Accepted Manuscript Title: Leveraging Supply Chain Visibility for Responsiveness: The Moderating Role of Internal Integration Author: Brent D. Williams Joseph Roh Travis Tokar Morgan Swink<ce:footnote id="fn0005"><ce:note-para id="npar0005">Tel.: 817 257 6593.</ce:note-para></ce:footnote><ce:footnote id="fn0010"><ce:note-para id="npar0010">Tel.: 817 257 7151.</ce:note-para></ce:footnote><ce:footnote id="fn0015"><ce:note-para id="npar0015">Tel.: 817 257 7463.</ce:note-para></ce:footnote> PII: S0272-6963(13)00081-8 DOI: http://dx.doi.org/doi:10.1016/j.jom.2013.09.003 Reference: OPEMAN 839 To appear in: OPEMAN Received date: 1-2-2013 Revised date: 15-9-2013 Accepted date: 24-9-2013 Please cite this article as: Williams, B.D., Roh, J., Tokar, T., Swink, M., Leveraging Supply Chain Visibility for Responsiveness: The Moderating Role of Internal Integration, Journal of Operations Management (2013), http://dx.doi.org/10.1016/j.jom.2013.09.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Transcript
Accepted Manuscript
Title: Leveraging Supply Chain Visibility for Responsiveness:The Moderating Role of Internal Integration
Author: Brent D. Williams Joseph Roh Travis Tokar MorganSwink<ce:footnote id="fn0005"><ce:note-paraid="npar0005">Tel.: 817 2576593.</ce:note-para></ce:footnote><ce:footnoteid="fn0010"><ce:note-para id="npar0010">Tel.: 817 2577151.</ce:note-para></ce:footnote><ce:footnoteid="fn0015"><ce:note-para id="npar0015">Tel.: 817 2577463.</ce:note-para></ce:footnote>
Received date: 1-2-2013Revised date: 15-9-2013Accepted date: 24-9-2013
Please cite this article as: Williams, B.D., Roh, J., Tokar, T., Swink,M., Leveraging Supply Chain Visibility for Responsiveness: The ModeratingRole of Internal Integration, Journal of Operations Management (2013),http://dx.doi.org/10.1016/j.jom.2013.09.003
This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.
To effectively respond to the dynamics of today’s marketplace, organizations must be
operationally flexible, in multiple ways. Flexibility has been defined as “the ability to change or
react with little penalty in time, effort, cost or performance” (Upton 1994, p. 73). Prior research
has identified various types of flexibilities, and noted that flexibility enables organizations to
responsively align supply and demand, and several researchers have proposed hierarchical
frameworks relating flexibility types to each other, and to overall responsiveness (Holweg, 2005;
Koste and Malhotra, 1999; Malhotra and Mackelprang, 2012; Reichhart and Holweg, 2007;
Stevenson and Spring, 2007).
Our conceptualization of responsiveness incorporates a blend of flexibility types drawn
from these sources. First, it is important to note that we focus on external flexibilities rather than
internal resource flexibilities. External (aka, externally-driven) flexibilities pertain to operating
responses, rather than to specific resource characteristics such as labor flexibility or machine
flexibility. Hence, in studying external flexibilities, we capture capabilities that reflect the ways
that supply chain managers change their production/delivery quantities and qualities in response
to shifts in customer demands and to changes in supply. Second, responsiveness concepts in the
Overall Supply
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literature typically address tactical flexibilities, i.e., flexibilities that occur at the level of a
business unit (e.g., plant, product supply chain), rather than flexibilities associated with specific
functions or low level operations.
Researchers are not consistent in the flexibility types they associate with supply chain
responsiveness and related concepts. For example, Reichhart and Holweg (2007) define
“product flexibility” as the ability both to introduce new products and to make changes to
existing products, whereas Koste and Malhotra (1999) and a majority of other operations
management researchers make a distinction between “new product flexibility” and “product
modification flexibility”. In addition, researchers like Reichhart and Holweg (2007) and
Stevenson and Spring (2007) address “delivery flexibility,” whereas other researchers ignore this
dimension.
In the interests of consistency and parsimony, we define responsiveness in terms of four
external, tactical, flexibilities: new product flexibility, volume flexibility, variety flexibility, and
product/service modification flexibility. Table 1 briefly describes each flexibility type.
Collectively, these four types of flexibility reflect a supply chain’s overall responsiveness to
changes in demand and supply.
Table 1External flexibilitiesFlexibility type Description
New productDescribes how quickly and efficiently a supply chain can introduce new products. Relies on supply chain partners that design, test, produce, and position product inventories in response to potential market demands.
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Volume
Describes how quickly and efficiently total output can be scaled up or down in response to economic and market shifts. Involves shifting production levels without significant cost, time or performance penalties and adjusting distribution channels without sacrificing economies of scale or customer service levels.
Variety
Describes how quickly and efficiently a supply chain can manage transitions in production and delivery across heterogeneous products. Reflects the breadth of products that a supply chain can handle with its existing resources.
Modification (customization)
Describes how quickly and efficiently a supply chain can alter product and service features to the needs of particular customers. Includes changes made directly to the product and/or the product's delivery service.
3.2 Internal integration
Internal integration is an achieved capability that results from a set of interconnected systems and
processes that facilitate decision-making processes (Schoenherr and Swink, 2012). Internal
integration can be described by the ways that an organization “structures its organizational
practices, procedures and behaviors into collaborative, synchronized and manageable processes”
(Zhao et al., 2011, p.19), mainly involving the integration of data and information systems. Such
processes provide the infrastructure and guidelines for cross-functional information processing
and joint decision making. As a result of internal integration, collaboration occurs between
functional areas within the firm, thereby leading to goal alignment and improved performance
(Schoenherr and Swink, 2012).
3.3 Supply chain visibility
Visibility is a term that is often ill-defined and subsequently misused. We define supply chain
visibility as access to high quality information that describes various factors of demand and
supply. In order for information to be of high quality, it must be accurate, timely, complete, and
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in usable forms. Achieving a high level of supply chain visibility requires the acquisition of
multiple types of high quality supply chain information, which can be classified as either market-
level or partner-level information types. Market-level information describes conditions in
aggregate demand and supply marketplaces, including overall requirements and availabilities at
given prices. Partner-level information types are obtained directly from an organization’s supply
chain partners. Downstream or demand-related, partner-level information types include point-of-
sale (POS) or actual sales data, demand forecasts, customer inventory levels, and customer
promotional plans. Upstream or supply-related, partner-level information types include supplier
inventory levels, supplier lead time/delivery dates, advanced shipment notices, and distribution
network inventory levels.
Researchers argue that organizations need visibility of both demand and supply
characteristics (Barratt and Barratt, 2011). These information types are gathered from the
organization’s customers (demand visibility) and suppliers (supply visibility). Additionally,
organizations gather market-level demand and supply information from sources other than their
partners to obtain visibility to overall market conditions (market visibility). Accordingly, we
conceptualize supply chain visibility as comprised of demand, supply, and market visibilities.
3.4 Hypotheses
Organizations often seek supply chain visibility to cope with the uncertainty of their business
environments. A Delphi study conducted by Lummus et al. (2005) shows that managers strongly
associate accurate and timely visibility of customer demand and inventory information with
greater supply chain flexibility. Participants in the study stated that such visibility is necessary in
order for organizations to design appropriate reactions to change. In addition, Li et al. (2008)
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extend a framework proposed by Christopher et al. (2004), maintaining that demand information
sharing and end-to-end visibility are important enablers of supply chain agility (responsiveness).
They argue that these capabilities enable alertness to opportunities and challenges within the
supply chain and the surrounding environment; such alertness is deemed to be an important
prerequisite to responsiveness capability (Dove, 2005; Holsapple and Jones, 2005).
While little empirical study has been given directly to the effects of visibility on
responsiveness, several studies have explored the effects of inter-organizational information
systems on supply chain flexibility. Golden and Powell (1999) interviewed managers in retail
and manufacturing networks, and found that managers pointed to data sharing via information
systems as an important prerequisite to flexibility across the networks. Gosain et al. (2005)
analyzed survey data to conclude that interconnected information systems were requisite to
higher levels of flexibility. White et al. (2005) explored the role of information systems (e-hubs
and web services) in providing flexibility, suggesting that such systems deepen partnerships
needed to increase flexibility.
As noted by Reichhart and Holweg (2007), the need for responsiveness emanates from
uncertainty. As an organization obtains higher quality information about demand and supply
conditions from customers, suppliers and various other sources, it can anticipate changes and
thus be more responsive. In accordance with the extant literature, we expect that as demand,
supply, or market visibility increases, supply chain responsiveness increases, and vice versa.
Hypothesis 1a. Demand visibility is positively related to supply chain responsiveness.
Hypothesis 1b. Supply visibility is positively related to supply chain responsiveness.
Hypothesis 1c. Market visibility is positively related to supply chain responsiveness.
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Organizations are more successful when they gather, interpret, synthesize and coordinate
information, and then process the information in a structured and logical way in order to improve
decision making (Burns and Wholey, 1993). The capability to process such information is a
result of an organization’s internal integration activities (Schoenherr and Swink, 2012). Cross-
functional processes and systems provide the information processing infrastructure that enables
the firm to absorb, utilize, and interpret information to influence business processes. As a result,
quicker and more effective planning enables the firm to change its operations quickly and
effectively in response to the changing business environment. Researchers have empirically
linked internal integration with supply chain agility (Braunscheidel and Suresh, 2009) and with
flexibility performance (Schoenherr and Swink, 2012). Accordingly, we expect that as an
organization achieves higher levels of internal integration, its supply chain responsiveness
capability increases, and vice versa.
Hypothesis 2. Internal integration is positively related to supply chain responsiveness.
The foregoing literature clearly illustrates the importance of visibility. However, as
discussed earlier, we expect that the degree to which increased supply chain visibility improves
responsiveness depends upon an organization’s information processing capabilities. An
organization must possess the capability to interpret and apply the information to its own
strategic purposes and resource limitations. Absent this capability, visibility is likely to be
exploited only in limited ways, or it may be ignored altogether.
In 1996, Wal-Mart, the world’s largest retailer, launched an internet-based information
portal to provide its suppliers with visibility to sales trends and inventory levels, and since that
time, the integration of supply chain visibility into planning and operations processes has been
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lauded by academics and practitioners as key to effective supply chain management (Lapide
2011). However, in a recent symposium composed of large consumer packaged goods (CPG)
manufacturers, managers indicated that the potential supply chain benefits associated with
increased visibility have yet to be fully leveraged. Participants cited the lack of “enterprise-
wide” business processes (internal information processing capability) that can fully leverage the
information as a primary factor in lack of progress to date (Lapide, 2011).
Catalan and Kotzab (2003) describe an organization’s internal information processing
capability as the ability to read and understand market signals and related information.
Similarly, researchers suggest that information exchanges must be supplemented with further
process coordination and organizational integration in order to achieve greater responsiveness
(Lee et al., 2000). According to OIPT, without the information processing capability that results
from internal integration, supply chain visibility may have a limited effect on supply chain
responsiveness. However, as organizations achieve higher levels of internal integration,
organizational decision makers can develop a shared understanding of the meaning and
implications of information provided by greater supply chain visibility. In doing so, they can
more quickly and effectively capitalize on opportunities and mitigate perceived threats.
Therefore, we posit that an organization’s internal integration serves as a needed complement to
its supply chain visibility. Thus, as an organization gains greater levels of internal integration
capabilities, its supply chain visibility is made more valuable. Hence, internal integration acts as
a moderating force on the relationship between the dimensions of supply chain visibility and
supply chain responsiveness.
Hypothesis 3a. Internal integration positively moderates the relationship of demand
visibility to supply chain responsiveness.
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Hypothesis 3b. Internal integration positively moderates the relationship of supply
visibility to supply chain responsiveness.
Hypothesis 3c. Internal integration positively moderates the relationship of market
visibility to supply chain responsiveness.
4. Research method
4.1. Sample
We test our hypotheses using data collected from the annual Global Survey of Supply Chain
Progress. This cross-sectional survey is an on-going collaborative effort between university
professors and Computer Sciences Corporation (CSC), with contributions by the Council of
Supply Chain Management Professionals (CSCMP) and the Supply Chain Management Review.
This study utilized data collected in the 9th iteration of the survey, administered in 2012.
The survey instrument was developed for a single respondent, with the supply chain
management organization being the unit of analysis. Survey developers kept survey items as
simple as possible to ensure accuracy of responses provided by the single respondent, consistent
with recent approaches for studying inter-organizational phenomena (Flynn et al., 2010; Rai and
Tang, 2010). Items were reviewed by four academic researchers and five practitioners.
Respondents to the survey were supply chain executives embedded in firms located
throughout the world. Survey invitations were sent via e-mail to readers of the Supply Chain
Management Review, clients of CSC, members of CSCMP, and a list of university contacts.
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Three survey invitations were sent over a six-week period. Further, the survey was advertised on
the journal and participating corporate web sites.
Based on the key informant approach, we screened the responses and eliminated
informants whose titles were not directly related to a supply chain function (Wall et al., 2004). A
total of 206 usable responses were collected. Data were obtained from respondents in a wide
variety of firms. Table 2 provides a summary of organization characteristics. Responses were
collected from individuals in firms from more than 18 different industries, including
wholesale/distribution (12.1%), food (11.2%), retail (10.2%), automotive and transportation
(7.8%), and consumer goods (6.8%). Overall, 67.5% of the represented firms are involved in
manufacturing, whereas the remaining 32.5% are in distribution or retail.
Table 2Profile of responding organizationsMetric Frequency Percentage Cumulative
Firm industry roleAutomotive and transportation 16 7.8% 7.8%Chemicals 8 3.9% 11.7%Consumer goods 14 6.8% 18.4%Electronic equipment 4 1.9% 20.4%Machinery and industrial equipment 7 3.4% 23.8%Mining and metals 13 6.3% 30.1%Oil and gas 5 2.4% 32.5%Pharmaceuticals 5 2.4% 35.0%Pulp and paper 3 1.5% 36.4%Publishing and printing 3 1.5% 37.9%Financial 8 3.9% 41.7%Food 23 11.2% 52.9%Government 1 0.5% 53.4%Health care 5 2.4% 55.8%Retail 21 10.2% 66.0%Professional 6 2.9% 68.9%Third party logistics provider 5 2.4% 71.4%Wholesale/distribution 25 12.1% 83.5%
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Other 31 15.0% 98.5%No response 3 1.5% 100.0%
Annual Sales (USD)Less than $250 million 78 37.9% 37.9%$251 - $500 million 24 11.7% 49.5%$501 million - $1 billion 26 12.6% 62.1%$1 billion - $10 billion 47 22.8% 85.0%Greater than $10 billion 31 15.0% 100.0%
A limitation of the method used to invite respondents to participate in the survey is that it
did not allow calculation of a conventional response rate. Therefore, we assess nonresponse
bias, in two different ways. First, we compared return-on-assets of the sampled firms with their
respective industry median values using paired sample t-tests. Our results reveal that there were
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no statistically significant differences (p > .05) between the sample firms and industry median
values, suggesting that a performance bias is not present in our sample. Second, there were no
statistically significant differences in responses to any of the measurement items between early
(first 25%) and late respondents (last 25%), (Armstrong and Overton, 1977). Thus, we find no
cause for concern with regard to nonresponse bias.
4.2. Measures
4.2.1. Variables for hypothesis testing
The dependent variables in our study include new product flexibility (NPRODFLEX), volume
flexibility (VOLFLEX), variety flexibility (VARFLEX), and modification flexibility
(MODFLEX). We measured these four flexibilities by adapting scales from Koste, et al. (2004).
Respondents indicated the extent to which they agreed or disagreed with statements related to
each type of flexibility on a 5-point scale (see Appendix). Because flexibility is a relative term
(Upton, 1994), in each case respondents were asked to evaluate their flexibility relative to that of
their competitors. Collectively, the four measured types of external flexibility reflect a supply
chain’s overall responsiveness to changes in demand and supply (Koste, et al. 2004; Reichhart
and Holweg, 2007). As such, we estimated responsiveness as a second-order factor (RESP)
reflected by these four flexibilities.
Internal integration (INTEG) was measured using a seven item scale adapted from
Schoenherr and Swink (2012). This measure used the same 5-point scale (strongly agree-
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strongly disagree) to capture respondents’ level of agreement with the statements made in each
item (see Appendix).
Two existing studies offer examples of empirical measures of supply chain visibility
(Caridi et al., 2010; Wang and Wei, 2007). However, both focus only on visibility with
suppliers. Our aim was to assess visibility more comprehensively, in accordance with the more
holistic conceptualization discussed earlier. Consequently, we developed a scale by
incorporating inputs from our practitioner partners and from a review of the literature that
discusses conceptual aspects of visibility. These efforts led us to identify measures of 10 critical
elements of visibility (see Table 4). Collectively, the measures span both the upstream-
downstream and market-partner dimensions identified earlier. In addition, we designed the
measures to address the information quality attributes identified in the literature (Barratt and
Barratt, 2011; Barratt and Oke, 2007; Caridi et al., 2010; Wang and Wei, 2007), thus providing a
comprehensive operationalization of the visibility construct.
Table 4Measured elements of supply chain visibilityElement Source
Point of sale/actual sales CustomersDemand forecasts CustomersCustomers' inventory levels CustomersCustomers' promotional plans CustomersMarket-level demand information Various (internal and external)Suppliers' inventory levels SuppliersOrder lead times/delivery data SuppliersAdvance shipment notice SuppliersFinished goods location status SuppliersMarket-level supply information Various (internal and external)
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To measure each element of visibility, the survey first asked respondents whether or not
their major customers (or suppliers, respectively) share a certain type of critical information (for
partner visibility measures), or whether their company gathers the information (for market
visibility measures). If the answer was no, the measure was scored as 0. If the answer was yes,
then the respondent was asked to rate, on a 5-point scale, four attributes describing the quality of
that information, including timeliness, accuracy, completeness, and useful formatting. We
averaged these four scores to arrive at the visibility score for each element. The appendix shows
details for the items used in measuring visibility.
4.2.2. Control variables
We included several other variables in the statistical models used to test the hypotheses,
in order to control for potentially confounding effects. First, we used firm sales (SALES) to
control for organizational size. Some authors argue that smaller organizations are more
responsive than larger organizations by virtue of their flatter and faster decision-making
structures. A contrary argument is that large organizations are more responsive due to the
greater variety and availability of their resources. .
We also control for five different types of information technology integration. In this
study, we conceptualize internal integration as a set of collaborative social processes that are
capable of interpreting, synthesizing and coordinating information across the organization. As
such, information technology integration is not a primary focus in this study, but we do control
for its effect, given that it contributes to an organization’s overall information processing
capability. Technology related controls included measures of the extent of usage of enterprise
The survey required the respondents to answer questions on both the dependent and
independent variables; consequently, common method bias may be a concern. To avoid
common method bias we grouped the independent and dependent variables in different sections
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of the questionnaire (Podsakoff et al., 2003). To evaluate the extent to which common method
bias influences our empirical findings, we used the single method factor approach advocated by
Podsakoff et al. (2003) and implemented by Rosenzweig (2009). We added a single unmeasured
latent method factor whose indicators included all the measurement model constructs’ indicators,
and calculated the amount of each indicator’s variance substantively explained by the
measurement model constructs and by the single method factor. The average indicator variance
explained by the latent method factor is a mere 0.03 (compared to 0.79 for the average indicator
variance explained by the substantive constructs). The ratio of substantive variance to method
variance is about 26:1. In addition, all method factor loadings were not significant. These
results indicate that common method bias is unlikely to be a serious concern for this study.
5. Results
5.1 Hypothesis tests
Our hypotheses were tested using moderated regression analysis. Internal integration is
hypothesized to moderate the relationships between each supply chain visibility type and supply
chain responsiveness. We estimated interaction effects in the moderated regression model by
including cross-product terms as additional predictor variables. Due to multicollinearity
concerns, mean-centered scores were used, and each interaction term is entered separately
(Gopal et al., forthcoming). Moderated regression analysis is the preferred method in this case
(in comparison to sub-group analysis) as it maintains the integrity of the sample data (Zedeck et
al., 1971).
Table 6 shows the moderated regression results. The F-values in all five models are
highly significant (p<0.0005). Model 1 in Table 6 provides the estimated coefficients for control
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variables only. As expected, the usage of execution (EXEC) and analytic (ANA) systems are
positively associated with responsiveness (p<0.05). The industry control variable (INDUS) is
also significant, indicating that respondents from manufacturing organizations tended to rate
themselves higher in responsiveness. The other control variables are not significant, though the
volatility (VOLA) coefficient becomes significantly positive in subsequent models.
Model 2 in Table 6 adds the main effect variables: demand visibility, supply visibility,
market visibility, and internal integration. A non-significant coefficient for each visibility type
provides no support for H1a, H1b, or H1c, indicating that each visibility type has no significant
direct effect on responsiveness. The significant coefficient for internal integration (! = 0.329, t =
4.47) indicates support for H2; internal integration is positively associated with supply chain
responsiveness
Table 6Moderated regression results
Standardized Estimates DV = RESP
Model
1 2 3 4 5
Control Variables
SALES -0.118 -0.137* -0.098 -0.129 -0.140*
CRM 0.045 0.015 -0.005 -0.024 -0.019
SRM -0.103 -0.131 -0.151 -0.147 -0.145
ERP 0.054 0.024 0.041 0.035 0.032
EXEC 0.188* 0.208** 0.213** 0.203** 0.214**
ANA
0.329***0.126 0.118
0.138 0.144
INDUS 0.147* 0.133 0.140* 0.135* 0.138*
B2B/C 0.092 0.107 0.130 0.119 0.119
VOLA 0.116 0.124 0.151* 0.141* 0.135*
Independent Variables
DMNDVIS 0.031 -0.038 0.017 0.024
SUPPVIS 0.070 0.057 0.048 0.061
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MRKTVIS 0.122 0.133 0.127 0.135
INTEG 0.329*** 0.363*** 0.350*** 0.341***
DMNDVIS x INTEG 0.226**
SUPPVIS x INTEG 0.134*
MRKTVIS x INTEG 0.107
F 5.296*** 6.731*** 7.481*** 6.672*** 6.501***
R2 0.216 0.341 0.384 0.357 0.351
Adjusted R2 0.175 0.290 0.333 0.304 0.297
Δ R2 -- 0.125 0.043 0.014 0.007
* p < 0.05; ** p < 0.01; *** p < 0.001
Model 3 adds the interaction term for demand visibility and integration. The significantly
positive interaction coefficient (! = 0.226, t = 3.42) confirms support for H3a and indicates that
internal integration fully moderates the effect of demand visibility on responsiveness. Model 4
adds the interaction term for supply visibility and integration. Again, the significantly positive
interaction coefficient (! = 0.134, t = 2.06) confirms support for H3b and indicates that internal
integration fully moderates the effect of supply visibility on responsiveness. Finally, Model 5
adds the interaction term for market visibility and integration. In this case, the interaction
coefficient (! = 0.107, t = 1.63) is not significant, indicating that integration does not moderate
the effect of market visibility on responsiveness, and thus providing no support for H3c. Table 7
provides a summary of these hypothesis tests.
Table 7Summary of hypothesis tests
Hypothesis Support
... is positively related to supply chain responsivenessH1a Demand visibility Not supportedH1b Supply visibility Not supportedH1c Market visibility Not supported
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H2 Internal integration Supported
Internal integration positively moderates the relationshipof … to supply chain responsiveness
Independent VariablesVIS 0.023 -0.099INTEG 0.318*** 0.110VIS x INTEG 0.441***
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F 5.111*** 6.376*** 10.091***
R2 0.210 0.291 0.416
Adjusted R2 0.168 0.245 0.375
Δ Adjusted R2 -- 0.077 0.130* p < 0.05; ** p < 0.01; *** p < 0.001
Model 3 adds the interaction term. The highly significantly positive interaction
coefficient confirms that internal integration moderates the relationship of visibility to
responsiveness. The adjusted R-squared value for Model 3 is 38 percent, suggesting that the
effects explained in the overall model are substantial.
In addition to the post hoc analysis, we examined other possible sample dependent effects
in our findings. Though in the foregoing analyses we controlled for both industry type
(manufacturing versus retail/logistical services) and market orientation (B2B versus B2C), we
wanted to investigate possible differences in the findings for each of these subsamples.
Repeating the multiple regression analyses for each of these subgroups revealed no substantive
differences in the findings for any of these groups. Thus, our findings appear to be robust to
these potential sampling effects.
6. Discussion
To reduce supply chain uncertainty, managers endeavor to create greater supply chain visibility
by regularly and systematically collecting various types of supply and demand information.
While foregoing researchers have argued that such visibility enables greater responsiveness (e.g.,
Lummus et al., 2005), this hypothesis has not been previously tested empirically. Our findings
suggest that a higher level of supply chain visibility alone is not necessarily associated with
improved responsiveness. Given that information is dispersed across people, functions, and
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geographic regions within supply chain organizations, only with the appropriate level of
organizational information processing capability can organizations effectively utilize supply
chain visibility to increase responsiveness. According to OIPT, an organization must align its
information processing capabilities with its information needs. Hence, it must gather, interpret,
synthesize and coordinate information across the organization (Burns and Wholey, 1993) in
order to improve responsiveness. These information processing capabilities stem from an
organization’s internal integration activities (Schoenherr and Swink, 2012).
Our research can also be viewed as a refinement and extension of supply chain
integration research. Recent studies suggest that external and internal integrative activities create
greater responsiveness in supply chains. For example, Schoenherr and Swink (2012) show that
external integration activities are positively related to flexibility performance outcomes, such as
cash-to-cash cycle, inventory days of supply, and asset turnover. Our study investigates potential
intervening phenomena between external integration and performance outcomes. Specifically,
we examine the relationships of various dimensions of information visibility that likely emerge
from collaborative external integration processes. In other words, we view visibility as an
outcome of external integration. External integration conceptualizations generally include the
notion of information exchange processes; however, they have not captured the outcome of such
information sharing processes in terms of the types and quality of information generated.
Further, we examine responsiveness as a capability, versus as a performance outcome.
Specifically, we conceptualize responsiveness as a set of capabilities that reflect the ways in
which supply chain managers change their production/delivery quantities and qualities in
response to shifts in demand and supply that are likely to lead to performance outcomes such as
those that are examined in Schoenherr and Swink (2012).
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Given this background, external integration can be viewed as a means to achieve
visibility, and once obtained, visibility can be thought of as a resource. Specifically, visibility
may be regarded as an input to an organization that can be processed or transformed to produce
benefit (consistent with both social capital theory and the knowledge based view).
Conceptualizing visibility as a resource further differentiates it from the concept of external
integration, and it adds the specificity needed to support examinations of particular
organizational information issues. Our findings suggest that future studies should examine the
extent to which an enterprise actually uses its potential productive capacity of specific supply
chain information types as a determinant of performance.
Important theoretical implications can be gleaned from this study. The findings strongly
suggest that internal integration fully moderates the effects of supply and demand visibility on
responsiveness. This is consistent with researchers who maintain that internal integration is a
“crucial building block for complete supply chain integration leading to superior firm
performance” (Schoenherr and Swink 2012, p. 100). However, because our study examines
specific types of information, it provides an even stronger argument for OIPT as a way to explain
the effects of integration activities on supply chain performance, responsiveness in particular.
The theory is supported in suggesting that an organization seeking to maximize responsiveness
should invest in developing strong information processing capabilities through internal
integration activities. Such investments should be made prior to, or at least in concert with,
investments in systems and processes aimed at increasing supply chain visibility.
Our findings also have implications for theories of collaboration, which suggest that
shared information is critical to realizing benefits of collaboration between a buyer and supplier
(Min et al. 2005), and where organizations are struggling to achieve the expected benefits of
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collaboration (Nyaga et al. 2010). In addition, the importance of high-quality, shared
information is highlighted in the collaboration literature. However, our findings suggest that the
acquisition of high-quality supply chain information has little value for improved responsiveness
apart from the organization’s information processing capabilities via internal integration
activities. Thus, high levels of internal integration may be required for buyer and suppliers to
achieve the desired benefits of collaborative information sharing activities.
These findings can be further understood through an uncertainty/equivocality framework.
The organization theory literature suggests that organizations collect and process information not
only to reduce uncertainty, but also to reduce equivocality (Daft and Lengel 1986). While
similar to uncertainty, equivocality refers to the ambiguity that may exist around organizational
issues (Daft and Macintosh 1981; Weick 1976). In a supply chain setting, information obtained
through external linkages may have multiple interpretations within the firm. For example, a
sudden increase in demand, sensed from customers’ shared sales data, reduces uncertainty
regarding the customers’ level of sales; however, equivocality surrounding the cause remains.
That is, the demand shock may be interpreted as a result of a promotion, competitive event, or
some other cause, depending on the functional area or individual interpreting the information,
and the response to the demand shock would likely differ depending upon the interpretation.
Thus, even though visibility may reduce uncertainty, equivocality surrounding the information
may still lead to organizational paralysis, the antithesis of responsiveness.
Research indicates that equivocality is related to an organization’s information processing
capabilities (i.e., internal integration) (Daft and Macintosh 1981; Daft and Weick 1984; Kreps
1980; Putnam and Sorenson 1982; Weick 1976). Without proper information processing
capabilities to interpret, synthesize and coordinate information across functional areas within the
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organization, a lack of shared interpretation of information (i.e., equivocality) remains. By
developing information processing capabilities through internal integration efforts, organizations
can reduce both uncertainty and equivocality, allowing the firm to respond more effectively to
information stimuli.
From a practical perspective, it seems that managers are often frustrated by their inability
to effectively utilize the wealth of supply chain information now available to them. As discussed
earlier, the integration of demand signals into planning and operations processes has not
achieved the desired level of supply chain benefits. The upstream utilization of store-level POS
provides a prime example of this phenomenon. In a recent survey of Chief Supply Chain
Officers at manufacturers and retailers, only 19.8% indicated that their supply chains were highly
driven by retailers’ POS; only 3.1% of retailers indicated that their suppliers are utilizing POS
data to drive supply chains at a high level (CSCOinsights, 2012). Consistent with the findings of
our research, these surveyed manufacturers and retailers indicate they have access to this
information, yet they do not have the capabilities to utilize it effectively. Our research suggests
this is partly due to the misalignment between the collected information and the capabilities
needed to effectively process and utilize the information.
Looking forward, developing a strategy where information gathering and information
processing capabilities are aligned is likely to become increasingly important. In recent years,
the variety of information types available to supply chain managers has increased substantially
(Boeri 2013). The expanded use of smartphones and other mobile devices offers supply chain
managers’ new types of supply chain visibility. Businesses can now access information about
the supply chain directly from its consumers. For example, retailers can now use smartphone
applications (apps) to detect when inventory is not available on the retail shelf (i.e., stockout). In
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fact, shoppers can take a picture of a stockout, upload the photo via the retailer or third party app,
and thus notify the retailer of the stockout. This is only one example of emerging supply chain
visibility types, which have the potential to provide even more granular levels of information.
Such technologies are rapidly growing opportunities for businesses to expand their
visibility into factors affecting demand and supply. However, the findings from our research
strongly suggest that if organizations focus only on collecting and analyzing these data, they may
be unable to fully leverage their potential supply chain benefits. For example, retailers seeking
to leverage newly available real-time stockout information must have the information processing
capabilities that enable it to take action. By concurrently developing strategies and processes to
enhance intra-firm coordination, organizations can establish complementary capabilities needed
to leverage emerging information resources.
7. Conclusions and Suggestions for Future Research
The supply chain management literature has recently evolved with regard to supply chain
visibility, defining it as an outcome of information sharing, where the information is deemed to
be accurate, timely, complete, and in usable formats. While this conceptualization is more
thorough, the empirical link between supply chain visibility and responsiveness has remained
unclear (Holcomb et al., 2011). The findings of the current study suggest that internal
integration is the missing link in establishing how supply chain visibility affects supply chain
responsiveness. Simply collecting supply chain information that is accurate, timely, complete,
and usefully formatted does not alone appear to directly influence an organization’s ability to
respond to the changes occurring in the business environment. In our data, a positive association
between supply chain visibility and responsiveness becomes apparent only in the presence of
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high levels of internal integration. In accordance with OIPT, we conclude that internal
integration activities provide information processing capabilities needed to reduce uncertainty
and create a shared understanding of the information that visibility provides. Conversely,
visibility provides high quality information upon which internal integration processes can act,
thereby making such integration efforts more valuable.
The limitations of this study present opportunities for future research. First, we focus on
internal integration as a set of collaborative social processes that are capable of interpreting,
synthesizing and coordinating information across the organization. Such an organizational
information processing capability is engendered by organizational integration mechanisms such
as centralization, cross-functional teams, liaison roles, and social networks. Future research
should also investigate the importance of technology-based integration as a means to increase an
organization’s information processing capability. We included several measures of technology
as controls in our analysis. Follow-on work might investigate specific technology-based
integration mechanisms as complements to visibility, in detail.
Second, in this study we focus on supply chain responsiveness as the outcome of interest.
Future research should also examine the direct and interacting effects of supply chain visibility
and internal integration on dimensions of operational performance, including, cost, quality,
delivery, and asset utilization, for example.
Finally, this study employed relatively high-level operationalizations of both visibility
and internal integration constructs. Future research might assess specific visibility regimes in
organizations. By examining specific types of visibility in detail, researchers may learn more
about how specific types of information must be processed and shared among internal supply
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chain functional groups in order to become more actionable. Such studies are important, given
the rapid growth in technologies that are supporting huge advances in supply chain visibility.
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Appendix: Measurement itemsPlease tell us the extent to which your supply chain can change or adapt its operations with little time, cost, or other penalty. In each case please evaluate your flexibility relative to that of your major competitors (1 = Strongly Disagree to 5 = Strongly Agree)
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New Product Flexibility 1 (α = 0.850) 0.938c
Our start-up cost (in dollars) of introducing new products into our processes is low 0.763a
The time required to develop and introduce new products is extremely low 0.692
The average cost/unit of products is not affected when a new product is introduced into our processes 0.786
Productivity levels are not affected when a new product is introduced into our processes 0.823
Volume Flexibility 1 (α = 0.896) 0.736 c
We can operate efficiently at different levels of output 0.787
We can quickly change the quantities for products we produce or handle 0.843
We can vary aggregate output from one period to the next 0.832
We can easily change the volume (scale) of our processes 0.848
Variety Flexibility 1 (α = 0.915) 0.605 c
We can process a wide variety of products in our facilities 0.860
We can process different products in the same facilities at the same time 0.856
We can vary product combinations from one period to the next 0.900
We can changeover quickly from one product to another 0.805
Modification Flexibility1 (α = 0.882) 0.875 c
We perform product/service modifications quickly 0.747
Product/service modifications are easy to make 0.829
Operating system performance is not affected by the processing of modified products/service 0.823
The average cost/unit of products/service is not affected when modified product are introduced into our processes 0.723
Our products/services are designed to be easily modified 0.767
Internal Integration2 (α = 0.920): The extent to which internal functional teams (e.g., operations, purchasing, logistics, sales, marketing, finance, engineering, information technology) work together to accomplish supply chain planning and execution.
Functional teams are aware of each other’s responsibilities 0.808
Functional teams have a common prioritization of customers in case of supply shortages and how allocations will be made 0.808
Operational and tactical information is regularly exchanged between functional teams 0.856
Planning decisions are based on plans agreed upon by all functional teams 0.843
All functional teams use common product roadmaps and other procedures to guide product launch 0.831
Performance metrics promote rational trade-offs among customer service and operational costs 0.708a = First item in each constructs is a free parameter (no t-test values); b = All factor loadings are significant at p < 0.001; c = second-order factor loadings1Koste et al.(2004); 2 Schoenherr and Swink (2012)
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Please tell us the extent to which decisions makers in your supply chain organization have access to important operational information in the following categories: (1 = Strongly Disagree to 5 = Strongly Agree)
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Our major customers share their point of sales/actual sales information with us (Y/N):
The sales information we receive from our major customers is… (! = 0.840) 0.555 c
…timely 0.986a
…accurate 0.980
…complete (all the information we need) 0.980
…in a useful format 0.965
Our major customers share their demand forecasts with us (Y/N):
The forecast information we receive from our major customers is… (! = 0.768) 0.650 c
…timely 0.965
…accurate 0.942
…complete (all the information we need) 0.969
…in a useful format 0.963
We gather information from various sources to understand overall market level demand information (Y/N):
The market level demand information we gather is… (! = 0.797) 0.665 c
…timely 0.931
…accurate 0.934
…complete (all the information we need) 0.877
…in a useful format 0.899
Our major customers share their inventory level information with us (Y/N):
The customer inventory information is… (! = 0.870) 0.667 c
…timely 0.973
…accurate 0.990
…complete (all the information we need) 0.991
…in a useful format 0.974
Our major suppliers share inventory availability with us (Y/N):
The supplier inventory information is… (! = 0.908) 0.546 c
…timely 0.989
…accurate 0.988
…complete (all the information we need) 0.969
…in a useful format 0.980
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We get information from various sources to understand overall market level supply information (Y/N):
The overall market level supply information is… (! = 0.869) 0.608 c
…timely 0.959
…accurate 0.976
…complete (all the information we need) 0.967
…in a useful format 0.950
Our major customers share information with us regarding their promotional plans (Y/N):
The promotional information we receive from major customers is… (! = 0.811) 0.443 c
…timely 0.960
…accurate 0.976
…complete (all the information we need) 0.979
…in a useful format 0.975
Our major suppliers share information with us about order lead times/delivery dates (Y/N):
The order information we receive from major suppliers is… (! = 0.902) 0.621 c
…timely 0.974
…accurate 0.975
…complete (all the information we need) 0.956
…in a useful format 0.945
Our suppliers provide us with advance shipment notices (Y/N):
The advance shipment information we receive from suppliers is… (! = 0.898) 0.473 c
…timely 0.982
…accurate 0.988
…complete (all the information we need) 0.989
…in a useful format 0.977
Our systems/partners provide us with finished goods locations status in the distribution network (e.g., distribution centers, transportation) (Y/N):
The information we have regarding finished goods locations status in the distribution network (e.g., distribution centers, transportation) is… (! = 0.902) 0.514 c
…timely 0.984
…accurate 0.991
…complete (all the information we need) 0.988
…in a useful format 0.980a = First item in each constructs is a free parameter (no t-test values); b = All factor loadings are significant at p < 0.001; c = second-order factor loadings
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Volatility (α = 0.811): What is the rate of change (volatility) in your business unit’s competitive environment relative to change in other industries? (1 = Very Stable to 5 = Very Volatile)
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The rate at which your customers’ product/service needs change. 0.786a
The rate at which your suppliers’ skills/capabilities change. 0.575The rate at which your competitors’ products/services change. 0.814The rate at which your firm’s products/services change. 0.719
a = First item in each constructs is a free parameter (no t-test values); b = All factor loadings are significant at p < 0.001; c = second-order factor loadings
Analytics Capability: Please tell us about the degree to which data management and analysis techniques are used to improve supply chain strategies and activities. (1 = Strongly Disagree to 5 = Strongly Agree)
We easily combine and integrate information from any data sources for use in our decision making.
Please indicate your level of usage of the following tools: (1 = No Use to 4 = Lead User)Customer Relationship Management (CRM)Supplier Relationship Management (SRM)Enterprise Resource Planning (ERP)Execution Systems (TMS, WMS)
