The desire by firms to pursue gains from the trade of specialized
production has contributed to the rise of specialized intermediate
markets in the supply chain (Holcomb and Hitt 2006). These intermediate
markets intensify the partitioning of production and shift the focus
from the final market for goods and services to the processes by which
value is created in intermediate markets (Jacobides 2005). Third-party
logistics (3PL) provides a good example of a rapidly emerging
intermediate market that is characterized by the increasing use of
outsourcing particularly as organizations have moved into foreign
markets and globalized their supply chains and sources of materials.
This trend has led to rapid growth in the provision of contract 3PL
services (Razzaque and Sheng 1998; Sanders, Locke, Moore and Autry
2007). Armstrong and Associates (2010) estimate that the global 3PL
market for Fortune 500 companies amounts to US$187 billion in revenues
in 2007, with three quarters of US Fortune 500 companies using 3PL
Although an important component of global economic activity and an
area of interest to supply chain management (SCM) scholars (Carter and
Ellram 2003; Lai, Li, Wang and Zhao 2008; Marasco 2008), it remains
difficult for 3PL providers to understand the expectations of their
customers and determine what drives their choice of one provider over
another (Power, Sharafali and Bhakoo 2007). We address this issue by
developing a more consistent understanding of the factors that are
important in the choice of a logistics service provider. In addition,
the approach we have used is applicable to other buyer-supplier
relationships where a key challenge is the better understanding of how
customers, with different needs, differentially value each component of
the service when choosing a provider.
An overarching premise of this research is that customers differ in
terms of their preferences for particular 3PL offerings and that the
customization of logistics service packages to different customer
segments can improve the perceived value of the service offering. This
logic implies two research questions that are the empirical focus for
1. From the set of service components that influence the choice of
a 3PL provider, which components matter most?
2. Are the preferences of different customers sufficiently distinct
to enable segmentation?
We will focus on a customer's choice between competing 3PL
service offerings, and by doing so we take an approach that is different
from, but complementary to, prior work that has sought to identify the
way logistics service capabilities can be leveraged to create value
within a supply chain. Other authors have looked at the level of
customer satisfaction within existing 3PL relationships (Bowersox, Closs
and Stank 1999; Stank, Keller and Gloss 2001; Knemeyer and Murphy 2005),
differentiated capabilities (Lai et al. 2008) and logistics service
quality (Mentzer, Flint and Hult 2001). Our approach provides for a more
direct examination of the factors that matter at the point of deciding
on a particular 3PL supplier.
The 3PL industry provides a particular challenge to understanding
the way customers value different service components. Not only are the
key service components (transport, warehousing, etc.) inherently
complex, involving physical movement of goods, IT system support and
contact with service personnel, but a 3PL provider must be able to
bundle a broad range of services for different customers with different
needs. To address this complexity more rigorously, we use discrete
choice stated preference modeling, which allows us to identify those
components of the 3PL service offering that managers consider important
in their choice among logistics service providers. This approach has
been shown in the service literature to be a very effective way to
understand what customers value in both business-to-business (B2B) and
consumer-to-business contexts (Goodale, Verma and Pullman 2003; Iqbal,
Verma and Baran 2003).
The next section positions this study within the buyer-supplier
literature and describes the relevance of the methodology. We then move
onto the heart of the paper and describe the aggregate results that
reveal what customers value most when selecting a 3PL provider. The
majority of customers considered reliable performance, price, customer
service recovery and being easy to deal with as most critical to their
choice of a 3PL provider. Next, three customer segments are identified
that reveal variation in customer value for different 3PL service
components. This behavior-based segmentation model provides 3PL managers
with a useful starting point from which they can build a more
customer-aligned service offering.
Buyer-supplier exchange relationships involve both (1) a choice of
the activities to outsource and (2) the selection of an appropriate
supplier to perform these activities. The conditions that determine the
boundary between the activities carried out within or outside the firm
has been widely discussed in the literature using a range of theoretical
lenses (Sarkis and Talluri 2002; Holcomb and Hitt 2006; Terpend, Tyler,
Krause and Handfield 2008; Wallenburg 2009).
Transaction cost economics (TCE) is at the core of nearly all
discussions of the "make or buy" decision and has generally
received strong empirical support across a range of different economic
situations (Walker and Weber 1987; Williamson 2008; Wallenburg 2009;
Kamann and Van Nieulande 2010). In the case of logistics outsourcing,
TCE argues that the buyer/customer will, once they have made the choice
to outsource logistics generally, choose that provider offering the
greatest efficiency in terms of "planning, adapting, and
monitoring" costs (Williamson 2008, p. 2). Additionally, TCE notes
that in transaction environments where performance is
unpredictable--such as that commonly found within the 3PL
industry--buyers will seek safeguards to minimize uncertainty in
outcomes (Williamson 2008). Therefore, according to TCE theory,
differences in the costs and risk prevention competencies among the
group of competing 3PL providers are likely to provide robust
determinants for why a buyer selects a particular 3PL provider.
However, an exclusive focus on TCE as an explanation of the 3PL
selection process offers us an incomplete picture of the complexity of
the decision being made. Supplier selection is also based on the
perceived value created by outsourcing and the inherent desire among
buyers to maximize the benefits that they derive from establishing
outside supplier relationships (Terpend et al. 2008). An alternative set
of theories drawn from the resource-based view (RBV) of the firm,
examines how firms seek to build embedded capabilities and knowledge for
addressing complex, practical and repeated problems (Madhok 2002; Mclvor
2005). In line with this thinking, there is strong empirical support for
the proposition that the decision to outsource is heavily influenced by
organizational capability considerations (Hoetker 2005; Jacobides and
Winter 2005) and the creation of new value (Terpend et al. 2008). From a
logistics service provider's perspective, this suggests that
contracts will be won by presenting to potential customers unique
capabilities and embedded knowledge that are not on offer by their
Recent work on the resource-advantage (R-A) theory of competition
suggests that the TCE and RBV focus on long-term equilibrium is too
broad to be an effective basis for strategic SCM research (Hunt and
Davis 2008). They argue that SCM scholars need to pay greater attention
to heterogeneity and the effective matching of specific supplier
capabilities with the needs of particular market segments in
environments in which information is imperfect and costly.
We return to these considerations of heterogeneity and segmentation
below. First we consider the service components or attributes of
logistics providers that matter most to a buyer.
3PLiterature--Importance of Different Attributes
Traditionally, 3PL providers have offered customers three primary
competitive benefits: reduced cost, faster delivery and improved
reliability (da Silveira 2005; Voss, Page, Keller and Ozment 2006).
However, recent work in SCM has suggested that a new paradigm is
emerging based on a more sophisticated supply chain (Melnyk, Davis,
Spekman and Sandor 2010). If new competitive pressures are emerging then
an important unanswered research question is: "to what extent has
the structure of demand in the 3PL customer base changed?" One
difficulty in seeking answers to this question is the very large number
of different attributes that have been suggested by different authors.
This reflects the richness of the bundle of services that a 3PL provider
offers as well as the usual difficulties of precisely defining the
nature of transactions and quality dimensions in a service environment.
To illustrate the point, Sarkis and Talluri (2002) list 31 potential
factors and Stank et al. (2001) utilize 38 items in their analysis.
In broad terms, we can distinguish between economic exchange
factors (that will potentially be wider than an initial price);
logistics performance (encompassing delivery speed, reliability, etc.);
technology (primarily IT-related capabilities); relational attributes
(e.g., understanding the customer and fit between cultures); flexibility
(being able to respond to changes in requirements); as well as a range
of other social exchange factors that do not fit easily into these
categories (such as reputation, ability to innovate and managerial
Different studies have provided mixed results on the relative
importance of economic and social exchange factors. For example, studies
have shown that customers prefer a cost focus and are reluctant to
remunerate 3PLs for outstanding service performance (van Laarhoven,
Berglund and Peters 2000). Voss et al. (2006) report that delivery
reliability is critical to carrier selection--ranking second in terms of
importance and first when it comes to intention to purchase. Delivery
speed and price are also considered to be order winners according to da
Yet, in a survey of 66 US 3PL firms, Stank, Goldsby, Vickery and
Savitskie (2003) indicate that performance quality is primarily an order
qualifier and not a differentiator in the eyes of the customer.
Likewise, Griffiths, James and Kempson (2000) state that attributes such
as operational performance quality, technology and price are frequently
taken for granted. Lai et al. (2008) propose that the level of
information technology capability significantly affects the competitive
advantage of a 3PL provider by reducing costs, supporting innovation and
service quality. If correct, this work has direct customer service
implications because it appears that customers of 3PL services are
increasingly recognizing that cost advantages and delivery performance,
while necessary, are not always sufficient in the modern business world
(Cahill 2006). Furthermore, Voss et al. (2006) demonstrate empirically
that the importance of operational and strategic attributes has changed
in recent years due to competitive pressures and constrained
According to these varied theoretical perspectives and empirical
findings, the selection of a 3PL provider requires economizing on both
transaction costs and the costs of developing capabilities and utilizing
idiosyncratic knowledge found among alternative suppliers. Wallenburg
(2009) has called for further research in SCM that is able to clearly
distinguish where customer value is derived. This call is at the heart
of the empirical work underpinning this paper and motivates our desire
to not only identify the relative importance of attributes (McGinnis and
Kohn 1990; Sarkis and Talluri 2002), but to unpack the specific levels
for each attribute and thereby, separate the order winners from the
order qualifiers (Hill 2000). (1) Moreover, it makes little sense to
weight the relative importance of delivery reliability in comparison
with, say, contract price, unless scholars can put levels on the
different attributes and specify precisely what is meant by less
reliable performance and how it makes a difference to 3PL supplier
selection. We therefore derive the following proposition:
Proposition 1: Buyers will trade off between a range of attributes
that both minimize transaction costs and create value, but the final
choice of 3PL provider will be determined by the specific levels of each
attribute rather than a simple weighting of attributes.
Heterogeneity and Segmentation
It is reasonable to expect that the value derived from any
combination of service attributes will differ considerably between
individual customers purchasing 3PL services. Yet the dominant
perspectives in the supply chain literature--TCE and RBV--provide no
mechanisms to look at the nature of customer demand. R-A theory (Hunt
and Davis 2008) has the benefit of highlighting the complex choices that
are required given heterogeneity in customer tastes and preferences, and
the distinct self-interest seeking behavior among decision makers.
Hence, it is inappropriate to aggregate demand data among all buyers of
a 3PL service offering, but rather demand is best viewed as collections
of market segments (Hunt and Davis 2008).
Studies have shown that variation in supply chain demand is
frequently unrelated to standard a priori factors, such as size or
customer industry type (Dibb and Wensley 2002). Coltman, Devinney and
Keating (2011) have extended this literature and proposed that the logic
of segmentation--based upon simple observable characteristics--may be
too simplistic as a representation of what customers are actually doing
and demanding. Hence, it is our argument that the mixed findings
reported in the segmentation literature suggest that the historic
emphasis on products (e.g., Fisher 1997) or transactions (Mentzer et al.
2001) as isolated segmentation criteria is insufficient. Erevelles and
Stevenson (2006) foresaw this when they stated that when B2B
segmentation research has proven to be suboptimal, it has focused on
relatively isolated buying situations rather than an a priori
understanding of customers' needs along several dimensions
Our thinking is in line with Gattorna (2006), who suggests that it
is possible to develop an appropriate supply chain segmentation strategy
by developing a more sophisticated understanding of the series of
"behavioral logics" that interact and are traded off in the
final selection decision by customers. The behavioral logic--that can be
measured empirically using utility theory--explains why a group of end
customers buy a product and from this point it is possible to develop an
appropriate supply chain strategy to meet the needs of the segment
concerned. Utility theory provides an appropriate lens to examine buyer
preferences directly and identify those tangible and intangible
attributes that are most important to market segments. It follows
logically then that customers will select the 3PL service provider that
offers maximum benefits, utility or value (i.e., utility maximization).
Our approach is to use an experiment to examine buyer decisions
(albeit looking at stated choices rather than actual choices) to advance
the literature and unpack exactly how service attribute levels should be
configured and segmented. This allows us to directly address the
capabilities, attributes and levels that are most likely to improve
positional advantage in the market. Based on this discussion, we derive
the following proposition:
Proposition 2: Different buyers have different preferences for 3PL
services, and these preferences are sufficiently distinct to enable
identification of segments that have implications for positional
advantage in the marketplace.
RESEARCH METHODS AND SAMPLING
An effective method for evaluating the level of demand for various
service characteristics offered by different 3PL providers is to model
preferences as a choice response to experimentally designed service
profiles. Discrete choice analysis (DCA) has been used to model the
choices of key decision makers in a variety of organizational areas
spanning marketing, operations management, transportation and economics.
In the B2B service context, Goodale et al. (2003) used DCA to develop an
improved understanding of service capacity scheduling while Iqbal et al.
(2003) showed that service development and exposure to information,
influences the features of transaction-based e-services. Buckley,
Devinney and Louviere (2007), in studying foreign direct investment
location choice, demonstrated the efficacy of DCA in understanding very
complex managerial decision making.
The theoretical model underpinning DCA draws on Thurstone's
(1927) original propositions in Random Utility Theory to provide a
well-tested and generalizable theory of behavioral science (McFadden
1974). It allows scholars to conceptualize choice as a process of
decision rules that can be statistically tested using the multinomial
logit (MNL) model (Louviere, Hensher and Swait 2000). When selecting any
product, service or combination of both, a decision maker will
consciously or unconsciously compare alternatives and make a choice that
involves trade-offs between the components of those alternatives. The
result of this process is a choice outcome that can be decomposed
conditional upon the options available within the experimental design
(Hensher and Puckett 2005).
Discrete choice experiments typically involve the following steps:
(1) identification of the key attributes; (2) specification of the
levels of the attribute; (3) creation of the experimental design; (4)
presentation of alternatives to respondents; and (5) estimation of the
choice model. Verma, Thompson and Louviere (1999) review the DCA
literature and provide guidelines for designing and conducting DCA
studies in the services context. Research has demonstrated that choice
predictions resulting from DCA-based experiments are, in general, very
accurate representations of reality (Louviere et al. 2000).
DCA applies experimental design techniques that allow us to discern
the marginal utility associated with an attribute and its levels without
having to consider every possible combination of alternatives available.
As the starting point we used a 4 (7) fractional factorial design to
construct our base design and then combined it with an endpoint design
to enable the estimation of some two-way interactions as well as all
main effects (Louviere et al. 2000). This approach utilizes the
principles of orthogonality and asymmetry to maximize the efficiency of
the parameter estimates while controlling for the desired number of
choice sets (see Street and Burgess 2007, for a more detailed
explanation). The final design was divided into 12 blocks of 16 choice
sets, with respondents completing one block of 16 choice sets each.
Every choice set required respondents to choose between two generic
logistics service profiles, an example of which is given in Appendix A,
in which the levels of seven different attributes were varied according
to the underlying experimental design. To avoid biases from order
effects, the sequence of the 16 choice sets and the allocation of
respondents to a particular block were randomized. An appendix that
describes the creation of the choice sets is presented in Appendix B.
Details on the Experimental Design
Drawing on random utility theory, we recall that the latent
preference for a given attribute is specified to have an observed and
unobserved component. To estimate the multinomial logit (MNL) model, we
assume that the unobserved component is uncorrelated across choices and
individuals. Accordingly, the latent preference or utility of respondent
n for option j is given by U = [beta]'[X.sub.nj] +
[[epsilon].sub.nj], where [X.sub.nj] is the vector of attributes of
option j and [beta] is the vector of parameters of preference weights
associated with each attribute. By assuming [[epsilon].sub.nl] to be
independently and identically distributed (IID) extreme value type I,
McFadden (1974) showed that the choice probability could be given by:
[P.sub.ni] = [[exp([beta]'[X.sub.ni])]/[[J.summation over (j =
Consistent with the assumptions of MNL, Street and Burgess (2007)
provide guidance for the construction of optimal experimental designs.
These designs, termed "D-optimal designs," enable researchers
to estimate [beta] more precisely by seeking to minimize generalized
variance. This is achieved by combining an endpoint design and its fold
over with an orthogonal main effects plan (Louviere et al. 2000). For
instance, in our study we started with a [4.sup.7] fractional factorial
design to create the profiles for the first option in each choice task
of the base design. We then constructed the second option in each choice
task by systematically varying the levels of the attributes so that as
many pairs of profiles as possible would have different levels for each
attribute. As our design needed to evaluate preference for seven
attributes with four levels, we used modular arithmetic to identify a
generator to create the profiles in the second option where the levels
in (k+1)/2 attributes must change. This base design was 100% efficient
and resulted in 96 choice tasks that we then divided into 12 blocks of
eight. Each respondent was presented with 16 choice tasks, eight from
the endpoint design (see Table B1) and eight from the base OMEP design
(see Street and Burgess 2007, for example). The endpoint design is a
subset of the full-factorial design where only the highest and lowest
levels of each attribute are included. This produced in a near-optimal
design where the C matrix is orthogonal, and all main effects and some
two-factor interactions can be estimated independently
3PService Attributes, Levels and Covariates
A substantial amount of empirical and conceptual work has examined
the relative importance of service and cost as determinants of both
shipper freight transportation choice (La Londe and Cooper 1989;
McGinnis 1990) and 3PL provider choice (Flint, Larsson and Gammelgaard
2005). As we have discussed above there are many attributes that may be
important in selecting a logistics provider. We began with a list that
has been produced by Coltman et al. (2011) where they identify the
relative importance of 21 factors that characterize core and peripheral
attributes underlying 3PL demand. Based on a reduced form of
utility-theoretic DCA, known as maximum difference scaling or best-worst
analysis, they pared these 21 factors down to those ten attributes most
relevant to the 3PL choice decision in the minds of the customer. These
10 attributes accounted for more than 75 percent of explained variation
and include: (1) reliable performance, (2) delivery speed, (3) customer
interaction, (4) track and trace, (5) service recovery, (6) supply chain
flexibility, (7) professionalism, (8) proactive innovation, (9) supply
chain capacity and (10) relationship orientation. Table 1 presents the
definitions for each attribute.
Reliable performance--consistent "on time" delivery
without loss or damage of shipment
Delivery speed--amount of time from pickup to delivery
Customer service--prompt and effective handling of customer
requests and questions
Track and trace--transparency and "up to the minute" data
about the location of shipments end to end
Customer service recovery--prompt and empathetic recovery and
resolution of errors or problems concerning customers
Supply chain flexibility--ability to meet unanticipated customer
needs, e.g., conduct special pickups, seasonal warehousing
Professionalism--employees exhibit sound knowledge of products and
services in the industry and display punctuality and courtesy in the way
they interact and present to the customer
Proactive innovation--this activity refers to the provision of
supply chain services aimed at providing new solutions for the customer
Supply chain capacity--the ability to cope with significant changes
in volumes, e.g., demand surges and deliver through multimodal transport
services including: international express and domestic, by air; ocean;
Relationship orientation--characterized by sharing of information
and trust in the exchange partner
The Coltman et al. (2011) study is unique in that they measure an
extensive array of attributes on a relative importance scale. The study
falls short, however, because they do not address the issue of
specifically how the levels of these attributes matter in a more
realistic decision-making context and how they interact with price. Our
aim is to achieve a more complete utility-based examination that better
explains individual behavior.
In our experiment each attribute comprises four levels and this
gives an opportunity to combine related attributes. For example,
attributes such as reactive customer service and proactive service
recovery are combined under the more general "customer service
recovery" attribute label. By presenting related attributes as
levels under a higher order attribute label, we were able to narrow the
final set of attributes in this study to seven. The nature of the
experiments makes it preferable to have limited numbers of different
attributes to ensure a shorter and less arduous task is completed by the
experimental subjects. For example, because all subjects are presented
with a series of choices, increasing the number of attributes considered
would tend to increase the number of choice sets that need to be
In order to refine the definitions of die attributes and to
identify representative levels for each attribute we also conducted an
extensive pretesting procedure that included several rounds of
qualitative work to ensure realism. This work included reviewing the
academic literature, industry reports and Web sites, along with insight
gained from semistructured interviews across the seven countries with a
total of 37 3PL customer firms. The interviews were used to ensure that
the definitions accurately reflect the conceptual domain of each
attribute and thereby, establish content and face validity. Appendix C
gives a complete description of the attribute definitions as well as the
The final selection of levels for each attribute is as follows.
Reliable performance, as a measure of delivery in full, on time, and
error free was divided into three percentage-point increments ranging
from a high of 98-100 percent to a low of 89-91 percent of the time.
Price levels were allocated as a percentage of the difference vis-a-vis
price parity, starting with a low price of 0-4 percent less than price
parity, defined as "what you currently pay" and ranging to a
price of 5-8 percent higher than price parity. The levels for customer
interaction pick up two different aspects of the service concept. The
first relates to the ease with which business is conducted with the
logistics service provider. The second relates to the effort that the
provider puts into building the relationship with their customer through
measures such as loyalty schemes. Capacity equates with being able to
meet unanticipated customer needs and the levels vary in a range between
excellent (industry leader) to below industry average. Service recovery
is defined in a more expansive way than for example, just finding
missing packages by distinguishing between proactive and reactive
service recovery efforts. The levels range from being very proactive (an
industry leader) to being slow to respond to problems and unlikely to
propose solutions. Innovation is defined as the provision of new
services and the options vary in a range between very innovative (an
industry leader) to poor innovation and unlikely to propose solutions.
Innovation offers substantial potential for service providers to
differentiate themselves from competitors. The emphasis on logistics
innovativeness as a source of customer value has recently been reported
by Flint et al. (2005) and Wagner (2008). Moreover, logistics
outsourcing has steadily gained a more relational focus that emphasizes
the benefits of long-term exchange over spot market transactions (Murphy
and Wood 2004). However, prior attempts by 3PL providers to improve
innovativeness and enhance customer relationships have faced many
challenges (Wallenburg 2009). Professionalism is concerned with the
knowledge of the service provider. It effectively combines two slightly
different areas of knowledge: that related to the logistics industry and
that related to the customer's business.
Besides the DCA task, the survey instrument also included various
background questions that were used to examine the impact of covariates
on the model. Firm size was measured based on the number of employees in
the company. A measure of 3PL importance was based on the following
question: "How important are transportation and logistics service
providers to your business? We are particularly interested in your
product and/or service cycle time and whether logistics is critical or
not. Provide a rating from 1 to 5, where 1 means not critical, and 5
means absolutely critical (make or break)." Finally, preferred
style of exchange relationship was based on the customer's
preference for a collaborative relationship between supplier and
customer vis-a-vis an exchange relationship that is focused on
efficiency and lowest cost to serve. The question required respondents
to "Please allocate a percentage between 0 and 100 to the
particular style of exchange relations your company prefers with
transportation and logistic service providers. There are four
relationship styles for you to choose from, interactions that are (1)
primarily collaborative relationships between supplier and customer,
based on trust, (2) focused on efficiency and lowest
"cost-to-serve," (3) capable of quick response to irregular
demands and flows, and (4) based on finding solutions to unpredictable
The indiscriminate pooling of data offers limited insight because
it can mask the importance of relationships between explanatory
attributes (Hatten, Schendel and Cooper 1978). In response, a variety of
latent class techniques have been developed and applied to generate more
accurate cluster or segment solutions (McLachlan and Basford 1988;
Bensmail, Celeux and Raftery 1993; Wedel and Kamakura 2000). These
models are particularly useful in estimating the likelihood that a
specific firm fits into a class of firms for which a particular model
applies. More specifically, by using latent class modeling we are able
to derive a maximum likelihood-based statistical model that accounts
simultaneously for both the similarity and differences between decision
makers based on their actual preference for different service
characteristics. The advantage of using this approach is well documented
and provides a more elegant interpretation of the cluster or segment
criterion that is less arbitrary and statistically more appropriate (see
Vermunt and Magidson 2002, for a general explanation).
Sampling and Data Collection
Invitations to participate in the study were sent via email to the
account representative with primary responsibility for 3PL contracts. A
sample of 998 Asia Pacific company contacts was obtained, all of who
were customers of large multinational 3PL providers. During the data
collection phase, each respondent received an e-mail from the research
team with an invitation to join the research project. Although no
explicit remuneration was provided for participation, each
respondent's details were entered into a drawing to win a plasma
television. After agreeing to participate, respondents were directed to
a Web page that provided information about the survey and definitions of
the attributes under investigation. Native language versions of the
survey were available in English, Chinese, Japanese and Korean.
Extensive rounds of forward and backward validation were carried out
using a commercial translation service (http://www.translationsabc.com/)
and native language experts in each country to ensure that the
translations were identical.
The respondents were then asked to complete a survey that included
16 experimentally generated choice sets. Three hundred and nine firms
completed the survey giving a final response rate of 31 percent once
undelivered emails were taken into account. Approximately one-third of
responding firms are from Australia and New Zealand, and another third
are from China, with the remaining firms located in Hong Kong, India,
Japan, South Korea and Singapore. The distribution by industry type is
skewed toward the largest users of 3 PL services such as manufacturing,
wholes ale/retail and transport/storage. The median firm size was
approximately 3,200 employees, with the smallest firm having 16
employees and the largest 400,000. The summary characteristics for all
the responding firms are shown in Table 2. One salient characteristic of
the data is that although the respondents are all customers of Company
X, they typically deal with more than one global 3PL provider (79
percent of firms use multiple 3PL providers). Thus, even though the
firms are common in that they all use Company X, their use of other 3PLs
reduces the extent that selection bias is a problem in the sample.
ANALYSIS AND RESULTS
The MNL model has well-defined statistical properties that can be
applied to pooled data or segment-based models. The approach used in
this study matches established conventions, closely mirroring that of
previous studies in operations management and marketing (Verma, Plaschka
and Louviere 2002; Iqbal et al. 2003). Our examination of the
choice-modeling responses is divided into two stages: (1) aggregate
level MNL results and (2) a latent class segmentation model.
The first objective of our study deals with the trade-offs
customers make between attributes. Table 3 shows the relative main
effects for each attribute with respect to all other attributes within
the model--in fact the table lists attributes in order of importance.
The main effect values were obtained using a two-step approach: (1) main
effects were calculated for each attribute by subtracting the utility
associated with the lowest level of the attribute from the utility
associated with the highest level; and (2) these values were normalized
such that the main effects from all of the attributes sum to 1. An
advantage of this analysis is that it allows for the comparison between
the relative importance of each attribute on a common scale (Verma,
Louviere and Burke 2006). In this case operational performance is nearly
10 times more important than professionalism when it comes to choosing a
The results also allow us to delve deeper into the customer value
proposition by understanding how customers strategically trade-off
between the various service features available when choosing a 3PL
provider. We provide more detailed commentary for each of the service
attributes in turn.
Reliable performance is the core competence for logistics service
providers and it is the single attribute that has the greatest influence
on choice. As the levels of reliability increase from a low of 89-91
percent to a high of 98-100 percent of the time, there is a steady
increase in the effect.
Price levels are important as a determinant in choice, and in this
study the results reveal a surprising lack of statistical significance
at the "0-4 percent more than now" level ([beta]=0.044, p=NS).
This indicates that there may be some customers that are not price
sensitive, providing price increases are not too great. It is
interesting that the value of [beta] for the case of prices being 0-4
percent less than now is smaller than the value of [beta] at prices
equivalent to now. This suggests that for some customers lower prices
are not an incentive, and may even be a disincentive. For example this
might occur if a customer felt that a big drop in price signaled some
potential problem in an area that was not captured by the specific
attribute levels of the survey.
The results for customer interaction indicate a positive and
statistically significant relationship between the choice of logistics
provider and being "easy to deal with" independent of whether
rewards are provided ([beta]=0.177, p < 0.001 and ([beta]=0,147, p
< 0.001). Interestingly, the strongest effect was observed when
providers were "difficult to deal with" and used rewards
([beta] =-0.198, p < 0.001). This suggests that customers will not
choose providers who try to buy the loyalty of their customers through
rewards programs without investing sufficiently in the relational
aspects of service delivery.
A review of the 3PL market indicates that the industry has
generally adopted a reactive approach to service recovery--a situation
where it is the customer's responsibility to contact the 3PL if
they have concerns about delivery. Online track and trace capabilities
are examples of sophisticated ways to automate this process.
Alternatively, providers can be proactive and take responsibility for
notifying the customer of likely delays--for example, through mechanisms
that identify parcels that are late and proactively contact customers to
advise them of the reason for the delay. The general picture here is one
where being "the industry leader" ([beta] = 0.169, p <
0.001) or "better than the industry average" ([beta] = 0.130,
p < 0.01) is important at the aggregate level.
Capacity equates with being able to meet unanticipated customer
needs. The results show a clear preference for a provider that is the
industry leader ([beta] = 0.082, p < 0.05) and a very strong dislike
for providers that are below the industry average ([beta]=-0.135, p <
0.001). The large negative values of [beta] when providers fail to meet
industry average performance, and the relatively modest gains from good
performance, suggest that for some customers a reasonable ability to
meet unanticipated customer demands is an order qualifier, rather than
an order winner. For such customers this capability is required to get a
"seat at the table" but is of less value in winning work.
Innovation is defined as the provision of new services and is
generally considered to be very important across all product and service
categories. Being the "industry leader" is important ([beta] =
0.081, p < 0.05), with "poor innovation" counting against a
provider ([beta]=-0.191, p < 0.001). The pattern of behavior here
mirrors that for capacity and suggests that customers may regard a
reasonable level of innovation ability as an order qualifier, rather
than an order winner.
Professionalism is concerned with the knowledge of the service
provider. It effectively combines two slightly different areas of
knowledge--that related to the logistics industry and that related to
the customer's business. The results indicate that this is not, in
general, an important characteristic although there is a preference, as
one would expect, for providers with deep industry and customer business
knowledge ([beta] = 0.057, p < 0.05).
Latent Class Segmentation
To account for heterogeneity in the data, a latent class
segmentation analysis was conducted using a three-step procedure to
select the best solution. This involved: (1) finding the model with the
best information criterion based fit; (2) using classification
statistics for the preferred model to ensure that the model had an
acceptably low ratio of classification errors; and (3) plotting the
estimates for each segment in the preferred model against one another to
ensure that the segment solution was not an artifact of scale factor
First, an examination of the fit statistics revealed that the
three-segment model had the best fit in terms of information criteria
scores such as the Bayesian information criterion and the consistent
Akaike information criterion. Second, an examination of the
classification statistics indicates that the three-segment model is
preferred to both two- and four-segment alternatives and has an
acceptably low ratio of classification errors. Lastly, the estimates for
each segment in the preferred model were plotted against one another to
confirm that the three-segment solution represented actual differences
rather than systematic variance. The various fit criteria and
classification statistics are shown in Table 4 for models with between 1
and 4 segments and can be interpreted based on lower values being
associated with better model fit.
The segment scores for each attribute and level are shown in Table
5. The Wald statistics reported in this table reveal whether the [beta]
parameters vary within each attribute and across the segments. A
non-significant p-value associated with this Wald statistic (e.g.,
professionalism) means that the indicator does not discriminate between
the clusters in a statistically significant way. The p-values associated
with the beta parameters provide a deeper understanding of how the
preference structures differ between the segment models. One of the most
interesting aspects of these models is that they show how the segments
differ not only in terms of what matters to respondents but also in
terms of what they do not consider to be important. Interpretation of
this data requires deeper discussion, and we provide a detailed
segment-by-segment commentary below.
Segment 1 is the largest group, comprising 62 percent of responding
firms. The segment is most concerned with reliable performance, customer
interaction and customer service recovery. An interesting characteristic
of this segment is that the respondents do not reward extremely high
performance (98-100 percent of the time) or penalize poor performance
(89-91 percent of the time) as heavily as segments 2 or 3. Further, the
firms in Segment 1 are not highly price sensitive. For customers in this
segment price is an order qualifier rather than an order winner. Instead
there is a clear preference for firms that are easy to do business with
and promote exchange relationships based on interaction and high levels
of supply chain service recovery and innovation. These are firms who
also regard industry average levels of capacity and innovation as order
qualifiers for a 3PL provider.
An examination of the covariate or descriptive data analyses
reported in the table indicates that the number of employees (a
frequently used measure for firm size) has a positive and statistically
significant relationship to membership in this segment ([beta]=0.353, p
< 0.001). Segment 1 comprises mainly large companies with 50 percent
of firms in this segment employing more than 200 employees, and 34
percent are of medium size (20-200 employees). 3PL operations are
considered to be critical to the buying firm's business
([beta]=1.158, p < 0.01), and the style of exchange relationship with
a 3PL provider is not based on efficiency or low cost to serve
strategies ([beta]=-0.012, p < 0.001). The covariate analysis
indicates that Chinese firms dominate this segment ([beta]=0.463,
In summary, the buyer behavior of customers in Segment 1 goes
beyond a purely transactional relationship. Comparisons with the other
segments and the observations of the covariates suggest that the key
differentiator for customers in this segment is a strategic exchange
relationship that allows them to manage the risk in the business
exchange and generate innovative solutions that better meet their
business critical transportation requirements.
Segment 2 is the second largest group with 27 percent of responding
firms. This group comprises 49 percent large firms and 41 percent medium
size firms. The respondents were primarily concerned with reliable
performance where extremely high performance is highly regarded
([beta]=1.877, p < 0.001) and low performance is penalized heavily
([beta]=-2.027, p < 0.001). The relationship to price is nearly
linear and this segment looks favorably on customer interaction. Being
the industry leader in terms of service recovery and capacity is
important to these firms; however, they do not value innovation or
The covariate analysis indicates that firms in Segment 2 do not
believe 3PL services are critical to their business ([beta]=-0.099,
p=NS). Company size ([beta]=0.224, p=NS) and efficiency ([beta]=-0.004,
p=NS) were also not observed to be to be important to the membership of
this segment. Australian firms dominate this segment ([beta]=0.383,
p=0.001). The combination of characteristics of firms in this
segment--an emphasis on being the best in a number of areas and on
reliability but not innovation--coupled with some indications that 3PL
services are not critical strategically, suggests that these firms are
looking for providers with proven solutions and low risk. We suggest
that this might lead to a small consideration set that includes the
relatively high profile 3PL providers (i.e., DHL, FedEx or UPS).
Segment 3 is the smallest group comprising 12 percent of the
sample. These firms are concerned primarily with current price, where a
price that is lower than what they currently pay is highly regarded
([beta]=1.536, p < 0.001) and a higher price is penalized heavily
([beta]=-2.295, p < 0.001). Although delivery performance is
important, with very high performance in particular being valued, this
is less important than for Segment 2. For firms in this segment, a 3PL
provider should be the industry leader in terms of service recovery
([beta]=0.669, p < 0.01) and place emphasis on customer interaction
([beta]=0.578, p < 0.05).
The covariate analyses indicate that Segment 3 comprises 34 percent
small companies (< 20 employees), 28 percent medium sized firms and
37 percent large firms. The size of the firm has a negative and
statistically significant relationship to 3PL choice ([beta]=-0.576, p
< 0.001), the 3PL operations are not considered to be critical to the
buyer's business ([beta]=-1.059, p=NS) and the preferred style of
exchange relationship with a 3PL provider is one based on efficiency or
low cost to serve strategies ([beta]=0.016, p < 0.01). In summary,
the buyer behavior in this segment places heavy emphasis on price and is
likely to reward the lowest priced 3PL provider with their business.
The relative main effects for each segment are also shown in Figure
1. This figure highlights in a simple visual way the variation between
segments based on the order of magnitude differences for each attribute.
Segment 1 is highest on the broader value-based attributes such as
customer interaction, customer service recovery and supply chain
innovation. Segment 2 is driven most noticeably by reliable performance
with the score more than twice as high as the nearest alternative group.
Segment 3 is clearly dominated by price.
[FIGURE 1 OMITTED]
DISCUSSION AND MANAGERIAL IMPLICATIONS
We began this paper with two questions pertaining to the value that
customers place on different service attributes and the way these
valuations differ between customer segments. In addressing these
questions, our study not only makes a contribution to SCM theory
building but also provides normative implications for how 3PL businesses
should compete. First, the results clearly indicate That the majority of
managers base their decisions on four key factors: (a) reliable delivery
performance; (b) price parity with other providers; (c) being among the
industry leaders in customer recovery; and (d) not being difficult to
deal with. These are the most critical issues for customers, with these
attributes explaining 79 percent of the variance in the decisions.
Striking the Right Balance in Service Design
Although this is the picture in aggregate, managers will also be
interested in a more detailed analysis that identifies the groups that
are most worth pursuing. We have shown that support for reliable
performance is consistent across all three segments, eclipsing the
relative importance of all of the other features. The firms in Segment 1
will be attractive to those 3PL providers with sufficient resources to
invest in service systems that transfer and share knowledge and
resources. True to the spirit of service-dominant logic in marketing
(Vargo and Lusch 2004), the most important attributes for this segment
place emphasis on the primacy of operant attributes such as being easy
to deal with and innovative. These attributes require the application of
human skills and collaborative relationships to coproduce value for the
Furthermore, our results indicate that in order to strike the right
balance, 3PL managers must appreciate that not all firms have the time,
energy or motivation to form the type of coproductive relationships that
service-dominant logic implies. Although the firms in Segment 2 are
still concerned with the overall service process they are less concerned
with hands-on coproduction and are attracted to 3PL providers with
strong brands and proven solutions. On the other hand, the firms in
Segment 3 are driven by the exchange of goods and will be attracted to
3PL providers that are willing to compete on price alone.
It is also important to note, that across all three segments, the
greatest impact on choice is seen when an attribute scores negatively.
This reveals that poor performance on key service areas will result in a
significant negative impact on the likelihood of being chosen as a
Deviations from Previous Work
Our results provide important deviations from previous work. A
considerable body of empirical work on 3PL performance has investigated
the level of satisfaction with the current logistics provider. In part,
this is because the overall satisfaction with the services offered is
thought to be an antecedent to increased market share and profitability
(Anderson, Fornell and Lehman 1994). For example, Stank et al. (2003)
show that a good "relational performance" by a 3PL (measured
by knowing a customer's needs, cooperating with the customer and
making recommendations for improvement) has beneficial effects on
customer satisfaction measures.
These studies might lead to an expectation that relational factors
would play a larger role than they appear to at the point of customer
choice. One part of the explanation is that the choice environment is
different to the environment in which an existing 3PL customer makes a
statement of satisfaction with their provider. We may expect that
satisfaction measures will be closely correlated with the choices that
people make, but the connection between the two is not direct.
(A) If a factor that influences satisfaction is hidden or implicit
then the buying firm may not be aware of its importance and hence
discount this factor when choosing a supplier.
(B) If a factor is regarded as subject to fluctuations or is
inherently unpredictable then it may play a lesser role in supplier
selection, even though it is critical to buyer satisfaction. For
example, even if I view a high degree of communication as important in
making me more satisfied with the relationship, I may feel that this is
hard to predict at the point of deciding on a supplier, or I may judge
that the quality of communication will depend on an account manager who
is likely to change during the life of the contract. In either case this
characteristic may become less important at the point where I make a
choke between logistics service providers.
(C) A characteristic may rise to prominence as a result of the
procedures used to select a supplier, but be pushed into the background
when satisfaction with the supplier is considered. For example, price is
likely to be a significant factor in the choice of a supplier, but may
be much less salient in assessing satisfaction with an existing
The Threat of Commoditization
A concern among 3PL managers that was identified during our
qualitative research and has been reported in prior studies relates to
the extent to which the services that a 3PL provides are regarded as
unique to the provider. Another way to express this is to ask whether
customers regard 3PL services as a "commodity." A commodity is
considered to be a nondifferentiated offering that holds few if any
intangible components and is sold primarily on the basis of price (Coase
1937). The core components of a commodity are well known, mostly stable
and widely shared among competing firms.
As the 3PL segment has become well established, it is natural to
ask whether there has been a shift toward commoditization, with the
negative consequences that this would bring for logistics providers. One
way of answering this question is to look carefully at how customers
view the service provision, especially as it applies to the importance
of price and basic quality measures when choosing between potential 3PL
It is clear from our analysis that the firms in Segment 3 have
requirements that are relatively uncomplicated, that they feel confident
that more than one provider can safely meet their requirements, and they
tend to consider the 3PL market as a commodity. Hence one would expect
that they would choose a supplier mainly on the basis of price, provided
that the basic delivery requirements are effectively carried out and
other characteristics meet a minimum level of competence. However, the
other two segments do not appear to view the 3PL market as a commodity.
Because Segment 3 is the smallest segment in our sample, representing
only 12 percent of the overall sample, we can conclude that
commoditization in the 3PL industry does not appear to be a major threat
at this point in time.
CONCLUSIONS, LIMITATIONS AND FURTHER RESEARCH
A major challenge for the 3PL service industries has been to
determine the value that customers place on their different service
offerings so that they can then focus on delivering the right service to
the right customer segment. Our examination of the preferences of 309
3PL customers has identified the attributes and attribute levels that
matter most to 3PL customers and shown that the heterogeneity of these
preferences can be characterized by three segments. With the exception
of reliable performance, each segment is driven by a different set of
order qualifiers and order winners. One implication is that 3PL managers
should monitor the segment profiles of their customers to avoid
misalignment between these segments and their service offerings. The
logic of segmentation suggests management strategies that involve a 3PL,
or a team within a 3PL focusing on a particular customer group.
Although this study is one of the few to directly examine the
choice preferences for a 3PL provider, it has limitations. First, the
nature of the experiments carried out made it necessary to limit the
factors considered (to seven attributes each with four levels) given the
size of the sample and the time required to carefully consider a whole
set of different choice scenarios. As a result not all possible
attributes have been included, and we were not able to directly include
either measures of trust (including ethical standards, integrity) or
communication (keeps us informed, communicates expectations, seeks
advice, etc.). Second, it could be argued that the geographic location
of customers in the Asia Pacific region may have some bearing on the
results, so that the findings might not be applicable to the service
operations requirements in European and American markets. It is notable,
however, that the aggregate MNL model did not identify any significant
differences in customer choice behavior across the seven countries
examined in this study. Even though the numbers of respondents from some
countries are small, there are sufficient numbers from Australia (112)
and China (107) to make this noteworthy However, country level effects
were identified in the segment level covariate analyses. For example,
Segment 1 is clearly dominated by Chinese firms and Segment 2 is
dominated by Australian firms. A possible explanation for the
contradictory country level effect is that directional differences among
the segment level coefficients cancel out statistical significances at
the aggregate level. Most importantly, these results imply that there is
far less homogeneity at the country level than is normally assumed in
the literature. Although this insight provides exciting opportunities
for future research, it is important to exercise caution given the
sample size limitations in this study. Third, while we have unique data
that identifies the most important person within each buying firm, it is
often the case that these individuals are influenced by various parts of
the organization, including finance, accounting, purchasing, information
technology management and senior management. Future research could
investigate the role of the "buying center" on the 3PL
supplier selection process.
Despite these limitations we believe that the study has made a
unique contribution in using a stated choice experiment to demonstrate a
set of latent classes in a B2B buying relationship in the logistics
industry. Further research may be able to use similar techniques to
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This research has been supported by a grant from the Australian
Research Council (ARC), the Centre for Business Service Science and the
Institute for Innovation in Business and Social Research at the
University of Wollongong. The views expressed here are solely those of
the authors who are listed in alphabetical order. The authors wish to
thank Dr. John Gattoma and Stuart Whiting for their contributions to
discussions and the direction of the research project.
(1) The terms "order qualifiers" and "order
winners" refer to the operational capabilities (or attributes as
used in this paper) that lead to competitive advantage. Order qualifiers
are attributes where a performance on par with the competition, or at
some minimum level, is necessary in order to be in the consideration set
of a buyer. Order winners are attributes where being better than the
competition significantly increases the chance of being selected.
Edward J. Anderson (Ph.D., University of Cambridge) is Professor of
Decision Sciences, and Chair of the Operations Management and
Econometrics Discipline, in the Faculty of Economics and Business at
Sydney University in Sydney, New South Wales (Australia). His research
interests include game theory and optimization; his current research
focuses on the way in which companies should bid for work; this extends
his previous work on generator bidding in electricity markets into a
more general supply chain context. Dr. Anderson's research has been
published in a variety of journals, including the European Journal of
Operations Research, the International Journal of Industrial
Organization, Operations Research, Management Science, and Mathematical
Models of Operations Research.
Tim Coltman (Ph.D., Australian Graduate School of Management) is
Associate Professor and Deputy Director of the Institute for Innovation
in Business and Social Research at the University of Wollongong in
Wollongong, New South Wales (Australia). His research interests
frequently involve work with outside companies, including e-business and
customer relationships management projects with the SAS Institute, SAP,
Fairfax Business Research and Kimberly Clark, among others. Dr.
Coltman's articles have been published in many journals, including
the California Management Review, the Journal of Business Research, the
Journal of Information Technology, and the Journal of Business
Timothy M. Devinney (Ph.D., University of Chicago) is a Professor
of Strategy in the School of Marketing at the University of Technology
in Sydney, New South Wales (Australia). He has extensive international
experience, and his research interests include international business,
values, corporate social responsibility and social consumerism,
corporate strategy, choice modeling and experimentation, and
technology/knowledge management. Dr. Devinney coauthored The Myth of the
Ethical Consumer, and has published articles in a host of outlets,
including most recently the International Business Review, the
Australasian Marketing Journal, the British Journal of Management, the
Journal of Consumer Behavior and the European Management Journal.
Byron Keating (Ph.D., University of Newcastle) is associate
professor of Service Management in the Discipline of Tourism and
Services, Faculty of Business and Government at the University of
Canberra in Canberra, Australian Capital Territory (Australia). His
current research interests are in the area of service technologies,
service consumption, service operations and service innovation. Dr.
Keating's work has been published or is forthcoming in premier
academic journals, including the Journal of the Academy of Marketing
Science, Electronic Markets, the Proceedings of the IEEE, Supply Chain
Management and the Journal of Business Logistics.
Example of a Stated Choice Task of Buyer Preferences in the Supply
Option One Option Two
Professionalism Deep knowledge Deep knowledge of
of both logistics and
logistics and acceptable
customer's knowledge of
Price Similar to 4% lower than
current price current price
DIFOTEF 98-100% of the 95-97% of the time
Customer service Better than Provider is very
recovery average: proactive and
responds to industry leader and
problems quickly where appropriate
and is able to detects problems:
propose an industry leader
Supply chain Excellent: Better than
capacity industry leader industry average
Relationship Easy to deal Easy to deal with,
orientation with, but rarely and frequently
rewards the rewards the
Supply chain Able to provide Unlikely to provide
innovation innovative innovative supply
supply chain chain solutions
Suppose options one * *
and two were the only
option would you be
most likely to
And if you could * * Neither *
choose one of these,
or seek other
would you choose ...
Endpoint Design for DCA
Row Option 1
Att1 Att2 Att3 Att4 Att5 Att6 Att7
E1 0 3 0 3 0 3 0
E2 0 3 3 3 3 0 3
E3 0 0 0 0 0 0 3
E4 0 0 3 0 3 3 0
E5 3 3 0 0 3 3 3
E6 3 3 3 0 0 0 0
E7 3 0 0 3 3 0 0
E8 3 0 3 3 0 3 3
Row Option 2
Att1 Att2 Att3 Att4 Att5 Att6 Att7
E1 3 0 3 0 3 0 3
E2 3 0 0 0 0 3 0
E3 3 3 3 3 3 3 0
E4 3 3 0 3 0 0 3
E5 0 0 3 3 0 0 0
E6 0 0 0 3 3 3 3
E7 0 3 3 0 0 3 3
E8 0 3 0 0 3 0 0
Attribute Definitions and Levels
Attribute Definitions Levels
Reliable performance Lower than what you currently pay
(DIFOTEF)--delivery in full, on (0-4% less); Similar to what you
time and error free. Complete currently pay; Higher than what
delivery of product (or service) at you currently pay (0-4% more);
the specified time agreed with the Significantly higher than what you
customer, and correspondingly pay (5-8% more)
Price--is what the customer pays 98-100% of the time; 95-97% of the
for the service and/or product time; 92-94% of the time; 89-91%
provided by the logistics service of the time
Supply chain capacity--the Excellent: industry leader; Better
capability to meet unanticipated than industry average; Equal to
customer needs. Includes conducting industry average; Below industry
special pickups, seasonal average
Customer service recovery--activity Very proactive: an industry
aimed at identifying and resolving leader; Better than industry
unexpected service delivery average response; Equal to
problems. The supplier response can industry average response; Slow
vary from being very proactive to respond to problems and
towards the detection of problems unlikely to propose solutions
and recovery; to very reactive.
Customer interaction--relates to Easy to deal with, and frequently
the customer's perception of the rewards the customer; Easy to
ease with which business is deal with, but rarely rewards the
conducted with the logistics customer; Difficult to deal with,
provider and the extent to which and frequently rewards the
they desire to reward and build customer; Difficult to deal with,
mutual trust with their customers. but rarely rewards the customer
Supply chain innovation--this Very innovative: an industry
activity refers to the provision of leader; Better than industry
supply chain services aimed at average innovation ability; Equal
providing new solutions for the to industry average innovation
customer. ability; Poor innovation and
unlikely to propose solutions
Professionalism--relates to the Deep knowledge of both logistics
logistics service provider's and customer's business; Deep
knowledge of the logistics industry knowledge of logistics and
AND the customer's business. For acceptable knowledge of customer's
example, logistics industry level business; Acceptable knowledge
professionalism would include logistics and deep knowledge of
knowledge of how to handle customs, customer's business; Acceptable
transportation, warehousing and any knowledge of both logistics and
other required logistics customer's business
Sample Descriptive Characteristics
Percent of Sample
Agriculture, forestry, fishing 0.01
Education, health and community services 0.07
Finance, insurance, property and business 0.14
Government administration and defense 0.01
Transport and storage 0.17
Wholesale and retail trade 0.27
Small (< 20) 0.16
Medium (20-200) 0.35
Large (> 200) 0.48
Country of origin
Australia/New Zealand 0.28
Hong Kong 0.07
Aggregate MNL Model
Beta Relative Main
98-100% of the time 0.452 *** 0.324
95-97% of the time 0.331 ***
92-94% of the time -0.319 ***
89-91% of the time -0.465 ***
0-4% less than now 0.154 *** 0.176
Equivalent to now 0.193 ***
0-4% more to now -0.044
5-8% more to now -0.304 ***
Easy to deal with, frequently rewards 0.177 *** 0.132
Easy to deal with, rarely rewards 0.147 ***
Difficult to deal with, frequently -0.198 ***
Difficult to deal with, rarely rewards -0.126 ***
Customer service recovery
Very proactive: an industry leader 0.169 *** 0.160
Better than industry average response 0.130 **
Equal to industry average response -0.017
Slow and unlikely to propose solutions -0.282 ***
Supply chain capacity
Excellent: industry leader 0.082 * 0.076
Better than industry average 0.066
Equal to industry average -0.013
Below industry average -0.135 ***
Supply chain innovation
Very innovative: an industry leader 0.081 * 0.096
Better than industry average 0.066
Equal to industry average 0.044
Poor innovation, no solutions -0.191 ***
Deep logistics and customer knowledge 0.057 * 0.037
Deep logistics, acceptable customer -0.003
Acceptable logistics, deep customer -0.047
Acceptable logistics and customer -0.007
* p < 0.05, ** p < 0.01, *** p < 0.001.
Model Fit and Parsimony for Models with Different Numbers of Segments
Number of Segments
1 2 3 4
Log likelihood -2937.6 -2772.2 -2697.2 -2639.4
AIC 5917.3 5630.4 5524.4 5452.8
BIC 5995.7 5790.9 5767.1 5777.61
CAIC 6016.7 5833.9 5832.1 5864.6
Npar 21.0 43.0 65.0 87.0
Class error 0.000 0.033 0.060 0.101
[R(0).sup.2] 0.187 0.291 0.348 0.397
Note: Bold items indicates best fit (i.e., minimum score).
Latent Class Model with Covariates
Segment 1 Segment 2
98-100% of the time 0.199 *** 1.877 ***
95-97% of the time 0.226 *** 0.989 ***
92-94% of the time -0.181 ** -0.840 ***
89-91% of the time -0.243 *** -2.027 ***
0-4% less than now 0.049 0.406 *
Equivalent to now 0.103 0.310 *
0-4% more to now 0.005 0.057
5-8% more to now -0.156 *** -0.772 ***
Easy to deal with, frequently rewards 0.164 *** 0.391 **
Easy to deal with, rarely rewards 0.153 ** 0.271 *
Difficult to deal with, frequently rewards -0.213 *** -0.249
Difficult to deal with, rarely rewards -0.104 * -0.413 **
Customer service recovery
Excellent: industry leader 0.153 *** 0.510 **
Better than industry average 0.139 * 0.061
Equal to industry average 0.042 -0.028
Slow to respond -0.334 *** -0.543 **
Supply chain capacity
Excellent: industry leader 0.062 0.332 *
Better than industry average 0.052 0.129
Equal to industry average 0.056 -0.208
Below industry average -0.169 *** -0.253
Supply chain innovation
Very innovative: an industry leader 0.105 * 0.235
Better than industry average 0.073 -0.011
Equal to industry average 0.058 0.015
Poor innovation, no solutions -0.237 *** -0.239
Deep logistics and customer knowledge 0.079 -0.058
Deep logistics, acceptable customer -0.046 0.057
Acceptable logistics, deep customer -0.026 0.017
Acceptable logistics and customer -0.008 -0.010
Company size 0.353 *** 0.224
Importance of 3PL 1.158 ** -0.099
Efficiency/low-cost-to-serve (231) -0.012 *** -0.004
Australia -0.209 0.383 ***
China 0.463 *** -0.009
Segment size 0.616 0.267
R[(0).sup.2] 0.114 0.688
Segment 3 Wald
98-100% of the time 0.872 *** 146.335 ***
95-97% of the time 0.183
92-94% of the time -0.531 *
89-91% of the time -0.525 *
0-4% less than now 1.536 *** 55.714 ***
Equivalent to now 0.981 ***
0-4% more to now -0.221
5-8% more to now -2.295 ***
Easy to deal with, frequently rewards 0.578 * 76.749 ***
Easy to deal with, rarely rewards 0.482
Difficult to deal with, frequently rewards -0.344
Difficult to deal with, rarely rewards -0.715 **
Customer service recovery
Excellent: industry leader 0.669 ** 137.894 ***
Better than industry average 0.455
Equal to industry average -0.319
Slow to respond -0.804 **
Supply chain capacity
Excellent: industry leader 0.622 41.113 ***
Better than industry average 0.291
Equal to industry average -0.312
Below industry average -0.601
Supply chain innovation
Very innovative: an industry leader -0.155 65.834 ***
Better than industry average 0.269
Equal to industry average 0.182
Poor innovation, no solutions -0.296
Deep logistics and customer knowledge 0.336 10.574
Deep logistics, acceptable customer 0.395
Acceptable logistics, deep customer -0.511 *
Acceptable logistics and customer -0.221
Company size -0.576 *** 13.308 **
Importance of 3PL -1.059 6.552 *
Efficiency/low-cost-to-serve (231) 0.016 ** 11.156 **
Australia -0.174 15.448 ***
China -0.472 ** 14.087 ***
Segment size 0.117
* p < 0.05, ** p < 0.01, *** p < 0.001.