.

o5. To enhance customer success.

o6. Lower customer complaints (percentage of total sales).

o7. To deliver goods in an undamaged state.

o8. To accommodate special or non-routine requests.

o9. To handle unexpected events.

o10. To provide quicker response to customers.

o11. To operate with low overall operating cost as a percentage of sales.

o12. To improve the rate of utilization of facilities/equipment/manpower in providing the services.

o13. Aggressiveness in increasing the value-added content of services.

o14. Aggressiveness in the reduction of order cycle time.

o15. To provide new and better services/speed of introduction for new services.

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