Accepted Manuscript
Title: Modeling the operational capabilities for customized
and commoditized services
Author: Tim Coltman Timothy M. Devinney
PII:
DOI:
Reference:
S0272-6963(13)00080-6
http://dx.doi.org/doi:10.1016/j.jom.2013.09.002
OPEMAN 838
To appear in:
OPEMAN
Received date:
Revised date:
Accepted date:
18-1-2013
13-9-2013
16-9-2013
Please cite this article as: Coltman, T., Devinney, T.M., Modeling the operational
capabilities for customized and commoditized services, Journal of Operations
Management (2013), http://dx.doi.org/10.1016/j.jom.2013.09.002
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Institute for Innovation in Business and Social Research, University of Wollongong,
Australia
Leeds University Business School, University of Leeds, UK
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Tim Coltmana*, Timothy M. Devinneyb
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Modeling the operational capabilities for customized and
commoditized services
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*Corresponding author:
Tim Coltman, School of Management and Marketing, University of Wollongong, Northfields
Ave, Wollongong, NSW 2522, Australia.
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Phone: +61 2 42 21 3912.
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Fax: +61 2 42 21 4170.
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Email: [email protected]
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Modeling the operational capabilities
for customized and commoditized services
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Abstract
According to the extant service operations management literature, substantial gains can be
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achieved for providers that are adept at aligning internal operational capabilities with
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customer needs. However, the most influential models in the field attempt to explain this
alignment without regard to the core resource allocation choices relating operational
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capabilities to different service offerings. To further our understanding of service operations
alignment, we apply a unique combination of experimental scenarios and discrete choice
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modeling to measure the role of managers in orchestrating operational capabilities. Using the
third-party logistics tender review and bid preparation process as an empirical setting, we
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reveal the resource allocation choices that managers make between six distinctive operational
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capabilities (customer engagement, cross-functional coordination, creative solutions,
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operations improvement, IT infrastructure and professional delivery) and show the subtle
ways in which these capabilities interact as the service context moves from one based on
commoditization to one based on customization.
Keywords: service operations, strategic alignment, discrete choice analysis, capabilities.
Modeling the decision to align capabilities with customer needs, page 1 of 2
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1 Introduction
In order to compete successfully, organizations need to align their strategies and capabilities
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with their customer needs. This primary concept underpins the service operations
management literature that emphasizes the need to bundle goods and services in ways that
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reflect the relative importance of each component to the customer (Normann, 2000; Roth &
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Menor, 2003). Among the more important decisions made by managers are choices about
how best to allocate operational capabilities that align with customer needs. Alignment in
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service design is conceptually well grounded, and recognizes the distinction between value
creation and capture (Lepack et al., 2007) and the central role of the customer in service
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design (Vargo & Lusch, 2004; Sampson, 2012). Yet the operations management literature has
long lamented the lack of empirically based work to guide managerial actions (Goldstein et
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al., 2002; Machuca et al., 2007; Ponsignon et al., 2011).
According to resource-based theory (RBT), firms are likely to gain a relative advantage vis-à-
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vis their competitors when that firm’s managers can estimate the future value of an
operational capability better than their competitors (Kraaijenbrink et al., 2010).1 However, the
role that managers play in orchestrating internal resources and operational capabilities
remains one of the most underdeveloped aspects of resource-based logic (Sirmon et al., 2011).
This gap is visible in operations management, where the focus is commonly on “generic
outcome characteristics” (such as cost, quality, delivery and flexibility) at the expense of how
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Resources and operational capabilities are used interchangeably in this paper, reflecting the tendency towards inclusive definitions in
RBT.
Modeling the decision to align capabilities with customer needs, page 2 of 3
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managers configure operational capabilities to create and capture value (Schroeder et al.,
2002). The resource allocation decision ─ which includes operational capabilities ─ is
important because “many of the resources and capabilities upon which competitive
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advantages are formed have their basis in the operations area” (Coates & McDermott, 2002,
p. 437).
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One reason why so little is known about the decision process that underpins alignment is that
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customer heterogeneity and process complexity makes the task particularly challenging.
However, by definition, such heterogeneity and complexity constitute the reasons why value
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is created in a service exchange. In a service operations context, customer heterogeneity is
reflected in customer preferences for commoditized services and highly differentiated services
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(Anderson et al., 2011). “Process complexity” refers to the degree of interaction and
interdependency (otherwise known as complementarities) between the operational capabilities
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that are required to deliver a service. The critical and strategically important issues that follow
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are: 1) determining what operational capabilities are required for a commoditized service
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offering and a differentiated service offering, and 2) how these operational capabilities differ
in their level of interaction. The literature has not addressed the question of how managers
orchestrate operational capabilities (Priem & Butler, 2001; Sirmon et al., 2007), and we argue
that future research needs to better understand the conditions under which service alignment
is more or less effective.
This paper makes another contribution: we examine the endogenous role that managers play
during the alignment process. Our emphasis on the choices that managers make in aligning
value-generating operational capabilities departs considerably from the traditional approaches
that label the state of alignment based on the post hoc level of consensus between operational
Modeling the decision to align capabilities with customer needs, page 3 of 4
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outcomes and organizational goals (Boyer & McDermott, 1999). Hitherto, researchers have
ignored the important “deciding” aspect of alignment — where managers make choices about
which of the many operational capabilities available to the firm will best align to a portfolio
and can be measured at the moment of their selection (Makadok, 2001).
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of customer service requirements. These choices reflect the value of operational capabilities
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This study goes beyond a reliance on survey or secondary data by utilizing an experimental
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methodology to test two propositions. An experimental approach allows for the explication of
managerial choices in controlled scenarios that lead to less ambiguity regarding how
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managers interpret the relative importance of operational capabilities.2 This reduces the
impact of an individual’s perception of the measurement instrument, allowing them to make
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better comparisons about which capabilities perform better in different service contexts. It
also allows us to examine managers’ reactions to identical strategic and operational options in
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identical environments. The method has been shown to be effective in modeling complex
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social and economic behaviors in service operations management (Pullman et al., 2001), in IT
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service strategy (Richard et al., 2012), and in the design of government and public services
(Verma et al., 2006).
Although experiments of this type represent one of the better ways to disassemble decision
making, they are useful for prediction only to the extent that the experimental design provides
realistic variants of the context in which actual decisions are made. Realism requires a depth
of focus, which is achieved in this study by directing attention to the tender response and bid
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Strictly speaking, our approach is quasi-experimental, in that we do not experimentally manipulate the managerial characteristics and
other factors that may influence outcomes. We do, however, account for them in the analysis.
Modeling the decision to align capabilities with customer needs, page 4 of 5
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preparation process in the third-party logistics (3PL) industry. The 3PL industry is a large,
highly competitive, specialized intermediate service operations market where most providers
renew between 15% and 30% of their contracts each year (Saipe & Seiersen, 2007). Deeper
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theoretical and empirical understanding of the way managers select operational capabilities
during the contracting or tender review and bid preparation process is warranted, due to the
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revenues in the US alone were above $127 billion in 2010.
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size and strategic value of the 3PL accounts. For example, Burnson (2011) reports that
The remainder of the paper is organized as follows. The next section outlines the theoretical
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background to our work and presents the research propositions. The ensuing discussion
outlines the research methodology and describes the empirical process whereby specific
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measures were developed to test our model. The results section quantifies the different ways
that managers choose to compete with different capability levels. Finally, we present the
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implications of this research for both scholarship and management practice.
Perspectives on alignment and the resource-based theory
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2 Theoretical background
The alignment of supply with demand is core to operations management (Cachon &
Terwiesch, 2012). Operations management scholars have traditionally measured the state of
alignment by assessing the degree of consensus, convergence or congruence between objects.
This is achieved by measuring the “level of agreement within the organization regarding the
relative importance of cost, quality, delivery and flexibility to the organization’s operational
goals, as well as the relationships between these competitive priorities and operational
policies” (Boyer & McDermott, 1999, p. 290). By comparing the scores on these
characteristics, researchers can report the presence or absence of alignment. This approach
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tells us what level of alignment was achieved and whether increased congruence between
objects leads to increased performance (Hill, 1985). A small number of scholars have come to
see alignment as a process of related choices based on individual cognitive characteristics that
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play a key role in assessing the value of specific resources and operational capabilities
throughout the organization (Hanson et al., 2011). This viewpoint directs our attention to the
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central role that managers play in the way resources are built and deployed to account for
changing market circumstances (Adner & Helfat, 2003; Ambrosini & Bowman, 2009). This
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viewpoint is important because service design models, such as the service strategy triad (Roth
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& Menor, 2003), have little to say about the choices that managers make about which specific
service design capabilities will be required to satisfy diverse customer markets.
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Resource-based theory posits that a firm’s ability to create and appropriate value stems from
differences in the possession of resources (Barney 1991), as well as the decisions by
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managers about the orchestration of resources (Sirmon et al., 2007). To explicate the role of
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managers in RBT, Sirmon et al. (2007) propose a resource management framework that
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reflects the partially sequential actions of managers. They define resource management as
“the comprehensive process of structuring, bundling and leveraging the firm’s resources with
the purpose of creating value for customers and competitive advantages for the firm” (p.
1392). Structuring is based on the acquisition and accumulation of a portfolio of resources.
Once a portfolio of resources is in place, the firm can then bundle resources into operational
capabilities. These operational capabilities then enable leveraging to occur. Thus, the ability
to create value requires all three processes to be synchronized in ways that align with the
service offering required by customers.
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An operational capability can be considered valuable if it either enables customer needs to be
better satisfied (Verdin & Williamson, 1994), or if it enables a firm to satisfy needs at lower
costs than competitors (Peteraf, 1993). The argument that resources have value in relation to
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their ability, inter alia, to meet customers’ needs is entirely consistent within RBT (Makadok,
2001) and service operations management (Roth & Menor, 2003). Resource-based logic also
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recognizes that it is not easy for managers to form an accurate picture about the precise
contribution a specific operational capability will make under conditions of customer
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heterogeneity and process complexity. Under a regime of uncertainty, where customer
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preferences vary and the variety and connectedness of the service offering is complex, the
ability to determine value in any specific operational capability is subjective (Kraaijenbrink et
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al., 2010). This study seeks to explain the subjective resource management process whereby
managers’ value operational capabilities, and then investigates how managers orchestrate
Operational capabilities and service operations management
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operational capabilities in different service settings.
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Resource-based theory is characterized by inclusive definitions, where scholars appear to care
little about whether constructs are labeled as “resource based,” “capability based,” or
“competence based” (Kraaijenbrink et al., 2010). For example, Barney (2002, p. 155) defines
firm resources as “all assets, capabilities, organizational processes, firm attributes,
information, knowledge, etc. controlled by a firm that enable the firm to conceive of and
implement strategies that improve its efficiency and effectiveness.” While he considers
inclusiveness to be part of RBT’s strength, others believe it to be a weakness that generates
ambiguity and confusion (Priem & Butler, 2001). Kraaijenbrink et al. (2010) argue that
scholars need to recognize explicitly the differences among types of resources.
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The focus in the present study is a subset of organizational capabilities that we define as
operational capabilities. Drawing on Helfat et al. (2007) we define operational capability as
the capacity of an organization to purposefully bundle its resource base in ways that enable
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the organization to perform the ongoing task of transforming inputs into outputs. The terms
“capacity” and “purposefully” imply that managers make a choice about how best to leverage
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capabilities to align with a specific service objective. Our definition of operational capability
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is consistent with recent work in operations management (e.g., Wu et al., 2012).
An examination of the way managers align operational capabilities within a service design
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context requires researchers to examine the effects of the wide variety of operational
capabilities that transform inputs into outputs. These include the role of people within the
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organization, the technological infrastructure required to achieve greater visibility and
transparency throughout all aspects of the supply chain (Chase & Apte, 2007), and the
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2010).
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centralized or distributed nature of location in relation to the customer (Ponsignon et al.,
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Although listing all the capabilities that are relevant to 3PL providers is not possible, certain
types of capabilities can be recognized in all businesses that reflect the core processes for
creating economic value. Day (1994) suggests that the most distinctive features of a customeroriented organization is their mastery of “outside-in” and “inside-out” capabilities, along with
“spanning capabilities” (pp. 40–41). Outside-in capabilities are associated with customer
engagement; and inside-out capabilities are associated with the delivery pitch to the customer.
Critical spanning capabilities are manifested in “typical business activities such as order
fulfilment, new product development and service delivery” (p. 38). In the tender review and
bid response context these capabilities are required to enhance learning and integrate
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specialist knowledge across functional units in the bid team, in order to create innovative
customer-focused solutions, support information exchange with IT and maintain operational
improvements in on-time and error-free delivery. Figure 1 graphically illustrates the
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distinctive capabilities that underpin value creation in the tender process in the 3PL industry.
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< Figure 1 here >
3 Research propositions
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How managers best allocate operational capabilities to new and existing business
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opportunities is an important managerial function that has been under investigated. These
choices require managers to consider and make trade-offs between a number of operational
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capabilities based on what they believe will create the most value. Porter and Siggelkow
(2008) propose that managers must also understand context to avoid misjudging the subtle
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ways in which resources and capabilities interact under different market conditions. The
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present study seeks to build on this work by uncovering differences in the ways that managers
allocate distinctive operational capabilities to align with two service contexts: one based on
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commoditization and the other on customization.
3.1 Aligning stand-alone operational capabilities in commodity service
contexts
Logistics is a turbulent industry where customers can be quite varied in terms of their
demands, and highly sensitive to price variability. Empirical studies confirm this view and
indicate that customers often view logistics companies as commodity providers that offer
tools for reducing operating costs and improving basic logistics services, such as speed and
reliable delivery (Anderson et al., 2011). A commodity is considered to be a nondifferentiated service offering that is sold primarily on the basis of price (Samuelson, 1948).
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The core components of a commodity are well known, mostly stable and widely shared
amongst competing firms. Weill and Ross (2009) suggest that companies such as United
Parcel Service (UPS) have long benefited from a clear operating model that offers a highly
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standardized end-to-end service delivery package. In commoditized service settings,
standardized delivery enables the sequencing of activities to compete on price by reducing
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overall production costs that leverage common factors of production across locations
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(Campbell & Goold, 1998; Dewett & Jones, 2001).
Work in service operations provides a hint as to the operational capabilities that will be
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important in a commoditized service setting. For example, it is expected that managers will
place a heavy emphasis on sustained incremental improvement capabilities that enable
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specialist knowledge to be exchanged, in order to lower costs and improve service delivery
(Grant, 1996). The customer contact model holds that less direct contact with the customer
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will enable managers to develop service delivery systems that operate at peak efficiency, as
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required by commodity markets (Chase, 1978). Thus, information technology that enables
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firms to share information across products, management services, and locations will be
important.
When orchestrating a portfolio of operational capabilities for a commoditized service offering
some operational capabilities will be executed adequately, others poorly; but a few must be
superior to the competition if the business is to sustain a market position that is valuable and
difficult to match (Day, 1994). It is important, therefore, to know which operational
capabilities create a distinctive capability, rather than being simply part of a sequential series
of necessary activities. It is also important to know if the operational capability will create
positional advantage without interacting with other operational capabilities. Schmidt and Keil
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(2013, p. 211) describe this situation as comprising stand-alone improvements that arise “due
to the availability of new technology that reduces average costs or that allows new product
features that increase customers’ willingness to pay.” Indeed, the success and failure of
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market-leading service providers such as DHL relies on the fact that the managers of firms are
making more strategically appropriate decisions than others (Coltman et al., 2010).
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Experienced decision makers will recognize the discrete benefits from distinctive operational
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capabilities in a commoditized service setting where competitive pressures are high and the
relationship between the service provider and the customer is primarily formed on the basis of
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price. Thus we propose:
P1: In markets characterized by commoditized service delivery, managers will
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perceive greater direct benefits from stand-alone operational improvement
capabilities that support standardized end-to-end solutions, than they will in
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markets characterized by customized service.
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3.2 Aligning complementary operational capabilities in customized
service contexts
In a customized (or differentiated) service case, discrete product features (such as overnight
delivery, reliable supply and comparative costs, once the mainstays of the industry) are no
longer considered sufficient, and a greater emphasis is placed on innovative solutions
(Andersson & Norrman, 2002). These innovative solutions help customers achieve reliability
levels high enough to enable inventory cost savings, and provide greater visibility and
transparency throughout all aspects of the supply chain (DHL, 2004).
Managers are driven by the search for novelty and innovative customer solutions whenever a
customized service is required. Innovative services are achieved by combining previously
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unrelated capabilities (March, 1991), and new problem-solving approaches based on cospecialized assets (Lippman & Rumelt, 2003). This view incorporates the opportunityrecognition perspective associated with innovation, wherein managers seek to combine
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resources and capabilities in ways that create new learnings and relationships between
corporate means and customer ends (Shane, 2012).
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Operational capabilities can “create additional product market value by interacting with the
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firm’s current resource base through creating complementarities” (Schmidt & Keli, 2013 p.
211). The theory of complementarities ─ developed originally in economics to explain
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changes in modern manufacturing (Milgrom & Roberts, 1995) ─ argues that resources and
capabilities reinforce each other to increase their marginal productivity (Devinney & Stewart,
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1988; Collis & Montgomery, 1995). Complementarities are said to exist when “doing more of
one thing increases the returns of doing more of another” (Milgrom & Roberts, 1995, p. 181).
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In other words, the synergies that arise when specific operational capabilities are combined
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exceed the value-creating capacity of each resource in isolation.
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In a customized service setting, firms will place a premium on complementary operational
capabilities because competitors find it hard to imitate the interactions between capabilities.
These interactions are surrounded by causal ambiguity and structural complexity, enhancing
the firm’s potential to differentiate the service offering and sustain a positional advantage.
Thus, we propose:
P2: In customized service-oriented settings, managers will place greater
emphasis on synergies between operational capabilities than they will in markets
characterized by commodity services.
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4 Research design and method
The domain used to test our propositions was the Asia Pacific management team of a major
multinational logistics firm, which included representation from business units in Australia,
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New Zealand, Singapore, China, Hong Kong, India, Japan, South Korea, and Taiwan. The
3PL market is highly competitive, and contract bid teams routinely respond to tenders with
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major multinationals, amounting to hundreds of millions of dollars per year. During each
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tender preparation process teams of senior personnel from regional office and country
subsidiaries come together to design the service delivery system that will provide the best
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chance of winning the contract, subject to the profitability requirements of the business.
Figure 2 illustrates the methodology applied. The subsections below provide detail on
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important aspects of each step. The core of the analysis is the discrete choice experiment
conducted in Step 2. This is where we test our propositions. The discrete choice experiment is
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based on the mixture of capabilities underlying the “service concept scenarios” derived in
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Step 1. Step 3 represents a validation step where we show that the manipulation behind the
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discrete choice scenario is valid.
< Figure 2 here >
Step 1: Establish a representative model of the service concept
scenarios
In Step 1 we undertook a multi-stage process to identify the customized and commoditized
“service concept scenarios” as given in Appendix A. Alpha Corporation is representative of
the “customized service” segment. It is a firm noted for innovation and flexibility and
demands high levels of customer interaction, product/service recovery and proactive
innovation from its suppliers. It is less concerned about price and capacity issues. Beta
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Corporation is representative of the “commoditized service” segment. It is a firm noted for
cost leadership and efficiency. For it, price is the dominant factor, with other concerns playing
a secondary role, while supply chain innovation is immaterial.
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The two scenarios are consistent with low cost/differentiation strategies (Porter, 1996),
operational excellence, and customer intimacy value disciplines (Treacy & Wiersema, 1993).
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To add realism to the overarching manipulation used in Step 2, we obtained real request for
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quotation (RFQ) documents from large multinational companies. To ensure that the text in
each scenario was consistent with a customized and commoditized service we used the results
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from a recent 3PL customer study conducted by Anderson et al. (2011).
Next, we needed to establish which capabilities were required to deliver the outcomes
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identified in each of the two representative service concept scenarios. Operationally, two
different approaches were applied. First, 15 senior managers were interviewed (all interviews
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were recorded and transcribed). This was complemented with observation of the tender
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response processes and secondary data collection (for example, actual tender response
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documents), with the aim of developing an understanding of the contract bid response
process. Based on these interviews, it was possible to determine the overarching sequential
process for each competitive bid, which was: 1) establish a plan, 2) define the components
that business unit representatives will work on, 3) collate responses, 4) price the work, 5)
check the tender response for quality, and 6) deliver the pitch to the customer.
The second approach applied a thematic analysis to the recorded interviews and transcripts,
observation notes and tender bid documents to identify the most important capabilities
required for each service concept scenario. Because the tender review and bid preparation
process relies heavily on information and knowledge integration between business units, we
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draw on the theoretical work by Day (1994) on market-driven capabilities, and the knowledge
integration mechanisms proposed by Grant (1996). Our interpretations of the data was
validated by following the four-step process proposed by Hirschman (1986), where a priori
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definitions for all the capabilities were pretested and workshops were held with managers to
ground interpretation in the tender bid response process. In addition, several presentations of
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our findings were made to the participating firm during the workshops to ensure that the
scenarios proposed were realistic. This validation process helped to establish the validity of
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six capabilities used in the experimental manipulation (Step 2), both in terms of their
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distinctiveness and their definitions.
Table 1 presents the definitions of the six capabilities identified, which we label: active
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customer engagement (AE), cross-functional coordination (CF), innovation and creative
solutions (CS), continuous operational improvement (OI), IT infrastructure (IT), and the
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professional delivery of the tender offer (PD).
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Our propositons imply that good operating performance based on standardized or highly
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integrated processes is vital when the service is considered to be a non-differentiated or
commoditized product. This demand structure will place an emphasis on operational
improvement (OI) in processes based on standardization, and IT infrastructure (IT) that
enables efficient information exchange across products, services and locations (Bharadwaj,
2000). Both operational capabilities generate efficiencies relative to the competition. Crossfunctional coordination (CF) is central to organizing for process improvement and knowledge
creation in operations management (Delbridge & Barton, 2002). However, in customized
settings where intangible components matter, suppliers will direct attention towards more
innovative solutions (CS) where higher levels of customer engagement are required (Chase,
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1978) to ensure that client-specific needs are well understood. Table 1 illustrates the
differences in capabilities levels, reflecting the importance and efficiency and standardization
when commoditization is sought after and the need for capabilities to mold with client-
< Table 1 here >
Step 2: The service delivery system design trade-off
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specific needs when customized service demand is preferred.
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In Step 2 we moved onto the discrete choice experiment (DCE), which allowed us to test our
propositions formally. Specifically, the DCE was designed to measure the degree to which
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variations in the underlying capabilities would impact on the likelihood of the expected
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success of a bid (see Appendix B for the sample task).
The logic underlying this approach is based on the fact that any empirical assessment of the
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underlying capabilities necessary to support the commoditized-customized dichotomy cannot
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rely on customers only. This is so for both practical and cognitive reasons. First, customers
are unlikely to be aware of, or even interested in, how service provider capabilities interact to
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yield the value they receive. Hence, the most appropriate sample of raters (or respondents) in
a study such as this should include experienced service provider decision makers that
frequently choose between different capability levels to identify the best service design
configuration. Second, these managers rely on mental representations derived from their
experience and training to determine the level of capability required for different service
contexts. It has been shown that the mental representations held by skilled decision makers
enable them to size up a situation quickly, to identify the way things interconnect and to
determine what types of actions are appropriate (Lipshitz et al., 2001). These mental models,
cognitive maps or causal maps are simplified representations based on combinations of
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observation, intuition, and expertise (Klein, 1998) which enable managers to allocate
resources in ways that align with different/heterogeneous service contexts.
The testing of the propositions via a DCE was conducted in line with established methods
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(see Louviere et al., 2000, for a detailed discussion). Managers were presented with 16 sets of
four mixtures of capabilities as presented in Appendix B, with eight of the choice sets relating
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to each service concept scenario. Each capability had two levels (adequate and superior), as
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outlined in Table 1. Based on Step 1 in our research process, these two levels were viewed as
appropriate because the firm felt that no provider would be offering services based on
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“inadequate” capabilities. Each capability was associated with an experimental attribute that
was defined in the DCE. The design used to create the choice sets was based on a D-optimal
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design, where the resulting treatment combinations were presented to enable the collection of
preference data (Louviere et al., 2000). The key advantage of this family of designs is that it
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minimizes the generalized variance of the resulting parameter estimates while providing
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sufficient data to enable the estimation of main effects and all two-way interactions. It also
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minimizes the number of choice tasks that the managers need to complete.
The managers task was to choose which of the options out of the four presented in each set
was: 1) MOST likely to create a winning bid, or 2) LEAST likely to create a winning bid.
These were our dependent variables in the logit estimation with the option representing
MOST coded as +1, the option representing LEAST coded as ˗1, and the two non-chosen
options coded as 0. This coding is equivalent to a partial ranking task.
Sample
Sixty-two managers completed the DCE. These individuals represent the majority of
personnel responsible for designing tender responses for major TPL customers across the Asia
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Pacific region. The sample size was established to fulfill the model specification and
identification requirements of the experimental design completely.3 Fifty-four percent were
female and 76 percent had completed a tertiary qualification. They were, on average, 34 years
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of age with a range of 22–46 years. To verify the expertise of the sample, individuals were
asked to indicate if they had previously been involved in the selection of a particular
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transportation and logistics service provider. This binary YES/NO question is appropriate to
establish response reliability where managers are required to assess the value of different
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operational capabilities in the tender review and bid preparation process (Sirmon et al., 2007).
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All respondents who answered YES to this question were included in the final analysis. Respondents were also asked to indicate their level of influence on the tender response and
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bid preparation strategy on a five-point semantic differential scale anchored with “no
influence” and “maximum influence.” The average score of 3.1 also supports the
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appropriateness of the sample.
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Step 3: Establish the validity of the scenario manipulation
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The most important component of our analysis was the overarching “service concept
scenario” manipulation that conditioned the DCE. Hence, it is critical that we establish the
validity of this manipulation; in other words, to determine if the attributes used in the scenario
manipulation were appropriate and that the service concept scenarios (that is, commoditized
and customized service offerings) were perceived differently.
3
Two factors imply that sample size is not an issue. First, the sample represents nearly every manager in the sample universe. Hence, a
larger sample size was impossible. Second, the use of DCE with 16 choice options implies that we have considerable information on
each manager, being able to characterize their decision models effectively, both in the aggregate and at the individual level.
Modeling the decision to align capabilities with customer needs, page 18 of 19
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Independence was measured using a set of hierarchical tests that estimated one model relative
to the other across a range of scale factors. The full procedure is shown in Appendix C.
Results reveal that the test statistic for λ1 is 57.47, with the point estimate of the scale factor
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ratio μ = 0.5. Thus, λ1 can be rejected, because it exceeds the critical value of 24.32. While
this negates the need to test λ2, it is noteworthy that this statistic is also rejected, with a value
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of 52.46. Together, these tests support the discriminant validity of the scenario-level
treatment, providing for direct comparison of parameters between the two service concept
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models.
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5 Results
The model used to analyze the DCE data is a variant of the conditional logit model, where the
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capabilities and their associated levels act as the independent variables, with the dependent
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successful.
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variable being the preferred choice set that will maximize the likelihood of a tender bid being
The DCE results in Table 2 provide output of the models for the aggregated, commoditized
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and customized service concept alternatives. The first proposition implies that managers
facing scenarios where a commoditized service is desirable will utilize discrete capabilities
(for example, standardization, efficient information integration) more intensively than
managers facing scenarios where demand implies a more customized service. When faced
with the commodity service scenario, all six capabilities were significantly associated with the
arrangement of an attractive bid: AE, CF, CS, OI, IT, and PD. In contrast, in the differentiated
service scenario, managers identified only three variables as significantly associated with bid
success: AE, CS, and OI. The absence of any negative sign in the commodity service scenario
Modeling the decision to align capabilities with customer needs, page 19 of 20
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reveals a clear preference for superior over adequate capabilities in the commodity service
context.
< Table 2 here >
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The second proposition examines the importance of the interdependencies between
capabilities, where we propose that the benefits accruing from the interaction between
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enhanced service capabilities will be more pronounced in the pursuit of a customized service
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offering. Nine of the fifteen possible interactions were significant in the customized scenario,
while only one of the fifteen was significant for the commoditized scenario. In other words,
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when managers are required to respond to customer demands for innovative solutions they
give more standing to the complementarities between capabilities. Therefore, Proposition 2 is
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supported, with the results revealing that managerial preferences for IT and CF depend on the
level of complementary capabilities in CS, AE, and OI.
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As an example, consider the benefits accruing from IT infrastructure, when a customized
service concept is required. The interactions with active engagement (β = 0.29, p < 0.05),
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cross-functional coordination (β = 0.46, p < 0.001), innovative and creative solutions (β =
0.21, p < 0.05), operational improvements (β = 0.35, p < 0.05), and professional delivery (β =
0.20, p < 0.05) are all significant. The differences between models reveal that context plays a
critical role in understanding the value of IT infrastructure. A similar result is visible with
cross-functional coordination, where all five interactions are significant in the customized
service; whereas all interactions in the commoditized service model are not significant.
Further useful descriptive statistics provided by the logit model are the odds-of-choice ratios
(see Figure 3). These scores are a measure of effect size, describing the strength of association
across the two levels for each capability in the study. For example, when a commoditized
Modeling the decision to align capabilities with customer needs, page 20 of 21
Page 21 of 44
service offering is required, there is a 245 percent increase in odds (p < 0.001) that managers
will choose end-to-end solutions that are integrated and fast, and a 90 percent increase in odds
(p < 0.001) that managers will choose a highly integrated IT infrastructure capability that
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covers regions and countries. However, when a differentiated service offering is required,
there is a 195 increase in odds (p < 0.001) that managers will seek novel business solutions.
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The greatest differences between models are found in the preference for integrated versus
stand-alone IT infrastructure, the extent of collaboration across country and regional levels,
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and the scalability of end-to-end solutions. The results in Figure 3 provide confidence that the
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model is a valid representation of distinctive capabilities that managers consider necessary to
support the two service offerings.
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< Figure 3 here >
6 Discussion
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This paper contributes to the core alignment proposition that forms the basis of the service
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strategy triad in service operations management (Roth & Menor, 2003) by providing an
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empirically grounded account of the alignment choices that managers make under different
contextual settings. Despite the importance of resource allocation decisions to effective
management within service-oriented firms such as Maersk, DHL, FedEx, and UPS, important
questions about how and to what extent the mobilization of operational capabilities might vary
have been rarely examined.
Our results serve as a basis for strengthening the empirical base of RBT by identifying the ex
ante conditions under which managers attribute value to six distinctive operational
capabilities. When a commoditized service is required, managers seek to compete on the basis
of distinctive capabilities to leverage sub-additive cost synergies (Tanrivedi, 2006). In other
Modeling the decision to align capabilities with customer needs, page 21 of 22
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words, the ability to win a competitive bid arises from operational capabilities that are
superior to the competition; if the capability is not scarce, it will not improve the firm’s
relative advantage (Schmidt & Keil, 2013). When a differentiated or customized service is
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required, it is not necessary for all operational capabilities to be superior to competitors.
Instead, the value of operational capabilities (such as IT and CF) depends on the value of
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other spanning capabilities (such as CS, OI, and AE). By interacting with the firm’s current
operational capabilities, complementarities are generated that provide new sources of value
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(Wernerfelt, 2011). In other words, super-additive value synergies (Tanrivedi, 2006) are
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sought where IT infrastructure and CF coordination capabilities interact with other
capabilities to increase their joint value: value(a, b) > value(a) + value(b).
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Defining the specific operational capability levels required for different services provides for
sharper differentiation than has been previously reported in the service operations literature
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(Karwan & Markland, 2007). For example, the results provide managers with a deeper
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understanding of how firms combine and recombine resources to align their supply strategy
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with product and service characteristics. The fine-grained assessment of operational
capabilities discussed here is consistent with work in the management literature (e.g., Miller
& Shamsie, 1996) showing that property-based resources (such as long-term contracts with
movie stars and theatres) are more valuable in stable environments, while knowledge-based
resources in the form of production and coordinative talent are more valuable in uncertain
(changing and unpredictable) environments. This study suggests that efficient information
integration based on standardized procedures is more valuable in commodity service contexts
and complementarities that enhance service innovation are more valuable in customized
service contexts.
Modeling the decision to align capabilities with customer needs, page 22 of 23
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Until recently the role of managers and their decisions have been largely absent from the
RBT. However, while this trend is being moderated by recent conceptual advances (Adner &
Helfat, 2003; Helfat et al., 2007; Sirmon & Hitt, 2009; Sirmon et al., 2011) further study is
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warranted (Crook et al., 2008). In this vein, our study makes an important empirical
contribution by drawing on a choice-theoretic methodology to unpack management decisions
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and using the information gleaned from that analysis to determine how managers choose to
compete during the tender response process. For example, at the aggregate level (see Table 2),
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our research supports the basic proposition found in the business press that general-purpose
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infrastructure technologies (such as railroads, plumbing and electricity, and IT infrastructure)
do not generate competitive advantages because they are both common and replicable (Carr,
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2003, p. 5). However, when the two-segment model is taken into account ─ based on a more
sophisticated analysis that distinguishes between commoditized and differentiated service
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offerings ─ the results reveal that general purpose infrastructure technologies such as IT are
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critically important to positional advantage.
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Resource-based theory has been criticized by many scholars for its definitional ambiguity
(Foss, 1997) and circular logic, where successful firms are so because they have unique
resources (Powell, 2002). This study addresses some of the shortcomings in RBT by
identifying the specific levels of operational capabilities (i.e., a particular resource type) that
are necessary for commoditized and customized service settings. The results reveal that
knowing more accurately the relative value of different levels of operational capabilities is at
least as important as possession of better operational capabilities. By shedding new light on
how interactions between operational capabilities matter, the study builds on and extends a
small body of work, which has investigated resource complementarities (see, e.g., Powell &
Dent-Micallef, 1997; Coltman et al., 2011a) and value synergies (Barua & Whinston, 1998).
Modeling the decision to align capabilities with customer needs, page 23 of 24
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These findings are important to service operations and innovation research (Dewett & Jones,
2001) because they reveal that when capabilities are tightly linked they create complexity,
ensuring that any imitation is both challenging and time consuming. Even if competitors
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understand the technical nature of each capability, they will find it difficult to decipher the
proper sequence in which it is deployed, creating unexpected combinatorial effects. Finally,
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the results add to the literature regarding the effective orchestration of capabilities (Sirmon &
Hitt, 2009; Sirmon et al., 2011) by demonstrating “a high order of integrative capacity”
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(Lawrence & Lorsch, 1967, p. 245) that managers possess. This higher-order capability to
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align the right service offering to the right customer appears to pass the resource-based view
of the firm test; becoming a valuable rent-producing resource that is scarce, inimitable, and
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non-substitutable (Barney, 2002).
Understanding how managers attribute value to those operational capabilities that will
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increase their ability to win a competitive bid helps us gain an understanding of what is
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required to improve operational performance. An important benefit of the experimental DCE
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methodology used in this study is that it aligns closely with actual choice behavior and avoids
the well-documented biases inherent in alternative methods such as ratings (Lenk & Bacon,
2008). Although prior operations management research has identified that cost, quality,
delivery, and flexibility are important (Schroeder et al., 2002), we know little about what level
of operational capability is required or what additional operational capabilities drive
managerial choices. The use of DCE allows researchers to address this issue in two ways.
First, because of the attention paid to both attributes of a decision and the levels of the
attributes, we can determine both which attributes are important and how that importance
varies with the level of those attributes. Second, because DCE “does not force subjects to
place answers on scales that are inconsistent with the ‘natural’ decision processes used by
Modeling the decision to align capabilities with customer needs, page 24 of 25
Page 25 of 44
managers,” it “offers greater control over alternative explanations” (Richard et al., 2012, p.
88). Hence, using a properly designed choice experiment enables operations management
researchers to design, collect, and assess the impact of complex operational trade-offs in new
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t
ways that is of value both practically — in telling managers how to optimize — and
academically — in helping scholars rule out alternative theoretical explanations of
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phenomena.
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In addition, although panel and survey studies can reveal statistically determined relationships
between practices (such as total quality management, lean manufacturing, or vertical
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integration) and performance, the actual resource orchestration choices that managers make
are treated as a black box. The variance that exists is assumed to be between firms, while the
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managers within a firm are assumed to be homogeneous or randomly different. Drawing on
discrete choice experimental methods — based on organizationally relevant scenarios and
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controlled decision tasks — we provide an alternative perspective that more directly measures
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the inner workings of the managerial black box. Specifically, we measure the resource
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configuration choices that individual managers make when responding to a customized and
commoditized service requirement. The relevant question here is not “How much does the
practice matter?,” but “Which specific operational capabilities are required?”. Furthermore,
we provide a level focus, whereby attention is given to the particular levels within each
operational capability. The relevant question here is “What level of operational capability will
provide the best chance of success?”. Hence, our study complements more traditional
practice-performance research in operations management that has identified various drivers of
high performance (Ketokivi & Schroeder, 2004) by revealing the extent to which
heterogeneity exists below the level of the firm.
Modeling the decision to align capabilities with customer needs, page 25 of 26
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This work is important because it brings the manager back into the service operations
equation. By concentrating on the role of the service operations manager as a decision maker
about what operational capabilities need to be built and deployed (Sirmon et al., 2007), we
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reveal an important source of variance in service outcomes. Hence, we show that managers
matter in the sense that they are simply not machines executing a firm level model. By doing
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this we shed new light on a key question in RBT by explaining how organizations end up
orchestrating the resources and operational capabilities in their possession to create superior
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value (Maritan & Peteraf, 2011; Schmidt & Keil 2013). As Zomerdijk and de Vries (2007)
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state, a starting point for future research in service operations is the empirical consideration of
the service design choices that managers actually make.
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7 Limitations and directions for further research
In writing about the theory building process, Weick (1979) cautions that theories developed to
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explain human activities cannot be generalizable, while they simultaneously offer high levels
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of accuracy and simplicity. Service design is a complex human activity, and this study
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p
sacrifices breadth for high levels of accuracy and simplicity. Thus, a limitation of this study is
that the results do not necessarily offer broad applicability beyond the contract logistics
population from which the sample of customers and supplier managers were drawn. This is
true of all resource- and capability-based theories where “the actual effect size of a particular
resource is context dependent” (Armstrong & Shimizu, 2007, p. 977) and further research is
required to confirm the generalizability of these results. Despite this limitation, the constructs
used in this study exhibit a high degree of applicability to many industries (Tanriverdi, 2006).
Bendoly et al., (2006) state that the success of operations management and the accuracy of its
theories rely heavily on an understanding of human behavior. The power of experimental
Modeling the decision to align capabilities with customer needs, page 26 of 27
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approaches is that they provide a clearer and more complete picture of a manager’s decision
calculus. Although many scholars believe that it is reasonable to forecast/predict market
outcomes from experimental scenarios (McFadden, 1986), all stated preference techniques
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suffer from the limitation that managers (and customers) may not behave as they reveal they
will in experiments (Lovallo & Sibony, 2010). Thus, we make no claim to model the actual
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success of the 3PL tender process because customers (not managers) determine the value in a
service exchange (Vargo & Lusch, 2004). It is important to note, however, that the managers
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sampled in this study represent a global market leading 3PL firm, and that anecdotal evidence
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exists to indicate that the systematic alignment of internal operational capabilities with
distinct buyer behavior structures has led to improved performance (Coltman et al., 2010;
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2011b). This work allows us to speculate that the sample of managers studied are able ex ante
to make better choices concerning the level of operational capability required to create and
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determined by the customer.
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capture value and that these choices should be close to the ex post realized resource value as
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p
Future work should seek to validate this claim, and we urge researchers to continue to explore
new experimental approaches that allow scholars to unpack the performance implications of
resource orchestration decisions in controlled ways. Further, the tender review and bid
response process is increasingly common and worthy of further investigation to evaluate the
generalizability of the results across a wider range of companies and industries. The design
literature offers another perspective that is worthy of greater investigation because it is
directly concerned with the way managers draw on associations and analogies to build up a
“prediction” for what might work (Romme, 2003).
Modeling the decision to align capabilities with customer needs, page 27 of 28
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8 Conclusion
The need to match supply with demand has gained a prominent position in the service
operations (Roth & Menor, 2003) and the wider operations management (Cachon &
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Terwiesch, 2012) literatures. However, the role that managers play in the alignment process
has been rarely investigated. This is critical because, if managers do matter, as the
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management literature indicates, then methodologically they are a “source of variance” that
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needs to be taken into account, irrespective of the empirical modeling used.
This study makes two contributions by empirically capturing the way managers orchestrate
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operational capabilities to better align with a customized and commoditized service
requirement. First, the results show how two understudied areas of operations management ─
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the role of managers in bundling operational capabilities (Sirmon et al., 2011) and the role of
alignment that forms the basis of the service strategy triad (Roth & Menor, 2003) — can be
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fruitfully combined to gain new insights into how firms compete during the tender review and
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bid preparation process. Second, from a methodological perspective, following on from work
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of Mantel et al. (2006), Garver et al. (2012), and others, we provide additional evidence about
the importance of stated preference methods for examining operational management issues.
Acknowledgements
This research was supported in part by the Australian Research Council under grant number
LP0668056. The authors wish to thank Pierre Richard, Byron Keating, Edward Anderson,
John Gattorna and Stuart Whiting for their contributions to discussions and the direction of
the research project.
Modeling the decision to align capabilities with customer needs, page 28 of 29
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Appendix A: Experimental scenarios
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Customized scenario: Alpha Corporation
Commodity scenario: Beta Corporation
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Appendix B: Sample DCE task
Option 2
Option 3
Active engagement
Adequate
Superior
Superior
Superior
Cross-functional coordination
Superior
Adequate
Adequate
Superior
Creative solutions
Adequate
Superior
Superior
Adequate
Operational improvement
Superior
Superior
Superior
Adequate
IT infrastructure
Superior
Adequate
Superior
Superior
Professional delivery
Superior
Superior
Adequate
Superior
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1. Which option is MOST likely to
create a winning bid
(Please tick one only)
2. Which option is LEAST likely to
create a winning bid
(Please tick one only)
Option 4
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Option 1
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Appendix C: Manipulation check on customer scenarios
In order to draw comparisons between the attributes and across groups, we generated odds
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ratios, which act as comparable effect sizes, and show the relative impact of the variables
across experimental groups. An odds ratio is the ratio of the odds of an event occurring in one
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group to the odds of it occurring in another group. The application of this technique led to the
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generation of odds ratios under each of the two scenarios, each representing the odds of
selecting an option when being presented with an attribute at one of its two levels.
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Odds ratios can be calculated from the coefficients of a logistic regression. If we regress Y on
for X is related to a conditional odds ratio:
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X, then the estimated coefficient
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where Y is a binary response variable and X is a binary predictor variable. We also have other
predictor variables (Z1, …, Zp), which may or may not be binary.
conditional odds ratio; that is,
is the estimate of the
is as an estimate of the odds ratio between Y and X when
the values Z1, …, Zp are held fixed. Although attribute coefficients from choice analysis are
standardized by the experimental design, it was also necessary to assess whether the impact of
random variance across groups may have led to misspecification of systematic variance. Here,
we again applied the model equivalence calculations.
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We assessed the validity of the group level manipulation by the set of hierarchical tests, as
outlined in Louviere et al. (1993). This aims to determine if the scenario manipulation
presented two distinct customer-demand scenarios (where Alpha Corporation presents a
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differentiated customer scenario, and Beta Corporation presents a commoditized customer
scenario).
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The evaluation of model equivalence was done by estimating one model relative to the other
across a range of scale factors. To examine model equivalence, we calculated the likelihood
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ratio test statistic using the following formula:
(1)
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λ1 = ˗2[Lμ ˗ (L1 + L2)]
where Lμ corresponds to the log likelihood with the estimate of the scale factor ratio (μ).
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This scale factor ratio is a simple multiplier that optimally scales the parameter estimates for
the second model relative to the first, providing for direct comparison of parameters between
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the two group-specific models. L1 and L2 correspond to the log likelihood estimates for the
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separate models (that is, exploration and exploitation). The test statistic is asymptotically χ2
distributed with (K + 1) degrees of freedom, where K is the number of parameters in the
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model. If this test statistic is not rejected, it is then necessary to determine whether this
equivalence is due to the scale factor. To achieve this, we estimated a test statistic using the
following formula:
λ2 = ˗2[Leq ˗ Lμ]
(2)
where Leq corresponds to the joint log likelihood value when the scale factor for both models
is fixed at 1. The test statistic is once again asymptotically χ2 distributed with (K + 1) degrees
of freedom. In our case, the test statistic for λ1 of 57.47 leads to the rejection of the null
hypothesis of model equivalence (it exceeded the critical value of 24.32). While this negates
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the need to test λ2, it is noteworthy that the statistic of 52.46 also supports the rejection of
equivalence. Together, these tests support the discriminant validity of the group-level
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treatment.
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Tender review and “bid preparation spanning processes”
Crossfunctional
coordination
Creative
solutions
Operational
improvement
IT
infrastructure
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Customer
engagement
Submission and pitch to customer —
“inside-out processes’
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Prepare tender solution, collate responses,
price and review quality
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Define tender scope and kick-off tender
approach — “outside-in processes”
Professional
delivery
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Figure 1. Tender review and bid preparation process: Basis of critical process
spanning capabilities
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Step 1: Establish representative model of customer market structures.
Review the literature to identify two representative service value disciplines
based on customized and commoditized service offerings
Identify capabilities required to create customer value for each segment
identified above.
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Establish a
representative model
of the service concept
scenarios
Detailed pre-testing to identify important capabilities in tender bid response
processes.
Interview senior managers (20 interviews).
Map bid response process flow.
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Complete thematic analysis.
Part B:
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Part A:
Compete quality function deployment exercise (15 managers).
Compare to prior case examples based on Apple and Lenovo.
Alignment trade-off
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↓
Step 2: The service delivery system design trade-off.
Conduct discrete choice survey (62 managers).
Examine direct effects and two-way interactions.
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Complete demographic analysis on respondents.
↓
Step 3: Establish validity of scenario manipulation.
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Discriminant validity
check
Complete hierarchical tests.
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Compare models to develop support for discriminant validity.
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Figure 2. Methodological steps and data gathering process
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300
Aggregate model
Differentiated service model
Commoditized service model
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191
161
150
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130
100
50
44
30
38
0
IT
AE
CF
CS
OI
PD
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‐50
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Odds of Choice 200
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250
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‐100
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Note: The odds ratio (y-axis) is a measure of effect size, describing the strength of impact for each operational capability. All
values are percentages. The operational capabilities are shown on the x-axis. To avoid confusion we display the line values for
the aggregate model only
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Figure 3. Impact on odds of choice for each capability
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Table 1. Service delivery system capability definitions and levels
Capability levels
Capability
DCE attribute and definition
Active
engagement
Active customer engagement: this
captures the depth of pre-work
undertaken to understand customer
priority areas and engage with the
customer.
Conducts numerous
customer visits to obtain
feedback, agree on the
best approach and build
relationships.
Rarely contacts the
customer directly and
typically requires minimal
information.
Crossfunctional
coordination
Cross-functional coordination:
leadership capability to coordinate
the effort of individuals within the bid
team and motivate participation at
both country and regional levels.
Draws on a culture of
collaboration to leverage
knowledge and skills
across country and
regional levels.
Rarely collaborates with
stakeholders outside of the
country in which company
resides.
Creative
solutions
Creative business solutions: the
ability to create innovative customer
solutions based on an ability to
acquire, assimilate and use
knowledge.
Understands and uses
detailed customer data to
find novel business
solutions.
Operational
improvement
Continuous improvement in
operations: the ability to make
sustained incremental improvements
in on time and error free delivery,
often involving the standardization
procedures.
Able to design scalable
end-to-end solutions that
are integrated and fast,
and that provide
increased capacity.
Bases improvement
initiatives on irregular, ad
hoc problem solving.
IT
infrastructure
IT infrastructure: the ability to
leverage corporate-wide IT systems
to share information across
products, management services and
locations.
Able to draw on a
common IT infrastructure
that ensures high visibility
across all activities.
Able to draw on standalone IT infrastructure
modules resident within
each country, region and
global HQ.
Professional
delivery
Professional delivery: the ability to
articulate the bid proposal details
(what, why and how the bid delivers
on RFQ requirements) during the bid
pitch.
Able to provide clarity in
presenting and give clear
examples that
demonstrate customer
value.
Provides a more generic
pitch about the bid.
Adequate
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Superior
Offers standard solutions
with only incremental
improvements.
Note: two level items were coded as +1 and -1. The superior capability is coded as +1 and the adequate capability is coded as -1.
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Table 2. DCE results
Commoditized
(Beta Corporation)
Beta
z-value
Beta
z-value
Active engagement (AE)
0.50
6.22
0.46
3.50
Cross-functional coordination (CF)
0.20
2.49
-0.21
-1.60
Creative solutions (CS)
0.59
7.28
0.68
5.06
Operational improvement (OI)
0.67
8.24
0.36
2.81
0.80
7.20
IT infrastructure (IT)
0.14
1.72
-0.25
-1.96
0.37
3.40
Professional delivery (PD)
0.32
4.08
0.18
1.40
0.37
3.44
CF x AE
0.02
0.33
0.20
2.04
-0.05
-0.57
CF x CS
0.03
0.55
0.10
2.00
0.00
0.04
CF x OI
0.05
0.89
0.20
2.05
0.00
0.04
CF x IT
0.14
2.32
0.46
4.55
-0.03
-0.34
CF x PD
0.06
0.91
0.28
2.83
-0.03
-0.40
IT x AE
0.10
1.60
0.29
3.00
0.02
0.21
IT x CS
-0.03
-0.41
0.21
2.15
-0.18
-2.11
IT x OI
0.06
0.95
0.35
3.53
-0.16
-1.85
0.02
0.38
0.20
2.06
-0.08
-1.00
0.20
3.27
0.43
4.23
0.05
0.63
-0.04
-0.69
0.05
0.49
-0.02
-0.23
0.10
1.57
0.12
1.21
0.09
1.10
CS x OI
-0.10
-1.67
-0.01
-0.11
-0.13
-1.56
CS x PD
0.06
1.00
0.19
1.92
0.02
0.19
CI x PD
0.00
-0.01
0.13
1.36
-0.05
-0.57
AE x CS
AE x OI
R(0)2
-2LL
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AE x PD
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IT x PD
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0.31
817
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Interaction effects
0.47
671
z-value
0.43
3.88
0.37
3.42
0.49
4.54
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Main effects
Beta
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Attributes
Customized
(Alpha Corporation)
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Aggregate
0.25
971
Note: Shaded cells represent highly significant results (p<0.05).
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