Analysis of Third Party Logistics Performance

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Transportation Research Part E 47 (2011) 547–570

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Transportation Research Part E
journal homepage: www.elsevier.com/locate/tre

An analysis of third-party logistics performance and service provision
Chiung-Lin Liu a,⇑, Andrew C. Lyons b
a b

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

a r t i c l e

i n f o

a b s t r a c t
The aim of the research described in this paper is to evaluate the relationship between the service capabilities and performance of UK and Taiwanese third-party logistics (3PL) providers. A study is presented based on a recent survey. The results identify the most important 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 service 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 performance. Similarities and differences between logistics practices in the UK and Taiwan are highlighted. Ó 2010 Elsevier Ltd. All rights reserved.

Article history: Received 5 June 2009 Received in revised form 29 May 2010 Accepted 26 August 2010

Keywords: Third-party logistics Service capabilities Performance

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 outsourcing 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 despite 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, practitioners would beneﬁt from understanding any correlation that exists between 3PL performance and different types of service 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 company 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
⇑ Corresponding author. Tel.: +886 983908146 (Taiwan).
E-mail addresses: [email protected] (C.-L. Liu), [email protected] (A.C. Lyons). 1366-5545/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi: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 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). 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 economies, 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 concerning the impact of different forms of service provision, ﬁll a series of gaps in the literature and extend the prevailing theory 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 warehousing, 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 ﬁrm’s 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 ﬁrm’s 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 capabilities. 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 linkage between logistics capabilities and logistics and ﬁnancial performance. Resources were deﬁned as a bundle of behaviourbased capabilities including information systems’-related capabilities and benchmarking and ﬂexibility expertise. The results
Table 1 The UK and Taiwanese economy in 2007. UK GDP: per capita (purchasing power parity, PPP) ($) GDP: composition by sector (%) Agriculture Industry Services 36,500 1.3 24.2 74.5 Taiwan 31,100 1.7 25.1 73.2

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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 organizational learning enhanced the ﬁrms’ ﬁnancial performance. It is evident from such studies that researchers have found logistics-related capabilities to be positively related to operational 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 provider’s service capabilities should meet the customer’s 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 Poist’s (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 thirdparty 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 customer 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 performance’’ (Lu and Yang, 2009). Thus, wide-ranging service provision may have a positive impact on the ﬁnancial performance 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 organization’s processes, such as reliability, speed of delivery and quality of service (Voss et al., 1997). Aside from minor differences in semantics, there is broad consensus 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 logistics-related publication which has discussed operational performance from both a 3PL provider and a 3PL customer perspective. 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 researchers 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 impact 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.

Table 2 Key service capabilities (provider perspective). Title/item Lieb and Randall (1996) Lieb and Randall (1999) Murphy and Poist(2000)a van Hoek (2000a) van Hoek (2000b, 2001) Larson and Gammelgaard (2001) w w w w w w Lieb and Kendrick (2003) Lai et al. (2004) Lieb and Bentz (2005a) Stefansson (2006)

550

Transportation-related Inbound transportation Outbound distribution Overseas sourcing Overseas distribution Merge in transit/freight (de)consolidation Direct transportation service Expedited delivery Emergency transport Transportation planning and management – related Fleet operation and management Route and network optimization/shipment planning Rate negotiation Carrier selection Freight forwarding/freight brokering Warehousing/inventory-related Warehousing/storage with good reception Customer spare parts Storage of products with special requirements (e.g., preparation for freezing and thawing) Inventory management/inventory replenishment Bonded warehousing Pick and pack Order processing Order fulﬁlment Cross-docking Product testing/inspection/quality control Product returns Reverse logistics Value-added services Labelling/marking Packaging Relabelling/repackaging Kitting Assembly/re-assembling/installation Production/selected manufacturing activities/ customization Repair Information technology Bar code scanning RFID Electronic commerce Tracking and tracing shipment information Logistics information systems Order entry/management systems Selection of software Interfacing with ERP systems; e.g., SAP

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C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570

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Product design and marketing support-related Packaging design Product conﬁguration/product design Promotional support Exhibition Finance-related Invoicing/billing function Freight bill auditing/payment Billing the ﬁnal customer Factoring/ﬁnancing service Insurance service Consulting services Logistics planning Supply chain design Other customer service Customs brokerage Call center operation/after sales service Management/performance reports Procurement of materials (e.g., purchase of lowerlevel materials, packaging materials and inventory)
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w C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570

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

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Table 3 Key service capabilities (customer perspective).
Murphy Peters Boyson Bhatnagar and et al. et al. et al. Poist (1998) (1999) (1999) (1998) Murphy and Poist (2000)a van Laarhoven et al. (2000) Sohail Knemeyer Sohail Hong and et al. et al. et al. Sohal (2003) (2004) (2004) (2003) Wilding and Juriado (2004) Lieb Vaidyanathan Sohail and (2005) and AlBentz Abdali (2005b) (2005) Knemeyer Arroyo and et al. Murphy (2006) (2005)

Title/item

Rao Rao Sink Dapiran Sink Gooley Millen et al. and et al. et al. and (1997) et al. (1993) Young (1996) (1996) Langley (1997) (1994) (1997)

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Transportation-related Inbound transportation Outbound distribution Overseas sourcing Overseas distribution Merge in transit/Freight (de)consolidation Direct transportation service Expedited delivery Emergency transport

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Transportation Planning & Management-Related Fleet operation and management Route & network optimization/ Shipment planning Rate negotiation Carrier selection Freight forwarding/Freight brokering w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w

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Warehousing/Inventory-Related Warehousing/Storage with good reception Customer spare parts Storage of products with special requirements (e.g., preparation for freezing and thawing) Inventory management/Inventory replenishment Bonded warehousing Pick and pack Order processing Order fulﬁlment Cross-docking Product testing/inspection/quality control Product returns Reverse logistics w w w w w w w w w w w w w w w w w w w w w w w w w

w

C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570

w w

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Value-Added Services Labelling/Marking Packaging Relabelling/Repackaging Kitting Assembly/Re-assembling/ Installation Production/Selected manufacturing activities/Customization Repair

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Information Technology Bar code scanning RFID Electronic commerce Tracking and tracing shipment information Logistics information systems Order Entry/management systems Selection of software w w

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Interfacing with ERP systems; e.g., SAP

Product Design and marketing support- related Packaging design Product conﬁguration/Product design Promotional support Exhibition w w w w w w w w w w w w w w w

w

w

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Finance-Related Invoicing/Billing function Freight bill auditing/payment Billing the ﬁnal customer Factoring/Financing service Insurance service w w w w w

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Consulting Services Logistics planning Supply chain design w w w w w w w w

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Other Customer Service Customs brokerage Call center operation/After sales service Management/performance reports Procurement of materials (e.g., purchase of lower-level materials, packaging materials and inventory) w w w w

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a

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

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Table 4 Key items of operational performance (provider/customer perspective). Title/item Provider perspective Brook (1999a,b, 2000a) Delivery To deliver expedited shipments To offer short delivery lead-time To offer greater proportion of on time and accurate delivery Quality To provide higher customer satisfaction ratings To enhance customer success (e.g., helping customers in value analysis, cost reductions, problem solving, etc.) Lower customer complaints (percentage of total sales) To deliver goods in an undamaged state Flexibility To accommodate special or non-routine requests To handle unexpected events To provide quicker response to customers Cost To operate with low overall operating cost as a percentage of sales To improve the rate of utilization of facilities/equipment/ manpower in providing the services Innovation Aggressiveness in increasing the value-added content of services Aggressiveness in the reduction of order cycle time To provide new and better services
a

Customer perspective Stank et al. (2003) Lai and Cheng (2003) and Lai et al. (2004) Lai (2004) Panayides and So (2005) Brah and Lim (2006) Yeung et al. (2006) w w w w w Panayides (2007) Menon et al. (1998) C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570

van Hoek (2001) w w w

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

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) w w w Morash et al. (1996a) Morash et al. (1996b) Fawcett et al. (1996) Morash and Clinton (1997) w w w w w w C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570 Fawcett et al. (2000) Zhao et al. (2001) Ellinger et al. (2002) Moberg et al. (2004) Rodrigues et al. (2004) Daugherty et al. (2005) Gimenez and Ventura (2005)

Delivery To deliver expedited shipments To offer short delivery lead-time To offer greater proportion of on time and accurate delivery Quality To provide higher customer satisfaction ratings To enhance customer success (e.g., helping customers in value analysis, cost reductions, problem solving, etc.) Lower customer complaints (percentage of total sales) To deliver goods in an undamaged state Flexibility To accommodate special or nonroutine requests To handle unexpected events To provide quicker response to customers Cost To operate with low overall operating cost as a percentage of sales To improve the rate of utilization of facilities/equipment/manpower in providing the services Innovation Aggressiveness in increasing the value-added content of services Aggressiveness in the reduction of order cycle time To provide new and better services

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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 performance 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 information for 3PL providers on the most essential services to enhance 3PL providers’ responsiveness, competitiveness and performance. 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 service 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 operational 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 analysis 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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Step 1

Selection of the performance and service capabilities of 3PL providers Review related literature Questionnaire development Interviews V alidity test Identification of 3PL providers in terms of service provision and their differences in operational and financial performance Cluster analysis ANOV analysis A Evaluation of the relationship between operational performance and financial performance for 3PL providers Simple regression analysis 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.

Step 2

Step 3

Step 4

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 employed 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 sufﬁciently reﬂected the ﬁnancial performance of the 3PL. Previous studies have indicated that there are objective and subjective 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 subjective 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 secondary data on most of the 3PL providers was not readily available. The percentage of the available secondary data for Taiwanese 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 measures 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 performance rating relative to perceived industry averages. The 32 service capability indicators were proposed by referring to previous logistics research (Lai, 2004; Murphy and Poist, 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 questionnaire 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 Economic 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 databases (Reed Business Information, 2007). All of the ﬁrms provided transportation and warehouse-related services.

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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 Dillman’s (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 letter, 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 services 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 services 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 t test 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’ industries 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 t test 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 differences (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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Harman’s 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 variance. 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). Typically, reliability is assessed in three forms: test–retest, alternate-form and internal consistency. Reliability is usually expressed on the basis of the Cronbach’s 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 pairwise 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. Cronbach’s alpha Taiwan Provider Financial performance Operational performance Service capabilities 0.890 0.936 0.947 Customer – 0.916 0.952 UK Provider 0.605 0.849 0.916 Customer – 0.879 0.920

Table 7 Importance of 3PL service capabilities to customers. Taiwan Rank 1 2 3 4 5 6 7 8 9 10 Service capabilities s22. Tracking and tracing s2. Outbound distribution s23. Logistics information systems s11. Order fulﬁlment s4. Rate negotiation s24. Order management systems s1. Inbound transportation s18. Bar code scanning s7. Storage s26. Interfacing with ERP systems Mean 6.296 6.119 6.066 6.053 6.037 6.036 5.935 5.824 5.741 5.710 UK Rank 1 2 3 4 5 6 7 8 9 10 Service capabilities s2. Outbound distribution s11. Order fulﬁlment s4. Rate negotiation s22. Tracking and tracing s26. Interfacing with ERP systems s23. Logistics information systems s7. Storage s32. Management reports s9. Inventory management s29. Billing the ﬁnal customer Mean 6.288 6.119 5.966 5.774 5.770 5.690 5.689 5.669 5.593 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 fulﬁlment and rate negotiation (mean scores were over 6.0, derived from a seven-point scale where 1 represented very unimportant 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 systems. 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 procedure, namely, the hierarchical method and the non-hierarchical method. A combination method using a hierarchical approach 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 suitable 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 Ward’s (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 initially 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 conduct a K-means cluster analysis, nine of the 92 were excluded due to missing data) were assigned to three clusters: 30 in cluster 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 (mean P 5), medium (mean P 3 and <5) and low (mean < 3), respectively. The ﬁrst type (cluster 1, n = 30) accounted for 36.1% of the sample. This type of 3PL was weak in most warehousing-related services (s8, s9, s10, s12 and s13), all value-added (s14–s17), all information technology (s18–s26) and some other services (s29, s31 and s32). It possesses a medium-level of capability in carrying out all transportation-related (s1–s6) and some ﬁnancial-related services (s27 and s28). It appears that ﬁrms of this type are traditional transportation companies. At 40.1% (cluster 2, n = 34) of the sample, the second type of 3PL achieved a medium-level of capability to perform most transportation-related (s2 and s3–s6), warehousing-related (s7, s9–s13), all valued-added (s14–s17), most information technology (s18–s19, s21–25) services, all ﬁnance-related (s27–s29) and other services (s31 and 32). Compared to the traditional transportation companies, they have a much higher capability in warehousing-related, valued-added and information technology-related services. It appears that ﬁrms of this type are making efforts to improve their competitiveness in these areas. The ﬁnal type of 3PL (cluster 3, n = 31, 33.7%) possesses a high level of capability in most of the 32 logistics service items. This suggests that they are comprehensive 3PLs.

Table 8 Importance of 3PL operational performance to customers. Taiwan Rank 1 2 3 4 5 Operational performances o3. On time and accurate delivery o7. Undamaged state delivery o4. Higher customer satisfaction o2. Short delivery lead-time o1. To deliver expedited shipments/speed of delivery Mean 6.50 6.34 6.18 6.13 6.13 UK Rank 1 2 3 4 5 Operational performances o3. On time and accurate delivery o7. Undamaged state delivery o4. Higher customer satisfaction o6. Lower customer complaints (percentage of total sales) o1. To deliver expedited shipments/speed of delivery Mean 6.62 6.57 6.10 5.99 5.85

Note: Boldface indicates coincident aspects of operational performance identiﬁed by both Taiwan and UK customers.

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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. Crossdocking, 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.

4.3.2. UK In 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 transportation, 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 (s9–s13), value-added services (s14–s17), information technology (s21–s25), ﬁnance-related (s27–s30) and other services (s31 and s32). Compared to cluster 3, they have a much higher capability in warehousing-related, valued-added and information technology-related services. It appears that ﬁrms of this type are making efforts to improve 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. This suggests 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 (s27–s30) 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

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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. Crossdocking, 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.

ﬁnancial performance items and nine operational performance items (o1–o3, and o6–11) 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, respectively, 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 for more 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. Eleven per cent of respondents in cluster 3 reported that their ﬁrms’ total sales volume was below 1 Million UK Pounds compared 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 equation provides insight into this relationship:

Table 9 ANOVA analysis of performance differences across the three clusters. Items Taiwan clusters 1 (n = 30) f1. Gross proﬁt margin f2. Sales growth o1. To deliver expedited shipments/speed of delivery o2. To offer short delivery lead-time o3. On time and accurate delivery 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 Overall operational performancea 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. 3.72 3.87 4.85 5.00 5.21 5.21 4.85 5.13 5.30 4.93 5.30 5.20 4.10 4.43 4.46 4.33 4.43 4.85 2 (n = 34) 3.94 4.15 4.94 5.03 5.15 5.35 4.79 5.06 5.15 5.15 5.47 5.35 4.22 4.76 5.06 4.88 5.09 5.03 3 (n = 19) 4.56 4.21 6.11 5.95 6.11 6.26 6.00 6.05 6.05 5.95 6.16 6.21 4.89 5.63 6.00 5.56 6.16 5.94 F 3.009 0.480 10.403* 5.844* 5.293* 7.588* 6.761* 6.347* 4.245* 4.240* 4.960* 5.680* 2.885 7.685* 12.884 7.160* 15.708* 13.789*
*

UK clusters C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570 Scheffe testb – – (1, 3) (1, 3) (1, 3) (1, 3) (1, 3) (1, 3) (2, 3) (1, 3) (1, 3) (1, 3) – (1, 3) 1 (n = 36) 4.57 4.36 5.26 5.50 5.64 5.61 5.31 5.53 5.77 6.11 6.00 5.89 4.67 4.86 4.63 4.57 5.00 5.36 2 (n = 21) 4.62 5.05 5.52 5.55 5.90 6.05 5.90 5.86 6.05 6.10 6.19 6.14 4.81 5.33 5.38 5.30 5.38 5.70 3 (n = 35) 4.09 4.37 5.26 5.17 5.54 5.49 5.14 5.29 5.50 5.76 5.97 5.62 4.26 4.50 4.49 4.41 4.55 5.13 F 1.630 2.183 0.696 1.325 1.043 3.465* 4.515* 1.763 2.376 1.484 0.422 2.550 1.654 5.081* 5.422* 5.421* 3.814* 6.319* Scheffe test – – – – – (2, 3) (2, 3) – – – – – – (2, 3) (1, 2) (2, 3) (1, 2) (2, 3) (2, 3) (2, 3)

(2, 3) (2, 3) (2, 3) (2, 3) (2, 3) (2, 3)

(2, 3) (2, 3) (2, 3)

(1, 3) (2, 3) (1, 3) (1, 3) (2, 3) (1, 3) (2, 3)

563

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Table 10 Chi-square tests results for the characteristics of respondents’ ﬁrms in clusters 1–3. Characteristics Taiwan clusters 1 Firms Age of ﬁrm Less than 5 years 5–10 years 11–15 years 16–20 years More than 20 years Total Total sales volume Less than 0.25 0.25–0.5 0.5–1 1–2 2–10 10–20 20–100 Above 100 Total Full-time Employees Less than 101 101–200 201–500 501–1000 Above 1000 Total 2 2 8 5 13 83 (million 6 8 2 2 8 2 1 1 UK pounds) 7.4 5 9.9 3 2.5 2 2.5 6 9.9 11 2.5 2 1.2 4 1.2 0 %a 2.4 2.4 9.6 6.0 15.7 2 Firms 1 9 7 8 9 % 1.2 10.8 8.4 9.6 10.8 3 Firms 3 3 4 1 8 % 3.6 3.6 4.8 1.2 9.6 Chi-square value (df) UK clusters 1 Firms 2 4 4 4 22 % 2.2 4.4 4.4 4.4 24.4 2 Firms 2 0 2 0 16 % 2.2 0.0 2.2 0.0 17.8 3 Firms 4 4 6 4 16 % 4.4 4.4 6.7 4.4 17.8 Chi-square value (df)

10.73 (8)

8.447 (8)

90 (missing data = 2)

6.2 3.7 2.5 7.4 13.6 2.5 4.9 0.0

1 3 4 1 4 4 1 0

1.2 3.7 4.9 1.2 4.9 4.9 1.2 0.0

18.08 (14)

0 1 2 6 13 5 6 3

0.0 1.1 2.2 6.6 14.3 5.5 6.6 3.3

0 0 0 1 7 3 2 7

0.0 0.0 0.0 1.1 7.7 3.3 2.2 7.7

4 2 4 8 10 3 2 2

4.4 2.2 4.4 8.8 11.0 3.3 2.2 2.2

25.03*(14)

82 (missing data = 1)

91 (missing data = 1)

23 0 4 2 0

28.0 0.0 4.9 2.4 0.0

19 11 2 1 1

23.2 13.4 2.4 1.2 1.2

11 4 4 0 0

13.4 4.9 4.9 0.0 0.0

15.94* (8)

22 5 4 3 2

23.9 5.4 4.3 3.3 2.2

7 5 1 1 7

7.6 5.4 1.1 1.1 7.6

29 3 0 1 2

31.5 3.3 0.0 1.1 2.2

22.76*(8)

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.

Financial Performance ¼ b0 þ b1 Operational Performance þ
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 the p = .01 level. Moreover, all of the parameter estimates for operational performance are signiﬁcant at p = .01. These results provide signiﬁcant support for the hypothesis that developing a strong operational performance for 3PLs can lead to enhanced ﬁnancial performance. Overall, the regression results demonstrate a strong relationship between 3PLs’ abilities to establish a better operational performance 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 coefﬁcients are signiﬁcant in steps (1) (condition 1) and (2) (condition 2), and in the step (3), the mediator is signiﬁcantly associated 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).

C.-L. Liu, A.C. Lyons / Transportation Research Part E 47 (2011) 547–570 Table 11 Regression results for the effect of operational performance on 3PLs’ ﬁnancial performance. Dependent variables Taiwan Independent variable Operational performance (p) Model F (p) R2 Gross proﬁt margin 0.591 (0.000) 46.189 (0.000) 0.349 Sales growth 0.474 (0.000) 26.057 (0.000) 0.225 UK Gross proﬁt margin 0.376 (0.000) 17.987 (0.000) 0.142

565

Sales growth 0.315 (0.001) 12.138 (0.001) 0.099

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

Operational performance and ﬁnancial performance are the same variables used in the previous regression. Service capabilities (i.e., service capabilities Â importance in Tables 12 and 13) were calculated using a weighted linear value. The importance 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 Â 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 concerned the undertaking of a comparative analysis between the UK and Taiwan form both a customer’s and provider’s perspective. 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 ﬁndings are consistent with a previous study (Yeung et al., 2006). The inﬂuences of service provision on 3PL operational performance (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 Taiwan’s 3PLs, aligning high levels of operational performance with quality (i.e., o4 and o5) and innovation (i.e., o13–o15) is a necessary strategy for the UK’s 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 offered by 3PLs cannot directly inﬂuence the 3PLs’ ﬁnancial performance. Through a better operational performance, 3PL providers 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 12 Results of mediator multiple regression analysis in Taiwan. Dependent variables Gross proﬁt margin considered Model 1 Independent variable Service capabilities Â importance (p) Operational performance (p) Model F (p) R
2

Sales growth considered Model 3 Gross proﬁt margin À0.013 (0.905) Model 1 Operational performance 0.563 (0.000) Model 2 Sales growth 0.218 (0.037) – 4.480 (0.037) 0.047 Model 3 Sales growth À0.072 (0.526) 0.514 (0.000) 13.154 (0.000) 0.228

Model 2 Gross proﬁt margin 0.324 (0.002)

Operational performance 0.563 (0.000)

– 41.718 (0.000) 0.317

– 10.089 (0.002) 0.105

0.598 (0.000) 22.837 (0.000) 0.350

– 41.718 (0.000) 0.317

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

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Table 13 Results of mediator multiple regression analysis in the UK. Dependent variables Gross proﬁt margin considered Model 1 Independent variable Service capabilities Â importance (p) Operational performance (p) Model F (p) R2 Operational performance 0.471 (0.000) – 31.338 (0.000) 0.222 Model 2 Gross proﬁt margin 0.203 (0.033) – 4.486 (0.033) 0.041 Model 3 Gross proﬁt margin 0.033 (0.743) 0.361 (0.001) 8.974 (0.000) 0.143 Sales growth considered Model 1 Operational performance 0.471 (0.000) – 31.338 (0.000) 0.222 Model 2 Sales growth 0.231 (0.014) – 6.210 (0.014) 0.053 Model 3 Sales growth 0.106 (0.302) 0.265 (0.011) 6.610 (0.002) 0.108

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

Table 14 Summary of results. Hypotheses Supported/not-support Taiwan H1 H2 H3 H4 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 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 3PL providers whose operational performance is high have better ﬁnancial performance compared to those with a lower operational performance For 3PLs, the relationship between service capabilities corresponding to customers’ key priorities and ﬁnancial performance is mediated by operational performance Not supported Partially supported Supported Supported UK Not supported Partially supported Supported Supported

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 performance for 3PLs but also investigates the impacts of the service capabilities of 3PL providers which correspond to customers’ 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 Taiwan’s 3PLs, aligning high levels of operational performance with quality and innovation 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 expect from 3PLs. Second, the results suggest that excellence in operations is more important than wide-ranging service provision. 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 capabilities 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 advantage 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

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effects on the 3PLs’ ﬁnancial performance. Second, future research could concern qualitative case studies to deeply understand 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, future 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 capabilities 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 company’s 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 company’s 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 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. 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 returns s14. 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. s28. s29. s30. s31. s32.

Invoicing/billing function Freight bill auditing/payment Billing the ﬁnal customer Insurance service Consulting services Management reports

A.3. Operational performance For the 3PLs’ questionnaire: Respondents were asked to provide a rating of the company’s 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 appropriate 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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Analysis of Third Party Logistics Performance

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