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INFORMATION TECHNOLOGIES AS ANTECEDENTS OF
DEMAND MANAGEMENT AGILITY AND SUPPLY CHAIN
PERFORMANCE

presented by

PANKAJ SETIA

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INFORMATION TECHNOLOGIES As ANTECEDENTS OF DEMAND
MANAGEMENT AGILITY AND SUPPLY CHAIN PERFORMANCE

By

Pankaj Setia

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Business Information Systems

2008

ABSTRACT

INFORMATION TECHNOLOGIES AS ANTECEDENTS OF DEMAND
MANAGEMENT AGILITY AND SUPPLY CHAIN PERFORMANCE

BY
Pankaj Setia
This research examines the role of information technologies in enhancing a ﬁrm’s
demand management (DM) agility. These impacts are being assessed for four types of IT
applications (internal supply side, external supply side, internal demand side, and external
demand side). Besides the direct impacts of IT applications we also assess the synergistic
impact of corresponding business initiatives. The categorization of technologies and
business initiatives into these four types is based on their functional nature, and their
scope within the overall supply chain. Further, while speed of agility has been examined
in prior research, this research goes a step forward by studying the role of information
technology in enhancing the effectiveness of a ﬁrm’s DM agility. To make it a
comprehensive evaluation, both speed and effectiveness of DM agility are measured as a

combination of three different dimensions— adaptive, entrepreneurial and responsive.

The results indicate complex dynamics for leveraging IT impacts. The study finds that
different types of IT systems have different ways in which they impact DM agility. Also,
the nature of their synergistic interaction with the corresponding business initiative varies
according to the nature of the technology. The research has both academic and
managerial implications. Academically, this is the ﬁrst attempt to establish the empirical
impacts of IT systems on agility within a firm’s demand management process. Also, the

empirical analysis includes effectiveness along with the speed of agility. Finally, we

establish the impacts of agility on supply chain performance. The study also develops
new managerial knowledge related to the impact of information technologies on
responsiveness to changes in customer demand. The knowledge of the differences in the
impacts of different IT systems, for example, will help managers leverage these IT
systems appropriately and hence realize greater value. We present a detailed discussion

of results and their implications.

Copyright by
PANKAJ SETIA
2008

ACKNOWLEDGEMENTS

I would like to thank my advisors and committee members who have been directly
involved in the development of this research. Their insights and experiences were very
helpful at all the stages of my research. I would like to especially thank Dr. V.
Sambamurthy and Dr. D. J. Closs who helped me develop my good ideas into a good
research model. Thanks also to my sister Monika Setia who has been a source of constant
feedback that was great help throughout. Finally, I would like to acknowledge the
contributions by my parents who helped me develop strong educational background and
work ethics that helped me get through this project.

TABLE OF CONTENTS

LIST OF TABLES ............................................................. vii
LIST OF FIGURES ............................................................ viii

CHAPTERIINTRODUCTION 1

CHAPTER 2: LITERATURE REVIEW .................................... 7

Development of resource proﬁles: resource picking versus capability

building ........................................................................ 9

Organizational Capabilities ................................................ 10

Capabilities Building Function ............................................ 13

Paths to Capabilities .................................................. 15

Complementarities ......................................................... 17

IT created Organizational Capabilities ................................. 21

IS capabilities ............................................................. 22

Strategic Capabilities ...................................................... 25

Organization’s Functional and Process Capabilities ................. 28

5149/96! r/Jaz'n Prom: Framework: ...................................... 31

Global Supply Chain Forum ................................... 32

SCOR: Supply Chain Operations Reference ............... 34

Demand management Processes ............................ 36

Demand Management Business Initiatives ........... 38

Demand Management Information Technologies. . . 4O

Demand Management Agility ............................. 42

Adaptive, Radiant, and Entrepreneurial, Agi/igl ............... 45

Two dimensions of DM Agility— speed and effectiveness 47

Supply Chain Performance ............................... 47

CHAPTER 3: RESEARCH FRAlVLEWORK 49

Demand Management Agility ........................... 49

Demand Management Information Technologies ..................... 52

Demand Management Business Initiatives ..................... 54
Complementarities between Use of demand management IT and

initiatives. 59

Controls ..................................................................... 61

CHAPTER 4: RESEARCH IVLETHODS 65

Data Collection .............................................................. 65

Measures .................................................................. 67

Analysis ..................................................................... 77

Regression diagnostics ..................................................... 87

CHAPTER 5: DISCUSSION AND CONCLUSION ..................... 88

Limitations 94

Contributions 96

Managerial Implications 97

REFERENCES ................................................................. 98

vi

LIST OF TABLES

Table: 1: Key studies in business value and capabilities perspective ......................................
Table 2: Studies that elaborated and test the complementarities in organizational phenomenon. .
Table 3: Description of the GSCF Processes ..............................................................

Table 4: Description of SCOR Processes ...................................................................
Table 5: Number of Employees in the sample .............................................................

Table 6: Internal supply side information technologies and initiatives ...................................

 

Table 7: External supply side information technologies and initiatives ................
Table 8: Internal demand side information technologies and initiatives .................................
Table 9: External demand side information technologies and initiatives .................................
Table 10: The events which call for an agile response from the firm ....................................
Table 11: The items used for three types Of agility .........................................................
Table 12: Measures of service performance and business performance .................................
Table 13: Factor analysis for demand management initiatives ............................................
Table 14: Factor analysis for demand management technologies .........................................
Table 15: First order and second order factor analysis for two dimensions of DM agility .............
Table 16: Factor analysis for supply chain performance ..................................................
Table 17: Reliabilities ...........................................................................................

Table 18: 381.8 analysis of the two models for speed, and effectiveness of DM agility .........................

Table 19: Summary of results of Hypothesis Testing ......................................................

vii

14

20

33

34

67

69

7O

71

72

74

75

76

78

79

80

81

83

84

85

LIST OF FIGURES

Figure 1: Two dimensions of technologies and processes critical in demand management
process ...................................................................................................

Figure 2: Demand management initiatives that impact a ﬁrm’s inﬂuence in meeting customer
demand ...................................................................................................

Figure 3: Information Technologies used for demand management ..............................

Figure 4: Research model for demand management and supply chain
performance ..............................................................................................

Figure 5: Determinants of speed of DM agility and its impact on supply chain
performance ..............................................................................................

Figure 6: Determinants of effectiveness of DM agility and its impact on supply chain
performance .............................................................................................

viii

38

39

42

49

63

64

CHAPTER 1: INTRODUCTION

The digitization of supply chains has led to numerous business innovations and
development of new models for managing physical information and ﬁnancial ﬂows
(Bowersox et al. 1995; Rai et al. 2006; Sambamurthy et al. 2003). The introduction of
information technologies often leads to superior performance of supply chains (V ickery
et a1. 2003). These technologies enhance the efﬁciency of work processes by facilitating
superior execution of management processes through digitally-enabled work systems. For
example, in the absence of the technological support, ﬁrms had to either rely on mass
production of high volumes with high standardization or follow job production that led to
high degree of customization but low volumes. However, enabled with its digital
infrastructure, Dell Inc. simultaneously achieves the conﬂicting objectives of mass
production and customization. Dell could simultaneously achieve these conﬂicting
objectives as the customers could personalize their own electronic products that were
assembled only after customer orders had been placed. The use of intemet and advanced
information technologies (IT) helped coordinate personalization of these mass customer
orders before assembly. However, while the impacts of IT systems on fulﬁllment of
customer demand are well documented in the popular press, little is known of the
complex managerial dynamics that help some ﬁrms realize these superior impacts.

The impact of IT is also associated with superior business and service
performance. For example, Dell not only leveraged its technologies for customization and
superior order fulﬁllment but also realized superior operational performance. Dell

reduced its inventory to only six days and has a negative cash conversion cycle for its

ﬁnancial ﬂows. It is thus no surprise that the ﬁrm’s fulﬁllment of customer demand has
gained increasing research attention. The increased interest to study demand fulﬁllment
in supply chains is also due to the relatively little organizational attention to supply
chains in last decade. For example, successful implementation of such initiatives, as
supply chain partnering that are considered vital for business success, has been very
restrictively adopted by only 50% of the respondents (Boddy et al. 1990). In general,
there is a huge gap and hence opportunity to enhance the performance of supply chains.
In a recent survey of North American manufacturers, Deloitte Consulting found that only
2% of the respondents regard their supply chains as world class (Li et al. 2005).

Supply chains have also become critical to performance due to the increase in
competitiveness of business environments and enhanced importance given to satisfying
customer demand (hypercompetition) (D’Aveni 1994; Sambamurthy 2000). New ways of
serving customers, managing production, and communicating with partners have changed
the way various supply chains manage customer demand (Boyer and Olson 2002;
Brynjolfsson and Smith 2000; Kaplan and Sawhney 2000; Lee and Whang 2001).
Efﬁciency in day-to-day operations is no more a sufﬁcient metric for evaluating
performance of a ﬁrm’s supply chain (Lee 2004). It is realized that the ﬁrms now need to
apply modern management principles to develop more advanced capabilities for rapidly
managing customer demand. Thus, recent research has studied various supply chain
capabilities such as absorptive capacity (Malhotra et al. 2005), supplier and customer side
information capabilities (Barua et al. 2004), HT (Banker et al. 2006), process integration
(Rai et al. 2006), and customer and supplier participation (Banker et al. 2006). However,

beyond these capabilities for superior operational performance, competitive performance

of organizations is crucially dependent on their capability to rapidly respond to the
changes in customer demand (Teece et al. 1997). Therefore, in this research, our focus is
on demand management agility which is the capability of the organization to rapidly
respond to changes in customer demand. We study the impact of information
technologies used in demand management processes on the creation of agility in a ﬁrm’s
demand management process.

Agility is the ability of the ﬁrm to respond to the demand changes due to changes
in business conditions (Goldman et al. 1994). Agile performance has been recognized as
vital for the ﬁrm’s overall performance. For example, Sambamurthy et al. (2003) posited
customer, partner, and Operations agility as critical antecedents to overall performance.
Agility is vital for the performance of the ﬁrm’s supply chains and manufacturing
operations (Swafford et al. 2006; Narasimhan et al. 2006). In this research, our focus is
on the agility in a ﬁrm’s demand management process we refer to as the demand
management (DM) agility. While speed of response has gained attention and is the
dominant conceptualization of agile response, the effectiveness of ﬁrm response is an
important consideration as well. Thus, in this research, we focus on both the speed and
effectiveness of DM agility. Further, we use the temporal nature of events and related
ﬁrm response to deﬁne and assess three types for each of the two dimensions. Based on
the timing of the ﬁrm’s response, three types of DM agility deﬁned in this research are
adaptive DM agility, entrepreneurial DM agility, and resilient DM agility. Adaptive DM
agility is related to the ﬁrm’s capability to concurrently realign and adjust to demand

ﬂuctuations, while entrepreneurial DM agility is the ﬁrms’ ability to proactively sense

and respond to changes in future demand. Finally, resilient DM agility is the ability of the
ﬁrm to retrospectively respond to unexpected and unforeseen changes that effect demand.

The successful and modern supply chain organizations are harnessing advanced
information technologies to create superior capabilities (Rai et al. 2006; Banker et al.
2006). These technologies have become necessary as the processes for demand
management, procurement, order fulﬁllment, and delivery have become more complex
and innovative features in products are increasing exponentially (Fisher 1997; J ap and
Mohr 2002; Lee et al. 1997). Information technology has been found to be the most
common factor that leads to effective supply chain practices (Bowersox and Daugherty
1987; 1995) and is a critical component of extended enterprises (Bowersox, and
Daugherty 1995; Bowersox, Closs, and Stank 1999; Edwards, Peter, and Sharman 2001).
The positive effect of information technologies on supply chain performance has further
heightened the attention on the role of IT in supply chains (V ickery et al. 2003; Jayaram
et al.2000). However, researchers have found that realizing superior value out of IT
investments is associated with complex organizational dynamics (Barua and
Mukhopadhyay 2000). Thus, this research explores underlying mechanisms by which
information technologies create agile capabilities in the ﬁrm’s demand management
process.

Interaction of IT with the business initiatives has often been identiﬁed as one of
the key mechanism to leverage IT and create organizational capabilities. The underlying
argument for signiﬁcant interactions is the core theme in the reasoning of the
complementarities theory that has found support in studies of IT value (Barua and

Mukhopadhyay 2000; Sambamurthy et al. 2003; Banker et al. 2006; Tanriverdi 2005;

Tanriverdi 2006). According to these researchers, the value impacts of IT are greater in
the presence of related organizational initiatives. Complementarities have also been
found to be relevant in the supply chains (Jayaram et al. 2000; Rai et al. 2006; Banker et
al. 2006). Complementarities were also found earlier in the manufacturing operations for
ﬁrms that made a transition from assembly line mass production systems to ﬂexible
manufacturing systems (CAD, CAM, etc.) (Milgrom and Roberts 1996; Parthasarthy and
Sethi 1993). While most of this research has looked at the application level, we extend
this research by evaluating complementarities at the process level. The process-level
analysis is recommended as it aids the measurement of IT impacts at the level where they
actually occur (Ray et al. 2005).

The study of complementarities gains special signiﬁcance in today’s business
environment due to its strong link with the managers’ ability to successfully manage their
activity systems. Managing these activity systems often involves a set of
interdependencies amongst the ﬁrms’ activities (Milgrom and Roberts 1990; Levinthal
1997; Porter 1996). In order to take decisions within compressed time ﬁ'ames,
organizational actors internalize these interdependencies as mental models (Siggelkow
2002). By studying complementarities, the goal of this research is to build the knowledge
of the interdependencies related to demand management processes. These activity
systems operate in highly complex business environments and are often managed through
a complex interaction between advanced digital technologies and a set of related
organizational practices. The interaction deﬁned as complementarities, along with the IT

systems, is proposed as an antecedent to agile demand management performance.

We study complementarities as a source of the agile capabilities to propose an
overall model of governance of demand management processes. The model contributes
by building new knowledge to enhance supply chain performance. Also, while the
proposed model is motivated to address the enhanced efﬁciency of demand management
process, it is also likely to aid the governance of IT systems and supply chain related
activity systems.

To summarize, the key research question being addressed in this study is

1. “How do IT and demand management initiatives impact the speed and eﬂectiveness of

DM agility in a ﬁrm ’s demand management process? ”

2. “How do complementarities between IT and demand management initiatives impact

the speed and eﬂectiveness of DM agility in a ﬁrm ’s demand management process? ”

We next review the business value, organizational capability, supply chain
process ﬁameworks, and complementarities literature and based on these develop our
research model. The development of research model is then followed by a brief

introduction of the proposed measurement and analysis.

CHAPTER 2: LITERATURE REVIEW

Resource based view (RBV) has become the key theoretical basis for
contemporary research analyzing impacts of IT on organizational performance. RBV, as
proposed by Penrose (1959), deﬁnes a ﬁrm to be an administrative unit that has the
explicit goal to efﬁciently allocate resources. Building on this early work, Petraf (1993)
and Barney (1991) deﬁne resources as the source of a ﬁrm’s competitive advantage.
These resources may include the ﬁrrn’s tangible and intangible assets, management skills,
organizational processes and routines, and the information and knowledge possessed by
it. Barney (1991) proposed RBV as the alternate to strategic competitive forces model by
Porter (1980). Barney argued that ﬁrm’s resources are a source of competitive advantage
if they possess four attributes - value, rarity, inimitability, and non substitutability. The
argument for long-term competitive advantage of these resources assumes the inelasticity
of their supply. Further, this inelasticity of supply is attributed to three organizational
processes: 1. path dependence - some resources can only be developed over long period
of time, 2. causal ambiguity - no clear knowledge of how to develop these resources and
capabilities, and 3. social complexity - some resources do not have ready market
availability for buying and selling.

Resource based view has been empirically tested for industry and ﬁrm-level
attributes and the comparison of its relative effects has found greater applicability at the
ﬁrm level compared with the industry level (Hansen and Wemerfelt 1989; Cool and
Schendel 1988; McGahan and Porter 1997). Heterogeneity of resources leads to differing
performance even within the same industry since resources across any two ﬁrms are

always unique. This uniqueness is attributed to the differences in managerial

competencies and organizational structures across ﬁrms. Even though there has been
empirical support, critics of RBV have raised issues related to its tautology in deﬁnition,
equiﬁnality (due to which different resource conﬁgurations might lead to same value),
missing treatment of product markets, and limited prescriptive applicability (Priem and
Butler 2001).

The key criticism related to the managerial irrelevance and missing treatment of
product markets stems from the description of ﬁrms’ resources as being limited and
having ﬁxed value over time. According to the critiques, RBV fails to incorporate the
role of entrepreneurial effort that can lead to the development of new resources to match
future demand shiﬁs in product or factor markets (p.31: Priem and Butler 2001). Also,
RBV falls short of deﬁning how the ﬁrms can develop a particular resource position. This
argument is even more signiﬁcant in case of the IS resources since these usually do not
have a direct impact on the ﬁrm’s competitive advantage. Wade and Hulland (2004) cited
this inapplicability of RBV to IS-related phenomenon to develop a category of resources.
Developing on Day’s (1994) framework, they extend the resource-based arguments for
the impact of IT resources to categorize three kind of IS resources: Inside Out (Initiatives
inside the ﬁrm that impact the ﬁrm’s outside performance), Outside In (bring in
knowledge from outside to inside the ﬁrm), and Spanning (organization wide resources).
While various categories of IT resources have been deﬁned, the mechanisms by which IT
resources contribute to organizational performance are less understood. Since IT
resources are primarily indirect contributors to the overall organizational objectives, an

important goal is to study the mechanisms by which these resources contribute to the

overall ﬁrm performance. Two such mechanisms - resource picking and capability
building - have gained attention in this stream of research (Makadok 2001).

Superior ﬁrm performance is oﬁen an outcome of the valuable resource and
capability combinations. Hence, the knowledge of mechanisms to develop these
resources and capability proﬁles has gained emphasis. Resource picking and capability
building are often highlighted as two such mechanisms that lead to the creation of these

proﬁles (Makadok 2001).

Development of resource profiles: Resource picking versus capability building

The resource-picking mechanisms deﬁne the selection of the valuable resources
while capability-building processes relate to the management of these resources
(Makadok 2001). For the former, the superior Ricardian rents accrue to the ﬁrms that are
able to ex ante discern the marginal productivity of a resource in combination with
current stock of ﬁrm resources and acquire them for a price less than this value (Barney
1986). Alternatively, the capability-building perspective, based on the Schumpeterian
view (Schumpeter 1950), proposes that superior rents accrue because of the ﬁrm-speciﬁc
capability-building processes that transform these resources to enhance ﬁrm productivity
(Makadok 2001).

Recently, the capability-building perspective has gained greater traction with
researchers as critical ﬁrm resources are oﬁen not available in open markets. These
imperfect factor markets thus challenge the assumption that valuable resources can be
picked from the open market. Certain resources, as know-how and reputation, have to be

developed in house (Teece 1976, 1980; Dierickx and C001 1989). Also, since all the ﬁrms

have access to same resources, a ﬁrm can gain competitive advantage through its ability
to pick valuable resources. Thus, resource picking assmnes that the ﬁrm has the ability to
discern ex ante the ex post marginal value of the resource to the ﬁrm. For example, the
ﬁrms’ ability to acquire a balanced portfolio of IT resources requires superior capabilities
for picking these at a price less than their value to the ﬁrm (Wheeler 2002; Feeny and
Wilcocks 1998). These arguments indicate that resource-picking itself may be dependent
on the superior managerial capabilities that distinguish the ﬁrms’ ability to pick
resources. Thus resource-picking does not address ﬁrm heterogeneity and does not
address the mechanisms for use of the resources. On the other hand, capability-building
perspective is different from resource picking, and addresses the dynamics in deployment
of resource instead of focusing on their selection. For example, Arnit and Shoemaker
(1993: 35) assert:

“Capabilities, in contrast, refer to a ﬁrm’s capacity to deploy Resources, usually in combination,
using organizational processes, to affect a desired end. They are information-based, tangible or
intangible processes that are ﬁrm-speciﬁc and are developed over time through complex
interactions among the ﬁrm’s Resources.”

Capabilities are very speciﬁc to the resource combination used in their creation
and hence may be particular to a ﬁrm. Since the motivation in this research is to study
these value-creating principles to highlight ﬁrm heterogeneity in the use of IT, the
capability-building perspective is adopted as the guiding framework for this research. We

next elaborate on the dynamics related to the creation of organizational capabilities.

Organizational capabilities
Contemporary research in organizational strategy has deﬁned various concepts that

capture distinctive organizational abilities which are antecedents to superior performance.

10

These abilities have been deﬁned through various notions such as competencies,
capabilities, or routines, and have become sine-quo-non to the organization of modern
day enterprise. The prime impetus for this newer conceptualization of organizing is the
increased complexity in internal operations and a very rapidly changing external
environment. These concepts highlight the evolutionary nature of ﬁrm activities and are
markedly different from the resources that are assumed to be isolated determinants of
value. It is assumed that the value of these resources is determined ex ante. However,
modern organizations realize value only after the resources are acquired. Value-
realization involves deployment of these resources in combination with other resources
and organizational processes. The evolutionary concepts capture different aspects of the
dynamics of this phenomenon. For example, Selznick (1957, 1996) has argued for the
presence of ‘distinctive competencies’ that create a sense of common goal and purpose.
Nelson and Winter (1982) proposed organizational routines for knowledge creation by
combining tacit knowledge through path-dependent learning mechanisms. Similar
arguments about the evolutionary nature of performance had been explored in the concept
of absorptive capacity by Cohen and Levinthal (1990); combinative capabilities by Kogut
and Zander (1992); and most recently, dynamic capabilities by Teece et al. (1997). Based
on Nelson and Winter’s earlier work on administrative and operational routines as self
developing mechanism for organizational grth and performance, Teece et al. proposed
the concept of dynamic capabilities. These were deﬁned as ‘the ﬁrm’s ability to integrate,
build, and reconﬁgure internal and external competencies to address rapidly changing
environments’ (p.516). The proposed evolutionary arguments run counter to the

neo classical assumption. The evolutionary and dynamic perspective refutes the notion of

11

deterministic and explicit organizational knowledge that can be ex ante modeled in the
form of ‘production ﬁrnction’. Similarly, the creation of value before the use of resources
(as proposed by resource picking perspective) is also against the evolutionary view.
Researchers have highlighted the role of complementarities as critical to explaining these
arguments. Foss (1996) analyzes these evolutionary theories and suggests
interdependencies (complementarities) as the key differentiating factor in these theories
that challenged the independent nature of the factors proposed in neo classical view of the
ﬁrm. Nelson and Winter (1982, p. 104) argue “skills, organizations, and technology are
intimately intertwined in one functioning routine and it is difﬁcult to say exactly where
one ends and another begins.” Similarly, Prahalad and Hamel (1990) in their description
of core competencies note the presence of complementarities as “the collective learning
in the organization, especially how to coordinate diverse production skills and integrate
multiple streams of technologies’. The interaction between technology and organizational
elements has also been theoretically framed in the concept of activity systems by
Milgrom and Roberts (1990, 1995). Though the empirical testing of the effects is limited,
it supports the existence of interdependencies. For example, Setia et al. (2006) found
multiple interdependencies of technology with the organizations’ initiatives as the source
of superior value.

We build on an activity system perspective to explore the complementary
processes that lead to the creation of superior capabilities and performance. This research
is motivated to assess the importance of activity systems in realizing the impacts of

information technology on performance. Next, we highlight the role of the

12

complementarities and other factors that contribute to the creation of organizational

capabilities.

Capability building function
Teece et al. (1997) explain that the creation of capabilities is characterized by the
ﬁrm-speciﬁc processes, positions, and paths.

“The competitive advantage of ﬁrms is seen as resting on distinctive processes (ways of
coordinating and combining), shaped by the ﬁrm ’s (specific) asset positions (such as the ﬁrm ’s
portfolio of diﬂicuIt-to-trade knowledge assets and complementary assets), and the evolution
path(s) it has adopted or inherited.”

F irrn’s processes characterize the managerial and organizational paths in which
things are done within the ﬁrm, and positions of the ﬁrm are related to their possession of
current endowments of intellectual property, technology, complementary assets, customer
base, and its external relations with the suppliers and customers. The organizations’
learning mechanism may recursively follow the classical evolutionary mechanism of
variation-selection -retention to build these dynamic capabilities (20110 and Winter 2002;
Helfat and Petrarf 2003; Grant 1996). A key insight from the research studying the
development of capabilities (see Table 1) is that during capability building each ﬁrm uses
different inputs of resources to develop speciﬁc capabilities. Helfat and Petrarf (2003)
deﬁned the tirneline in the building of capabilities as a lifecycle of development. As each
organization develops capabilities, it follows uniquely distinct pathways to combine
resources during the capability-building lifecycle. The events in the lifecycle of a
capability differ from the events in the other organization and hence the resulting

capabilities are path speciﬁc.

13

Table l: Kev studies in business value and capabilities perspective

 

StudL

Key ﬁndings related to capabilities

perspective

Context of Study

 

Teece et al.
(1997)

Firms leverage their current asset positions to
develop and renew superior capabilities that
enable them to maintain competitiveness

Reviews the different
frameworks in strategy and the
role of ﬁrm’s asset positions
and evolutionary and co-
evolutionary paths in the
creation of superior process
capabilities.

 

Complementary resources with other IT related

Review of and abstracts of 648

 

 

 

 

 

 

 

 

Picolli and resources and characteristics, act as barriers to articles from strategy,
Ives (2005) erosion of ﬁrms competitive position management and IS literature
IT resources and capabilities inﬂuence ﬁrm
performance through three signiﬁcant
organizational capabilities - agility, digital Highlights strategic
Sambamurthy, options and entrepreneurial action; and three mechanisms by which IT
Bharadwaj strategic processes- capability building, interacts with key processes,
and Grover entrepreneurial action and co evolutionary and ﬁrm knowledge and
(2003) adoption. relationships.
Survey of 432 ﬁrms in the
Supply chain integration, encompassing the manufacturing and retail
Rai, integration of physical, information and industries selected from a
Patnayakuni ﬁnancial ﬂows between fum and its supply random drawing of attendees
and chain partners, results in signiﬁcant and of the Council of Logistics
Patnayakuni sustained ﬁrm performance especially in Management's (CLM) annual
(2006) operational excellence and revenue growth. conference.
Based on the resource based
Dynamic capabilities are well-identiﬁed view develops the concept of
processes that are idiosyncratic in nature and dynamic capabilities and
Eisenhardt path dependent in formation. These have analyzes the differences in
and Martin signiﬁcant commonalities across ﬁrms and are their characteristics in different
(2000) created through the learning mechanisms. market dynamics.
Exploratory study conducted in
the context of RosettaNet
consortium and used cluster
Studies the complementarities between the Inter- analysis to characterize ﬁve
organizational process mechanisms and partner different supply chain partner
interface directed information systems and their conﬁgurations (collectors,
Malhotra et al. impact on the ﬁrm’s absorptive capacity in the crunchers, connectors, coercers
2005 context of supply chains. and collaborators.)
Analysis of survey, conducted
by Industry Week and Price
Waterhouse Coopers
Studied the impact of three categories of Consulting, of 1077 US.
manufacturing IT systems - resource planning manufacturing plants in the
systems (RPS), Operations management systems standard industrial
(OMS; and electronic data interchange system classiﬁcation (SIC) code 20 to
Banker et al. (EDI) on the ﬁrms HT and customer and 39, and which employed over
2006 supplier partnering capabilities. 100 people.

 

l4

 

Table l (Cont’d)

 

20110 and
Winter (2002)

Proposes a structure for the formation
and evolution of dynamic capabilities
through the co evolution of three
dynamic mechanisms: tacit
accumulation of past experiences,
knowledge articulation, and
knowledge codiﬁcation processes.

Draws from both the behavioral
and cognitive traditions in
organizational learning studies.

 

Makadok

Analyzes two strategic mechanisms of
resource picking and capability
building and their potential in creation

Develops a theoretical analytical
model to analyze the conditions
under which the two mechanisms

(2001) of economic rents. are complements and substitutes.

 

Builds upon the research into
competitive dynamics, the resource
based view, organizational
capabilities, and organizational
learning to explore development of
'dynamic' or 'ﬂexible response
capabilities' in hypercompetitive
markets.

Develops a knowledge based theory of
organizational capability and
highlights the mechanisms by which
the knowledge is integrated within
ﬁrms in order to create these
capabilities

 

 

 

 

Grant (1996)

 

Paths to capabilities

The capability-building processes follow unique and idiosyncratic paths as they
evolve in organizational work. Oﬁen three perspectives are used to explain the nature of
the organizational action that creates these paths - rational actor perspective, situational
control, and emergent perspective (Pfeffer 1982). According to the rational actor
perspective, people and organizations exercise free choice after evaluating alternative
course of actions. Situational control perspective argues that people and organizations
behave in certain ways due to individual external forces. and events. The emergent
perspective argues that the behavior emerges in dynamic interactions between actors’
internal motives and external circumstances. Building on these perspectives, it has been
argued that the impacts of IT on organization are emergent in nature (Markus and Robey

1988). In the context of IT systems, Markus and Robey identiﬁed the ﬁrst two

15

perspectives as technological and organizational imperative respectively. Both the views
are deterministic in nature. The determinism implies that knowing the nature of the
technological and organizational elements one can predict the transformation that will
occur by the induction of technology. However, Markus and Robey, using the third
perspective, argue that the transformation is emergent in nature and hence can not be
predicted. Thus, IT transformation in organization is speciﬁc to the order in which these
events emerge (path speciﬁc).

This path speciﬁcity is inﬂuenced both by the nature of the organization and the
technology, the dynamics of which have been explained as the duality of technology by
Orlikowski (1992). Orlikowski suggests that while the organization changes the form of
the IT artifact, the artifact changes the form of the organization and a new overall system
emerges as a result of the interaction between the two. Further, Orlikowski deﬁned
interpretive ﬂexibility as the degree to which the users of IS are involved in its
constitution during its development or use. The lesser the interpretive ﬂexibility, as is
common with most of the off-shelf supply chain applications bought by the organization,
the greater the signiﬁcance of idiosyncratic individual and organization actions in
adoption of these systems to organizational processes.

Individual appropriation of technology and enactment to work either supports the
existing way of doing things (inertia), applies the IT to augment the way things were
done (application), or substantially alters the way of work (change)
(Orlikowski 2000). While the appropriation mechanisms are at the level of the individual,
they critically impact the organization’s structure of signiﬁcation (meaning), structure of

domination (power), and structures of legitimation (norms) (Orlikowski 2000, Scott

16

2001). Thus the studies of paths by which IT systems are used in the organizations
involve an individual-level analysis to understand the organizational impacts.

Also, researchers have concluded that the scientiﬁc measurement of path-speciﬁc
phenomenon lends itself to an interpretive analysis, and the emergent change
characterized through these paths is essentially measured using the process theories
(Markus and Robey 1988). Due to these reasons, individual level of analysis, and process
theory approach the processes (or paths) by which IS capabilities are created is not
studied in this research. This reduces the functional form in (1) to CH = F CPB (IT, Cm)‘.

We next develop the individual components of the capability-building function

starting with the resources that are complementary to the IT resources.

Complementarities

As noted before, the central notion in the development of capabilities is an
activity system. Helfat and Petraf (2003) elaborated that capability building involves a
team working with a set of inputs that are processed into unique capabilities. Other
researchers have proposed the interdependence between the inputs and deﬁned these
inputs as complementary for the activity system. These complements are deﬁned to have
super additive synergies with each other. F ormalizing the notion, Milgrom and Roberts
(1995, p.181) deﬁne complementarities to exist between two things when “doing more of

one thing increases the returns to doing more of another” (Italics in original). Thus, ﬁrms

 

1 While the paths are excluded from the capability creation function being studied in this research they are
indeed important parts of ﬁrms’ capabilities. The argument that being speciﬁc to the organization the study
of path is not generalizable is also not a strong one as the ﬁndings have been often generalized in the form
of core lessons learned. For example, Clark et al. (1997) in their study of the creation of center of
excellence for superior IS performance, deﬁne these paths in the form of lessons learned that include - use a
“jump” approach, don’t waiver from the vision, and manage employee anxieties - amongst others.

17

possessing these complementary resources not only see an additive value that is the sum
of value due to these individual resources, but they realize incremental returns over and
above this additive value (Wade and Hulland 2004; Tanriverdi 2006). In the presence of a
complementary resource, overall value of the key resource is greater than the sum of the
two (super additive value). However, it is not the value but also the cost function that
may be suppressed due to the presence of the two complementary resources (sub additive
costs) (Tanriverdi 2006). The complementary argmnent is also stated as the increase in
marginal returns to a resource due to the presence of a complementary resource (Amir
2005: 638; Milgrom and Roberts 1995). The complementarities have been found in
various domains of organizational phenomenon and include the interactions of related
human resource practices (Ichinowski et al. 1997), debt and equity (Stenbacka and
Tomback 2002), and plant inspection and supply inspections (Mayer et al. 2004) (also see
Table 2).

Similar to the strategic literature, the notion of complementarities has been found
to be widely applicable in the IS research. After ﬁndings of missing direct impacts of IT
on ﬁrm performance, researchers have proposed that complementarities of IT with related
organizational resources are essential to realize performance impacts (Barua and
Mukhopadhyay 2000). In the initial application of complementarities to modern day
organizations, Milgrom and Roberts (1990, 1995) found that the shift from mass
production to lean manufacturing systems through adoption of computer based systems
for production (as CAD, CAM, CIM et al.) led to a discontinuous change in the frrms’
activity systems. This often resulted in loss of performance as ﬁrms grappled to ﬁt their

existing human resource, production, and marketing strategies in the new production

18

paradigm. The many failures, as that of the General Motors (GM), were later analyzed to
be due to the fact that complementary synergies in the related organizational resources
were not addressed (Milgrom and Roberts 1995). The complementarities have since then
been found between the ﬁrms with multi-business units (Tanriverdi 2006), shared
knowledge and IT (Ray et al. 2005), ﬂexible manufacturing systems, and organizational
strategy and structure (Parthasarthy and Sethi 1993) (see Table 2). Complementary
investments in organizational structure, strategies, and human resource practices have
thus been proposed to be sine-qua-non for realizing the beneﬁts of IT applications (Barua
and Mukhopadhyay 2000; Wade and Hulland 2004; Milgrom and Roberts 1990, 1995)
The underlying theoretical rationale for the presence of the complementarities is
that of synergy. Two resources with superior synergy are able to create super additive
value, which is greater than the sum of the value created by each resource individually.
Two different approaches are evident in the research evaluating these synergies between
the resources. The ﬁrst approach builds on the contingency/moderation perspective and
proposes the interaction between distinct organizational variables (Oh and Pinnsoneault
2007; Donaldson 1996). The second approach studies the complementarities between
whole conﬁgurations instead of the individual elements of these conﬁgurations (Sinha
and Van de Ven 2005; Whittington et al. 1999; Drazin and Van de Ven 1985). While
having a strong logical appeal, the application of second approach is valid only in the
presence of holistic conﬁgurations that may be characterized as ideal types (Sinha and
Van de Van 2005). Since most organizational phenomena are not pure arch types, the

contingency approach has been the preferred choice of the researchers (Banker et al.

19

2006; Rai et al. 2006). Thus, following the general consensus, we model the

complementarities as contingent interactions.

Igble Q Studies mat elaborated grid test the complementarities in organizational

 

 

 

 

 

 

 

phenomenon
Study Findings of complementary effects Context of Study
Proposes the ﬁt between the ﬁrm’s strategy
structure and managerial processes. Develops The shift of ﬁrms manufacturing
on the notion of edgeworth complements and systems from mass production to
Milgrom and Roberts formalizes the supermodularity using lattice modern lean or ﬂexible
(1995) theory. manufacturing
Superior ﬁrm performance results when the Study of the advanced
ﬁrms strategy and choice of organizational manufacturing technologies as
Parthasarthy and structure are congruent with the competencies CAD, CAM, automatic storage
Sethi (1992) and constraints of the technologic_al choices and retrieval systems and CIM.
Flexible automation systems and
different strategy type (quality
leadership, ﬂexibility leadership
or low cost), structure
(mechanistic or organic), shop
ﬂoor personnel skills
(specialized or diversiﬁed),
Empirical test of the differences in ﬁt and design manufacturing workﬂows
hence the complementary effects of the ﬁrms (sequential or parallel), and
Parthasarthy and manufacturing systems with its structure and project teams and workgroups
Sethi (1993) strategy. (rarely used versus often used)..
Complementarities amongst a group of core
activities and processes can account for
emergence of persistent patterns of change Develops on momentum
Milgrom, Quan and even without usual assumptions about theorem as the theoretical basis
Roberts (1991) economies of scale for developing the propositions.
Complementarities link contingencies to
conﬁgurations. Beyond the reductionist one to
one ﬁt, ﬁrms realize greater effectiveness by
Whittington et al. simultaneously synchronizing multiple
(1999) elements in the orggizational domain.

 

Ichinowski, Shaw and
Prennushi (1997)

 

 

Several elements of high performance human
resource practices are mutually complementary

 

Individual and team based
incentives, extensive screening
of new employees, employment
security, job rotation and
ﬂexibility and labor
communication are some of the
HR initiatives evaluated.

 

20

 

Table 2 (Cont’d)

 

Ray, Muhanna and

Complementarities between the shared
knowledge and the technical IT skills, generic
information technologies and level of IT
investments differentiate the performance of

Survey data collected from
matched sample analysis of
respondents from 72 ﬁrms in
health and insurance industry with

 

 

 

 

 

Mayer Nickerson and
Owan (2004)

 

 

efﬁcacy of plant inspection or vice versa
(complementary effects) even if there are
direct spillovers in learning.

 

Barney (2005) fums‘ customer service processes over 100 employees.
Studies the relatedness and
complementarities between multiple units in
a ﬁrm. Use of common IT infrastructure
technologies and IT management practices
(relatedness) leads to sub additive cost
synergies while complementarities between
the two leads to super additive value Analysis of data from 356 multi-
Tarriverdi (2006) synergies. business fortune 1000 ﬁrms.
Studies the impact of misperceptions between Theoretically models the impact
the interconnected activities and ﬁnds that the of bounded rationality of decision
misperceptions for complements are more makers, outdated mental models,
costly than those for interactions amongst and parochial interactive systems
Siggelkow (2002) substitute products. in tightly coupled systems.
Studies introduction of three complementary
product integration strategies - value-added Empirical analysis of data from 83
internal, add-on module and data interface- U.S. vendors of software
and their impact on a ﬁrms ability to integrate development tools with an
their new offering with the new and emerging average of 11 million dollars in
Nambisan (2002) complementary products. sales and 41 employees.
Theoretical model followed by an
empirical analysis of ﬁnancial
data for the eleven years from
1982-1992 on 3,119 publicly
traded manufacturing and
telecommunication ﬁrms in
Stenbacka and Studies the complementarities in the debt and Standard and Poor's
Tombak (2002) new equity instruments of ﬁnancing COMPUSTAT database.
Fins that 1. When the buyer cannot credibly
commit to the intensity of his supply
inspection, supply inspections and buffer
inventories are complements of each other,
but plant inspections are substitutes for these.
2. Supply inspections can increase the Qualitative and quantitative

analysis of 122 supply contracts
from a large biotechnology ﬁrm in
the San Francisco Bay area.

 

IT created organizational capabilities

The research in the information systems domain has identiﬁed IT as one of the

key resource input in the capability-building processes of the ﬁrm. These created

capabilities are the source of superior competitive advantage for the ﬁrm since they help

21

 

the ﬁrm acquire unique and valuable resource conﬁgurations for both the short and long
term (Eishehardt and Martin 2000).

Our review of the literature is of the studies that use information technologies as
an input to the capability-building process. The resulting capabilities are usually very
different in nature. The literature on the capabilities to manage these information
technologies suggests three different output capabilities that are created using the above
proposed function (see equation 1), with IT as the key input resource (Rai et al. 2006;
Banker et al. 2006; Sambamurthy et al. 2003; Feeny and Wilcocks 1998; Weill et al.
2002; Clemons and Row 1991). Speciﬁcally, IS capabilities, functional (supply chain)
and process capabilities, and strategic capabilities are the three output capabilities that we
now elaborate upon. The key distinction between these capabilities is the organizational
domain to which the capabilities contribute in terms of resource development. Further,
each capability differs from the other in terms of input paths, processes, and
complementary resources that lead to its creation. We next review the studies and

highlight these differences in these capability functions.

IS capabilities

IS capabilities are deﬁned as the ﬁrms’ ability to mobilize and deploy IT-based
resources in combination or co-present with other resources and capabilities (Bharadwaj
2000). These IS capabilities help in superior management of IS activities and may include
system design and software development, management of outsourced software
development projects, planning of information assets, managing system implementation

projects, and management of portfolio of IT assets (Zmud 1984; Feeny and Wilcocks

22

1998; Levina and Ross 2003; Ross et al. 1996). The notion of IS capabilities has gained
increased emphasis in recent research studying business value of information
technologies. This is primarily due to ﬁndings of missing impacts of IT on ﬁrm
performance (Brynjolfsson and Hitt 1993). Dubbed the productivity paradox, the
paradigm of missing impacts of IT has led the researchers to do an in-depth examination
of the IS capabilities in the organizations. Firms with higher IS capability were found to
outperform the ﬁrms in the control sample over many of the cost and proﬁt related
measures (Bharadwaj 2000). The capability-building function deﬁned above
differentiates IS capabilities from the IT assets. 18 capabilities are superior abilities that
help in the management of IT assets. We next review the literature that has studied the
creation of IS capabilities.

The research studying the Information system capabilities has been largely
focused on identiﬁcation of IS capabilities that would inﬂuence the superior business
performance. For example, Feeny and Wilcocks, building on the earlier work (most
notably on CIO’s assessment of IS capabilities (Earl and F eeny 1994; Feeny et al. 1992),
IT imperatives in organization (Rockart et al. 1996), and the role of in-house IS function
on outsourcing (Lacity et al. 1995)), describe nine core IS capabilities that encompass the
three domains of business and IT vision, design of IT architecture, and delivery of IS
services. These nine IS capabilities: 1.IS/IT leadership, 2. business systems thinking, 3.
relationship building, 4. architecture planning, 5. making technology work, 6. informed
buying, 7. contract facilitation, 8. contract monitoring, and 9. vendor development, span
the strategy and enactment initiatives in the policy design, application, and delivery of the

IS systems. Bharadwaj et al. (1998) conceptualized IS capability as the second-order

23

construct that is reﬂected in the six abilities of a ﬁrm measured as ﬁrst order factors — IT
business partnerships (IBP), external IT linkages (BIT), business IT strategic thinking
(BIT), IT business process integration (BPI), IT Management (ITM), and IT
Inﬁastructure (INF). Keen (1991) has associated two IS capabilities with the IS
infrastructure - that of reach and range. Byrd (2001) deﬁned IS infrastructure ﬂexibility
as the capability for connectivity, compatibility, and modularity of IT applications
possessed by the ﬁrms. Clark et al. (1997), in their study at Bell Atlantic, looked at the
change-ready capabilities to deliver IT based products, services, and business
applications with short development cycle times. They found that these IS capabilities
were ably supported by the superior design of the organizational structure, groupings of
people including their roles, relationships and task assignments, and incentives and
reward systems based on client evaluations (Clark et a1. 1997). Others researchers have
deﬁned the hierarchy of IS capabilities in the order of increased architecture maturity
(Ross 2003). These activities range from the application-focused silo capabilities to
enterprise wide IT architectures, followed by data and process centric IT architectures,
and ﬁnally modular architectures to support global standards with local differences.
Recent research has focused attention on the development of capabilities to
manage the portfolio of IT investments. Initially proposed by Macfarlan (1981), portfolio
management has most recently been espoused by Weill and Aral (2006). They deﬁne four
categories of IT investments: inﬁastructural, informational, transactional, and strategic.
Weill and Aral (2006) call for organizations to manage the portfolio of IT
applications through appropriate categorization. These categories of technologies may

then be assessed differently. Weill and Aral propose to use discounted cash ﬂow

24

methodology to assess their impacts. Likewise, organizations have started initiatives such
as project management ofﬁce whereby they develop various methodologies to better
manage IT projects. However, not much research has looked at the idiosyncratic paths
that impact these project management and portfolio management capabilities. Also, more
organized works need to be done in highlighting the antecedents and elaborating on the
ﬁt in the nomological network of these IS capabilities. In this research we differentiate IS
capabilities from strategic and functional and process capabilities, and our focus is on the

application rather than the development of these IS capabilities.

Strategic capabilities

The role of IT in creation of strategic capabilities follows a business paradigm
where proprietary information technologies were a source of competitive advantage for
the ﬁrms. Due to their rare, inimitable, non-substitutable, and valuable characteristics,
information systems as American Airline’s SABRE, (Cash and Konsynski 1985; Hopper
1990) and American Hospital Supply’s ASAP (Vitale 1990) by themselves were sources
of superior competitive advantage. However, with the ubiquitous nature of information
systems, IT resources are no more rare, inimitable, or non-substitutable. Researchers have
thus highlighted the need to focus on the capabilities perspectives to study assimilation in
the organizational processes of IT in combination with complementary resources and

through idiosyncratic paths (Clemons and Row 1991).

One stream of research has studied the role of IT in strategic capability
development and it has used the transaction cost theory as its basis. According to the

studies in this stream of research, the governance mechanisms — market based or internal

25

to ﬁrm - entail different transaction costs (Coase 1937; Williamson 1975). IT systems
help ﬁrms decrease these transaction costs (Gurbaxani and Whang 1991). Three IT
effects are especially pertinent for the decrease in transaction costs: 1. Electronic
communication effects -— reduced communication costs and increased reach (time and
distance), 2.Electronic brokerage effects —decreased cost of transactions and increased
number and quality of alternatives, 3. Electronic (process) integration effects — increased
degree of interdependence between participants in sequential business processes. Zaheer
and Venkataraman (1994) applied this transaction cost analysis to ﬁnd empirically-strong
support for the impact of asset speciﬁcity and trust on the degree of electronic integration
(deﬁned as a form of quasi vertical integration) between partners. The impact of
reciprocal investments, while positive, was found to be only weakly signiﬁcant.

Besides the transaction costs approach, strategic capabilities have also been
studied using the value chain perspective as well. Porter and Miller (1985) gave the
guidelines for applying the value chain perspective to development of strategic
capabilities from the information systems by adding value to a ﬁrm’s value chain
offerings. Lindsey et al. (1990) empirically studied the role of TELCOT system at Plains
Cotton Cooperative Association (PCCA) to strategically enhance the value of ﬁrm’s
offerings. The system helped PCCA to replace the phone as a means to seek bids for its
cotton. The electronic market, so established, helped move PCCA from being a merchant
to a broker. The increase of information content to its products helped it to move up the
value chain and offered a great strategic capability to PCCA.

Similarly, Beath and Ives (1986) developed a framework to classify the various

information systems and their strategic advantage to the ﬁrms’ pricing processes. These

26

strategic capabilities were proposed to be created due to the timeliness, content, and
format of the information that were impacted by the information systems technology.

Finally, researchers have studied organizational processes that lead to the creation
of these capabilities. Clemons and Row (1991), building on Teece’s (1986) work on
economics of innovation, deﬁned the creation of these strategic capabilities to be due to
the combination with the complementary resources. While IT was proposed to lower the
cost of vertical and horizontal ﬁrm transactions, it is only in combination with the
complementary resources that affect the value of the coordinated resource and occurs
through economies of scale and scope. Equating ﬂexibility with real options, Tallon and
Kraemer (2003) found signiﬁcant impact of IT ﬂexibility, a lower-level capability, on
creation of higher order strategic ﬂexibility. Similarly, both IT and strategic ﬂexibility
were found to be the lower—order capabilities that lead to the creation of higher-order
capability of strategic alignment (deﬁned as ﬁt between the information systems and
business strategy).

Allen and Boynton (1991) approached the strategic role of information systems
from the perspective of efficiency and ﬂexibility and deﬁned the development of two
types of capabilities -— low road and high road. The former espouses a more diffuse role
of IS in the organization while the latter is associated with building a more central and
independent IS architecture. IT has also been found to be an essential ingredient to
creation of pre-empting capabilities (Kettinger and Grover 1994), enhancing stickiness by
building greater switching costs (Bakos 1991; Feeny 1988), and developing ﬂexibility

(Kettinger and Grover 1994) and response capabilities (Feeny and Ives 1990).

27

Recent research has highlighted the role of IT resources and capabilities in
creating lower-order digital options and higher-order agile capabilities (Sambamurthy et
al. 2003). Based on the entrepreneurial processes and options perspective, Sambamurthy
et al. suggest that IT systems, along with the complementary processes, have superior
strategic impacts . Superior managerial and entrepreneurial processes are suggested to be
responsible for the ﬁrm realizing the value by appropriately selecting and performing
these strategic actions. Also, Weill et a1. (2002) studied the impact of lower-order IT
infrastructure capability demand on strategic agility. The role of IT in creating other
strategic capabilities has also been studied for environmental scanning and market
responsiveness (Weill and Vitale 2001), fulﬁlling information requirements (Roberts and
Wood 2002), and ﬂexibility (Monteiro and Macdonald 1996).

While strategic capabilities have been well researched using the various
perspectives elaborated above, information systems themselves are no more restricted to
the strategic realm and are an important part of the other organizational activity systems.
Recent research has thus focused on the contributions of IT to various functional
departments and processes. In line with this increasing trend, the focus in this research is

to study the creation of process capabilities.

Organization’s functional and business process capabilities

In the literature, the third type of capability created by the IT resources is related
to a speciﬁc functional area or a business process. These capabilities are speciﬁc to the
context in which the IT applications are implemented. Similar to the IT systems, the

various complementary factors, paths, and processes are also idiosyncratic to this context.

28

The notable work in the area of functional and business process capabilities has looked at
the customer service processes, new product development, and supply chain capabilities
(Ray et al. 2005, Barua et a1. 2004, Rai et al. 2006; Pavlou and Sawy 2006).

For example, Pavlou and Sawy (2006) studied the role of IT in creation of
competitive advantage in new product development (N PD). They studied IT—leveraging
competencies in NPD due to the effective use of process/resource management,
knowledge management, and cooperative work systems. These IT-leverage competencies
were found to impact reconﬁgurability (measured as absorptive capacity, market
orientation, coordination capability, and collective mind). Reconﬁgurability is deﬁned by
them as an NPD capability that leads to the NPD competencies which are customer-
related, technical, and managerial in nature.

In the domain of supply chains, Rai et al. (2006) studied the role of integration of
ﬁrm’s ﬁnancial, physical, and information ﬂows with that of the partners. They found
that the integration of data consistency and cross functional application integration led to
the creation of the functional capability — supply chain process integration, that is
determined by the extent of ﬁnancial, physical, and information integration of the ﬁrm
with its partners. After controlling for consumer demand predictability and ﬁrm size, this
supply chain capability was found to lead to superior operational excellence and revenue
growth. Similarly, Banker et al. (2006) studied two capabilities in the ﬁrm’s
manufacturing operations. In their longitudinal study, they found the impact of three
categories of information systems - resource planning systems, operations management
systems, and EDI - on Just-in-time (JIT) and customer and supplier partnering

capabilities.

29

Supply chain absorptive capability is another capability that has been studied in
the ﬁrms’ supply chains. Malhotra et al. (2005) found the positive impact of partner
interface directed information systems in creating absorptive capabilities in ﬁrms’ supply
chain. The study, conducted in the Rosettanet consortium, found that these information
systems implemented along with the inter-organizational process mechanisms lead to
enhanced capability of the organization to absorb knowledge due to greater
extemalization, internalization, socialization, and routinization. Similarly, Barua et al.
(2004) examined the customer and supplier side digitization of supply chains and
elaborated on their role in the creation of organizational information capabilities. Mass
customization is another capability that has been studied in the context of frrms’ supply
chains (Kotha 1995; Pine 1993)

These process level capabilities assess the frrms’ ability to act rapidly and
effectively and enhance process performance (Teece et al. 1997). Agility of the
organization has more recently been identiﬁed as one such capability that helps ﬁrm to
sense and respond to changed business conditions. In this research, our focus is on this
capability that helps the ﬁrm to respond with speed and effectiveness to the changes in
customer demand.

To summarize, while IS capabilities focus upon the management of various tasks
performed by IS professionals, strategic capabilities are rooted in the resource based,
Schumpeterian, or competitive analysis views (Porter 1980, Barney 1991, Schumpeter
1934). On the other hand, functional and process capabilities address the IT impact from
the perspective of an activity system. Thus, each functional area or process has an

interlinked set of activities that need to be synchronized with the information

30

technologies for the creation of superior performance. Often these activities are organized
within a function or a business process. Various functions and processes that have gained
attention of IS researchers in the recent past include customer service processes (Ray et
al. 2005) and new product development processes (Pavlou and Sawy 2006). In this
research, we study how the use of IT enhances the ﬁrm’s ability to respond to customer

demand changes.

Supply Chain Processes

The supply chain organization encompasses the management of the set of
activities of the ﬁrm that include logistics (Simchi-Levi, Kaminsky, and Simchi-Levi
2000), procurement (Moncska, Trent and Handﬁeld 1998) or operations management,
and the interface of these activities. Various process frameworks are deﬁned to
comprehensively envisage the set of supply chain activities (Lambert et al. 2005;

Bowersox et al. 2006; Srivastava et al. 1999).

Supply chain process frameworks

The identiﬁcation and deﬁnition of the processes in these ﬁ'ameworks is closely
linked to the deﬁnition of the supply chains adopted by them. These deﬁnitions of the
supply chain systems differ in terms of the scope — the extent to which it supports the
organization’s corporate strategy; intra and inter company connectedness — the degree of
formal and informal contact amongst employees across the departments and with the

supply chain partners; or the source of value addition - either through reduction in

31

operating cots, reduction in working capital, increase in asset efﬁciency, or by increasing
revenue (Lambert et al. 2005).

For example, while the Global Supply Chain Framework (GSCF) is strategic in
nature and espouses relationship management and cross functional integration to enhance
the overall EVA, the Supply Chain Operations Reference (SCOR) framework is more
transactional in nature and aims to minimize cost and maximize asset utilization (see
Lambert et al. 2005 for more detailed comparison). This distinction in these frameworks
is reﬂected in the nature of the identiﬁed processes and sub processes. We included the
three frameworks for supply chain processes proposed by GSCF, and SCOR. The
description of the process is similarly also given in Bowersox et al. (2006). Besides these,
two other frameworks were developed by Srivastava et al. (1999) and Melnyk et al.
(2000), but as pointed by Lambert et al. (2005), both of these do not have enough

description of the processes and hence are not considered further in the study.
GSCF: Global Supply Chain Forum

Global Supply Chain Forum was started in 1994 by executives from a group of
multi-national companies who developed a deﬁnition of supply chain management.
According to GSCF, supply chain management is "the integration of key business
processes from end user through original suppliers that provides products, services, and
information that add value for customers and other stakeholders" (Lambert, Cooper, and
Pagh 1998, p. 1). Three primary elements are deﬁned for the implementation of these into
the supply chain organizations: the supply chain network structure, the supply chain

business processes, and the management components. The supply chain network structure

32

is comprised of the member ﬁrms with which key processes are to be linked. The GSCF

proposes 8 different business processes (see Table 3).

Table 3: Description of the GSCF Processes

 

 

Customer Relationship Management - provides structure for development and
maintenance of relationships with customers (Croxton et al. 2001).

 

 

Customer Service Management - helps ﬁrms to develop a single point of contact for
providing information to customers and for administering the product service
agreements (Bolumole, Knemeyer, and Lambert 2003).

 

 

Demand Management - provides supply chains with structure for managing customer
requirement and includes reduction of demand variability and increase of supply chain
ﬂexibility (Croxton et al. 2002).

 

 

Order Fulﬁllment - develops a structure for fulﬁlling customer requirements and
includes network design to meet customer requests at minimum total delivered cost
(Croxton 2003).

 

 

Manufacturing Flow Management - activities necessary to obtain, implement and
manage movement of products through the plants in supply chain (Goldsby and Garcia-
Dastugue 2003).

 

 

Supplier Relationship Management - helps deﬁne activities to develop and maintain
relationships with suppliers (Croxton et al. 2001).

 

 

Product Development and Commercialization - deﬁnes activities for the joint
development of new products with the active participation of suppliers and customers
(Rogers, Lambert, and Knemeyer 2004).

 

 

Returns Management - deﬁnes the management of activities related to reverse logistics
including gate keeping, and avoidance (Rogrs et al. 2002).

 

 

 

Each of these processes is further divided into a set of strategic sub-processes.
These sub processes -provide more details for managing the processes and specify a set

of operational sub-processes for implementation of each. These sub-processes are at the

33

level of speciﬁc activities that need to be undertaken by the ﬁrm. The GSCF framework
also includes the management components - planning and control, work structure,
organization structure, product ﬂow facility structure, information ﬂow, management
methods, power and leadership structure, risk and reward structure, and culture and

attitude - that support the processes (Cooper, Lambert, and Pagh 1997).

SCOR: Supply Chain Operations Reference

The SCOR framework was developed by the Supply-Chain Council (SCC), a
nonproﬁt organization that was founded by Pittiglio, Rabin, Todd, and McGrath (PRTM),
a consulting company, and AMR Research in 1996. Initially, SCOR included four
business processes: plan, source, make, and deliver, and added Returns, as the ﬁfth

process in 2001. These ﬁve processes deﬁned by SCOR are as described in Table 4.

Table 4: Description of SCOR Processes

 

 

Plan - development of a roadmap to match demand with supply. The plan speciﬁes the
course of action for meeting sourcing, production, and delivery requirements.

 

 

Source - activities that are related to the procurement of goods and services to meet
planned and actual demand.

 

 

* Make - activities that help produce goods and services to meet planned or actual
demand.

 

 

* Deliver - provides ﬁnished goods and services to meet planned or actual demand,
typically including order management, transportation management, and distribution
management.

 

 

 

* Return - activities related with the returning or receiving returned products.

 

 

34

SCOR deﬁnes four level of details for each of these processes (Bolstorff and
Rosenbaum 2003; Supply-Chain Council 2003). At Level one is deﬁned the number of
supply chains as well as metrics to be used. Planning and execution processes in material
ﬂow are deﬁned at level two. Inputs, outputs, and ﬂow of transactional element are
deﬁned at level three and level four deﬁnes the implementation details of these supply

chain processes.

According to the SCOR model, each process is associated with three components:
business process reengineering, benchmarking, and analysis of best practices (Supply-
Chain Council 2003, p. 1). The three components help the ﬁrms transform their supply
chain processes to the next stage. Business process reengineering (BPR) helps deﬁne the
current state of process and this forms the basis for transformation of this process.
Benchmarking helps develop the metrics of performance for the proposed transformation,
and the best practices analysis studies and identiﬁes management practices and software
solutions of the successful performers and helps the ﬁrm to match them in the

transformation.

Another process model by Bowersox, Closs, and Stank (1999) (that was further
developed by Melnyk, Stank, and Closs (2000)) proposes eight supply chain processes -
Demand Planning Responsiveness, Customer Relationship Collaboration,
Product/Service Development Launch, Manufacturing Customization, Life Cycle
Support, Order F ulﬁllment/Service Delivery, Supplier Relationship Collaboration, and

Reverse Logistics.

35

The processes are not all executed in parallel and often can be temporally ordered.
This is because of the linearity nature of products and services conversion from raw
material stage to consumption phase. Shapiro et al. (1992) elaborated this linearity in
their description of the order ﬂow throughout the organization. Thus, as summarized by
the SCOR framework, the organizations start with the planning phase. It is at this phase
that the ﬁrm interacts with its customers. Often, given less emphasis in the contemporary
research although repeatedly emphasized in the theoretical works, the demand planning
process is the context of study for this present research. The process is highlighted to be
critical in all the three frameworks.

Also, in our analysis we view these processes from the focal ﬁrms’ perspective.
The alternate is to study processes from the point of origin to the point of consumption.
This latter view does not recognize the ﬁrm boundary to be delimiting and views the
overall supply chain network as the level of analysis. However, since our focus is on the
ﬁrm level heterogeneity and capability-building processes, we adopt the ﬁrm level rather
network view and study the supply chain processes at the focal ﬁrm.
Demand management process

The management of customer demand in an organization poses extreme
challenges. These challenges mostly arise because demand management entails
coordination and collaboration between different entities both inside and outside the ﬁrm.
The interface of multiple inﬂuences within supply chain and marketing or production
often creates sc0pe for errors in meeting customer demand, due to lack of
communication. Lee (2001) deﬁnes an insightful example to elucidate the complexities

involved in activities related to demand management process. A few years ago, Hewlett-

36

Packard (HP) lost the sales of its Pavilion computer on Christmas as supply chain
department did not realize that the marketing department has launched a promotional
scheme to compete with offerings from its competitors - Compaq and Packard Bell. In
this case, the lack of adequate communication between the supply chain and marketing
departments led to lack of inventories and a big loss of demand for HP. Similar to the
adverse impacts of lack of communication, the demand management may falter due to the
lack of initiatives to align the production strategy of ﬁrm with that of its suppliers and
share information within the ﬁrm. Similarly, Dell uses its intemet based communication
and collaborative work practices to coordinate with both the customers and suppliers to
match the demand with the supply.

The organizations that are best able to counter the demand related challenges rely
on both superior information technologies and business initiatives to coordinate and align
demand with the supply. Further, they also use these technologies and initiatives in their
external interaction. We categorize the use of these information technologies and
initiatives across the two dimensions based on the following criterion: 1. Are these
external to the ﬁrm or internal to it? 2. Are these related to the demand side (customer or
marketing related) or supply side (supply chain, production related)? (see Figure 1). Next,
we elaborate these four quadrants for both the business initiatives and information

technologies.

37

Figgre 1: Two dimensions of technologies and processes critical in demand management
process

 

 

 

 

 

" “scope of “W
Internal-m ’ 7 External
Functionality Supply Chain Supplier
I Coordination Information
. Sharing
IDemand Side Demand POS Data
Management and Collection
Marketing

 

 

 

 

 

 

Demand Management Business Initiatives
Based on these two dimensions, we identify the four key demand-management

initiatives (Figure 2):

1. Internal supply side initiatives - These initiatives include the activities related to
internal coordination of supply. An example of these initiatives is integration of
information related to production schedules, sourcing plans, route plans, and proﬁt
margins in assessing the planning and coordination of demand management activities.

Similarly, incorporating changes in supply chain structures, such as inclusion of new

38

suppliers or plan capacities, as part of one’s demand planning initiatives that are
important for appropriate responsiveness from the ﬁrm is an example of internal
supply side initiative. Similar to the acquisition of information, it is also important to
share and coordinate information related to demand across the various function.
Frequency of sales and operations planning (S&OP) done as part of joint planning
between operations and marketing is another initiative that helps enhance the
coordination efforts between the ﬁrms (Crum and Palmatier 2004, Lapide 2007).
Finally, frequent sharing of demand-related changes with production, sourcing,

logistics, and ﬁnance helps to enhance the coordination across functions.

Figpre 2: Demand management initiatives that impact a ﬁrm’s inﬂuence in meeting
customer demand

 

Demand Management Business Initiatives

 

 

 

 

External External Internal Internal
Supply Demand Side Supply Side Demand Side
Side Initiatives Initiatives Initiatives j
Initiatives

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2. External supply side initiatives — These activities are related to sharing information
and planning with external suppliers. Given the key role played by the external
suppliers in production of goods and services, information sharing with them is a
critical aspect of a ﬁrm’s ability to manage customer demand (Malhotra et al. 2005,

Lee et al. 1997). The ﬁrms may share various types of information with suppliers,

39

including demand shifts, market demand changes, details of its upcoming products
and services, and planned production outlays (Malhotra et al. 2005).

. Internal demand side initiatives — These initiatives include the internal marketing
related activities. Frequency with which a ﬁrm is able to integrate the information
from its marketing department that aids demand planning is a critical aspect of
creating demand projections and plans. Aiding the demand management related
technologies is the information about the related marketing instruments such as
promotions, special events, new outlets (Lee 2001), or marketing events, or
information about competitor’s activities, and newer substitutes that is often not

revealed from the statistical algorithms used for planning and forecasting.

. External demand side initiatives — These activities are related to the external customer

related initiatives. These initiatives are primarily based on the information obtained
from analyzing the data from customers and planning of demand based on this data.
Some examples include automatic replenishment initiatives including vendor
managed inventory (V MI), data collection regarding customer preferences and
interests (J aworski and Kohli 1993), sales insights into customer demands used to

develop new products and services, and customize existing offerings.

Demand Management Information Technologies

Technology plays an important role in coordination of activities within a ﬁrm

(Gurbaxani and Whang 1991). Hence, we now examine the information technologies that

are related to the four categories of initiatives discussed above. For each of the demand

management initiatives, we evaluate matching demand management information

40

technologies (IT) (see Figure 3). These technologies, related to each of these initiatives,

are:

1.

Internal supply side IT (for coordination): Technologies can play an important part in
the coordination of information across departments. Given the amount of information
that needs to be coordinated, digital means are the best measures to accommodate the
granularity and details of this information. The use of such information technologies
for coordinating the information across the logistics, supply chain, sourcing, and
ﬁnance functions is considered critical. In this research we assess the frequency with
which the ﬁrm uses these information technologies to manage the customer demand
through internal supply-side initiatives.

External supply side IT (related to sharing information with suppliers): These IT
systems help ﬁrm to manage its demand management activities across its external
suppliers, and technologies in this group include extensible markup language (XML),
and electronic data interchange (EDI) for complete and real time visibility into the
suppliers systems, partner related information systems, and group collaboration

systems (Asgekar 2003)

. Internal demand side IT (used for planning and forecasting demand). These

technologies help integrate and bring in information from the ﬁrm’s internal
marketing department. These technologies, are critical for the development of
responsive demand plans and include IT systems used for creating and changing
products for new and short life cycle products, data mining, and scenario

management,.

41

4. External demand side technologies (customer related). The technologies are used for
bringing in the data from the customer outlets and include POS data collection

technologies such as RF ID, and Bar coding. (Fish and Forrest 2007)

 

Figpre 3: Information Technologies used for dem_and management

 

Information Technologies in Demand Management processes

 

 

 

 

External External Internal Internal
Supply Demand Side Supply Side Demand Side
Side IT IT IT IT

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Demand Management (DM) Agility

Agility has become an important organizational characteristic due to the rapidly
changing organizational environment which calls for greater responsiveness of the ﬁrm.
It is deﬁned as “the ability to detect opportunities for innovation and seize those
competitive market opportunities by assembling requisite assets, knowledge, and
relationships with speed and surprise.” (Sambamurthy et al. 2003; D’Aveni 1994).

Other related deﬁnitions of agility have been proposed since the construct is still
in its initial stages of application to organizational phenomenon. For example, Shaniﬁ
and Zhang (1999) deﬁne agility as “the ability to cope with unexpected challenges, to
survive unexpected threats of the business environment, and to take advantages of
changes as opporttmities.” Researchers studying agility have emphasized that ﬁrm’s
ability to respond is a key measure of agility (Dove 2001; Overby et al. 2006; Zaheer and
Zaheer 1997). In this research we study the ﬁrm’s response capabilities to meet customer

demand as a measure of their demand management (DM) agility.

42

The agility of the organization is intricately linked with the agility of its order
management cycle (Swafford et al. 2006; Shapiro et al. 1992). It is thus not surprising
that agility in the ﬁrm’s order management cycle or its supply chain is often studied as a
critical part of the organizational agility (see Sambamurthy et al. 2003; Swafford et al.
2006). For example, Narsimhan et al. (2006) surveyed 354 manufacturing plants in two
phases and did a cluster analysis to distinguish low, lean, and agile performers. They
compared the adoption of various supply chain best practices across the groups and found
that agile performers are at par with lean performer group on statistical quality control
and benchmarking. However, these agile performers rated higher in the adoption of
practices such as supplier-base rationalization, customer orientation, integrated product
design, supplier development, manufacturing strategy integration, cellular manufacturing,
supplier information sharing, supplier partnerships, strategic supplier selection, JIT ﬂow,
TQM, and workforce development.

However, in supply chain literature, there has been more than proportional
emphasis on the supply side agility. Particularly, wide research attention has been given
to the manufacturing agility that is deﬁned by the researchers at Iaccoca Institute of
Lehigh University as “. . .A manufacturing system with extraordinary capabilities
(Internal capabilities: hard and soft technologies, human resources, educated
management, information) to meet the rapidly changing needs of the marketplace (speed,
ﬂexibility, customers, competitors, suppliers, infrastructure, responsiveness). A system
that shifts quickly (speed and responsiveness) among product models or between product
lines (ﬂexibility), ideally in real-time response to customer demands (customer needs and

wants)” Youssef (1992). Alternatively, manufacturing agility is also deﬁned as the

43

“capability of an organization, that is developed by proactively establishing virtual
manufacturing with an efﬁcient product development system, to (i) meet the changing
market requirements, (ii) maximize customer service level, and (iii) minimize the cost of
goods, with an objective of being competitive in a global market and for an increased
chance of long-term survival and proﬁt potential” (Gunasekaran and Yusuf, 2002).
Agility has been associated with many related concepts, such as ﬂexible
manufacturing (Bolwijn et al., 1986), lean manufacturing (Womack et al., 1990), time-
based competition (Stalk and Hout, 1990), and fast-cycle innovation (Tidd et al., 1997).
Researchers have also emphasized that agility is related to the introduction of advanced
information technologies and organizational initiatives in lean manufacturing. Swafford
et al. (2006) differentiate agility (a capability that is externally oriented) from ﬂexibility
(a competency that is internally focused) and emphasize the latter to be an antecedent.
However, the proposed distinction between competencies and capabilities is not so clear
in the strategic management literature which has often used these concepts
interchangeably. Williamson (1975) commented that it would be only with time that the
meaning of these words will emerge distinctly. Further, other researchers have deﬁned
manufacturing and logistic ﬂexibility to be a part of agility. Similarly, lean manufacturing
practices and agile practices are often not so clearly differentiated. While some
researchers have deﬁned these to be antecedents in a ﬁrms strategy (i.e. being agile
follows being lean), others have proposed that ﬁrms can follow both together (see
Narsimhan et al. 2006). Yet others have termed the joint strategy as being ‘leeagile’
(Goldsby et al. 2003). To summarize, the manufacturing literature has deﬁned agility to

be arising of the newer paradigm of production and hence the comparisons are with the

44

traditional concepts of ﬂexibility and lean production. While there has been considerable
attention to agility in the manufacturing research, there is little attention to the concept of
agility in the ﬁrms’ demand side operations (Stank and Lackey 1997). In this research,
we assess a ﬁrm’s response to changes in customer demand as a measure of the agility of
its demand management processes (Overby et al. 2006).

We propose demand management agility to be a measure of ﬁrm’s responsiveness
to changes in customer demand. We propose a more holistic view that transcends the
focus on manufacturing, and instead focuses on the agility in demand management
operations.

Further, the temporal dimension forms the basis of our conceptualization of DM
agility. Three different types of DM agility are studied based on the timing of the ﬁrm’s
response.

Adaptive, Resilient, and Entrepreneurial

While agility is the overall ability to have superior response mechanisms to
defend or gain enhanced competitive advantage due to changes in business environment,
it is the timing of the response that differentiates the three kinds of DM agility. Our
assessment is based on timing of the events that affect demand for a ﬁrm’s products. The
APICS framework deﬁnes these three time periods based on the time at which the event
occurs in the ﬁrm’s production cycle (Crum and Palmatier 2004). Our conceptualization
of DM agility is with reference to these three time periods. We contend that the overall
DM agility is a sum total of these three components i.e. ﬁrms with high values for these

three types will have greater demand management (DM) agility.

45

The adaptive DM agility assesses the ﬁrm’s response to the events that occur after
the demand plans have been ﬁnalized but the production on them has not yet begun. The
response actions as these are common in the case of regulatory or political changes that
may impact the business conditions and hence affect competitiveness of ﬁrms’ products.
Similarly, the changes in customer demand patterns, for example due to a new competitor
launch or another business event, might affect the demand for a ﬁrm’s products.

Unlike adaptive DM agility, resilient DM agility measures the ﬁrm’s response in
the time period when the execution on demand plans has begun and some value has
already been added to the products. These variations in demand arise due to general
business condition such as changes in business activity, and a competitor’s product
launch. The business practices of information sharing and close working relationship with
suppliers and rapid constant collaboration with marketing and sales intelligence facilitate
the actions that are requisite for adequate response.

Entrepreneurial DM agility is the ability of the fmn to sense before-hand business
opportunities and challenges. The events are invariably still the same and may include
introduction of new technologies or changes in the regulative or business environment. A
contrasting example of entrepreneurial DM agility was demonstrated by Dell when it
adopted the Internet to develop a model to meet customer demand only after it was
realized. Even the order to the suppliers was sent only after the customer has placed its
order leading to a very efﬁcient system of production. Entrepreneurial DM agility is
aided by the use of responsive technologies and business initiatives that help a ﬁrm to

respond to these events.

46

Two dimensions of DM agility: speed and effectiveness

We include in our research, all three DM agility types. These three types are all
measured for the two dimensions of DM agility —speed and effectiveness-that have now
gained importance in industry. While the speed of response is an important indicator of
the ﬁrm’s agility and has often been identiﬁed as the only dimension (Zain et al. 2005),
this research goes beyond the assessment of mere speed of response and also studies the
effectiveness of ﬁrm’s response. While the speed of response is a critical indicator of
overall performance, effectiveness is equally important, if not more. Let us consider an
example of a hypothetical cell phone manufacturer, Johnsung Pvt. Ltd. Johnsung might
see a huge ﬂuctuation in the demand for its cell phones due to the launch of iPhone by
Apple. Given the speed of the technology markets, it is imperative for Johnsung to
respond to Apple’s launch, with say a new phone, as quickly as possible. However,
merely ‘getting there’ with a new phone won’t suffice, as it would be hard for Johnsung
to then position it’s product differently from it’s other competitors. Thus, it is as
important for Johnsung to have an effective response to Apple’s launch-as it is to have a
speedy response. In this research, we assess both these dimensions of agility separately.
Supply Chain Performance

Performance metrics have gained increased attention as these help align and
measure the performance of diverse partners who all contribute to the same supply
chains. As the competition in current business environments is between supply chains
rather than the individual ﬁrms, it becomes imperative to standardize the measures of
performance for each ﬁrm. In the supply chain, the performance measures used have

been categorized according to the various dimensions. These dimensions include

47

quantitative versus qualitative (Beamon 1999, Chan 2003); supply chain attributes of
cost, quality, delivery, ﬂexibility, visibility, and trust (Schbnsleben 2004, Gunasekaran et
al. 2001, Chan 2003); focus as strategic, tactical, or operational (Gunasekaran et al.
2001); or the supply chain process (e.g. the processes in SCOR framework such as plan,
and deliver) to which the measures relate (Chan and Qi 2003, Huang et al. 2004 Li et al.
2005, Shepherd and Gunter 2006). In this research, our focus is on two types of
performance measures - service related and overall business related- in the context of
supply chains. We study the two as service performance and overall business
performance.

In the next section we elaborate on the relationships between these constructs and

develop the research framework.

48

CHAPTER 3: RESEARCH FRAMEWORK

As more activities are digitized, the efficiency of various work systems within the
demand management processes is increased. The enhanced speed of communication and
an increased ability for collaborative work poses a challenge and offers an opportunity to
enhance the response capabilities of these processes. However, the creation of enhanced
capabilities requires concerted organizational initiatives involving alignment of multiple
demand management activities in line with the new technologies. We build on these

arguments and explain the proposed research model (Figure 4) below:

F i re 4: Research model for demand mana ement and su 1 chain rformance

 

 

 

 

 

 

 

 

 

 

 

 

 

Demand Management
Information Technolog'es DM Agility
0 Speed, and _D.2I!__S“ Chain
0 Effectiveness ___Performance
# Adaptive - Service
# Resilient 0 Business
MW # Entrepreneurial
Business Initiatives

 

 

 

 

 

Demand Management (DM) Agility

DM agility is the capability of the supply chain to quickly respond to the
technological, regulative or business related events. These events might affect a ﬁrm’s
ability to plan or fulﬁll demand. Two key dimensions of DM agility studied in this
research are speed and effectiveness with which the ﬁrm responds to the changes in
demand. Further, based on the timing of ﬁrm’s response, DM agility is deﬁned to be of
three types. For the events that occur after the operational and production plans have been
ﬁnalized, but not executed, the ﬁrm’s response is characterized as adaptive DM agility.

The response to events that happen during the normal course when the demand plans are

49

being operationalized is deﬁned as resilient DM agility. Proactive sensing of demand
changes in future to redeﬁne customer products and offerings is deﬁned as
entrepreneurial DM agility. The cumulative level of the three types together determines
the extent to which the ﬁrm is considered agile. The ﬁrm that possesses more of the three

types of agilities in its demand management process is more agile than others.

Performance Impacts

The examples of such business events that either challenge or offer new
opportunities to ﬁrms are abundant in nature. These include changes in the customer
demand patterns, popularization of new alternates to the ﬁrm’s products and services,
regulatory changes that affect the proﬁtability of the ﬁrm’s products etc. The ability of
the ﬁrm to respond to these events determines its ability to ﬁrlﬁll the customer orders.
The ﬁrms that are quickly able to respond to the changes in the business environment
realize greater chances of fulﬁlling customer orders. Further, these ﬁrms also realize
greater customer satisfaction as they excel at the demand management and fulﬁllment

activities. Thus, we propose:

H I a: The speed of DM agility will lead to greater service performance

Further, as the ﬁrms see a change in the pattern of the customer demand, a speedy
response is essential to maintain the market position. According to the models of strategy
that emphasize the exploitation of market power (Porter 1980; Shapiro 1992), ﬁrms
ability to earn superior rents by impeding the competitive forces in product markets is
hurt due to these adverse events. While the managers may resort to strategic moves and

Short-term tactics (for example, by forcing the channel partners to stock up redundant

50

inventories), these have been condemned by the researchers as short lived and tactical in
nature (Teece et al. 1997: p. 513). According to these researchers a ﬁrm’s competitive
advantage, and hence performance, is based primarily on its ability to manage the new
business scenario by doing more enduring work in developing capabilities that help
reacquire their competitive position in the changed business scenario. The speed of DM
agility is one such capability that helps the ﬁrm to quickly regain its competitive

advantage in the marketplace, and hence

H I b: The speed of DM agility will lead to greater business performance

However, besides the speed of DM agility, it is imperative for a ﬁrm to be
effective in its response strategies. For example, not only a speedy response but also
effectiveness is essential when launching a product in response to a competitor’s product
launch. Thus, the quality of order fulﬁllment (and not just the time taken to ﬁrlﬁll them)
is a determinant of a ﬁrm’s effectiveness. Firms that are able to achieve greater
effectiveness in their response to the changed business conditions are more likely to
exceed the expectations and reduce complaints of the customer, thus realizing superior

service performance. Hence, we propose

H 2a: The eﬂ'ectiveness of DM agility will lead to greater service performance

While the regulatory technological or business events affect the value, rarity,
inimitability, or non-substitutability of existing resources, and ﬁrm’s gain competitive
advantage by responding to the changed scenario (Teece et al. 1997). The quality of the
ﬁrm’s response and hence its impact in the competitive marketplace is not determined by

a mere speedy response from the ﬁrm. Instead, a well coordinate and effective response is

51

essential for meaningful reconﬁguration of resources. The ﬁrms’ ability to gain
competitive advantage and capture superior rents is based on their ability to reconﬁgure
valuable, rare, and inimitable resources. Thus, effective DM agility is a direct precursor
to the business performance in the modern day business environment, where the

disruptions are a norm rather than exception. And we propose:

H 2b: The eﬂ'ectiveness of DM agility will lead to greater business performance

Demand Management Information Technologies

The role of IT in ﬁrms’ demand management process is becoming more and more
critical. Two distinct capabilities contribute to this growing interest in IT systems. The
ﬁrst is related to their technological superiority over the manual processes, and second
relates to the business capability encoded in the IT artifact. The technological superiority
of frrms’ technologies arises due to the associated digitization of processes, which
enhances the data storage capacity, transmission capacity, and processing capacity
(Culnan and Markus 1987; Huber 1990). The use of technologies helps digitize indicators
for plant performance, forecasts, production plans and inventory, and point of sales data,
making it easier for different partners in the supply chain to simultaneously process,
transfer, and store these vital inputs for demand management. The advanced capabilities
of digital technologies also help enhance the communication capabilities by decreasing
the cost and effort in communication (Rice and Blair 1984) and enhancing the precision
and selectivity in participation of group members (Culnan and Markus 1987; Sproull and
Kiesler 1986). For example, use of group collaborations systems helps the product design
departments and ﬁrms’ suppliers to collaboratively work on redesigning products based

on consumer response. Thus the digital capabilities of IT systems lessen the overall time-

52

span and effort required in coordination and transactions (Gurbaxani and Whang 1991).
This helps ﬁrms to respond to sudden changes, in the customer buying patterns or to
match a competitor’s offering (for example, through collaboration in product

development or dynamic management of demand).

The second factor that makes the IT systems superior is related to the business
capability encoded in them. The use of these systems helps routinization of complex tasks
in order fulﬁllment activities. These IT systems bring in external information in the form
of decision models of experts that helps enhance the quality of decision making (Huber
1990; Zmud 1983). For example, a sophisticated forecasting algorithm can be bought off
the shelf instead of being developed and implemented in house that would call for
recruiting various researchers at different levels where the forecasting needs to be done.
Similarly, the industry best practices may be coded into the information systems giving
ready access for ﬁrms to utilize these in their own business activities. For example, the
complex algorithms for supply chain optimizations available through information
systems can be easily routinized into the lower decision-making levels without physically
involving an expert at the level (Huber 1990). This ability to routinize has been
highlighted by the researchers as essential for the development of organizational
capabilities (Teece et al. 1997). According to them, the ability of the ﬁrms to respond to
the changing business conditions, especially in the low and moderately dynamic business
environments, is directly dependent on the extent of routinization of the organizational

activities (Eisenhardt and Martin 2000).

53

As business activities are routinized, the managerial attention is freed and can be
applied to contemplating response strategies to changes in the business environment.
Also, routines may facilitate creative experimentation to ﬁnd new ways of doing work
and hence facilitate innovative and effective response to threats and opportunities. For
example, in their case study at Volvo, Holmqvist and Stefansson (2006) found the impact
of RF ID on increasing the creative opportunities in the ﬁrm’s supply chains. Information
technologies such as RF ID help ﬁrms to monitor in real time the ﬂow of products and
services and hence help fast and effective alignment of demand forecasts to real sales.
Thus, based on the above reasoning, we propose that the adoption of the four types of
information technologies (internal supply side, external supply side, internal demand
side, external demand side) in the ﬁrms supply chains would help ﬁrms develop speed

and effectiveness of DM agility.

H 3a: The greater use of demand management I T will lead to greater speed of DM agility .
H 3b: The greater use of demand management I T will lead to greater effectiveness of DM agility .

Demand Management Business Initiatives

Over the last ﬁfty years, the supply chains have grown from being within the boundaries
of the ﬁrm (example the vertically integrated Ford River Rouge Plant) to being a complex
network of multiple suppliers. The products sourced from these suppliers have evolved
from being mere commodities to more specialized parts that are critical to the sourcing of
a ﬁrm’s products. Effective demand planning and management in contemporary supply
chains thus includes the supplier’s performance. Thus, a critical component for the ﬁrm
to realize superior performance of its demand management process is related to the use of

demand planning initiatives that enhance its ability to share information and collaborate

54

with suppliers. Shefﬁ (2005) describes an incident in the semiconductor industry that
very succinctly summarizes the role of business initiatives in the demand management of
the ﬁrm. A small ﬁre at the Philips’ Albuquerque plant in New Mexico that fabricated
semi conductor chips went totally unnoticed by the media. The ﬁre was put off in less
than ten minutes by ﬁreﬁghters and staff. While it was overtly not a major incident, the
soot, water and panicking ﬁreﬁghters did enough to destroy millions of semi conductor
chips to be used in cell phones. This was astutely caught by Nokia by noticing a pattern
in the disruption of supplies from Philips. Over the next weeks Nokia engaged in
continuous discussion with Philips to assess the impact on its ability to meet demand
forecasts and soon concluded that there is likely to be huge variance in its ability to meet
demand for cell phones in future. Following this assessment Nokia captured the existing
worldwide supply of semiconductor chips by contracting with other suppliers. Erickson,
another customer of Philips and ﬁerce competitor of Nokia, on the other hand, was
satisﬁed by the explanation from Phillips and waited. By the time Erickson realized the
extent of the problem it had lost its worldwide supply, and hence the ability to meet the
demand of the cell phones. While the story has been often told, it emphasizes how
demand management involves a closely managed relationship with suppliers, and

continuous ﬂow of information between partners.

Since in a supply chain value often lies outside of the ﬁrm boundaries, the arms
length market arrangements that might be efficient for one time transaction are not
suitable for transacting within the network of supply chains. To operate within the

boundaries of the ﬁrm and at the same time integrate the knowledge of the external

55

partners with their own supply chain, ﬁrms adopt various information sharing initiatives.
These business initiatives help monitor and integrate a large amount of the information
required for the management of demand planning and fulﬁllment activities. Complete
visibility of suppliers’ production and demand schedules, for example enhances the
accuracy of ﬁrm’s forecasting processes and helps them sense any discrepancy in planned
production schedules (Lee et al. 1997).

Similarly, the modern day organizations have greater inputs from the other
departments (e.g. sourcing, and logistics) to develop and evaluate the demand plans. In
line are also the initiatives for real time coordination with the point of sale outlets. These
various initiatives are studied as the demand management initiatives.

While in the traditional supply chains the means to respond to the changed
business conditions was redundant inventory (safety stock) or redundant capacity, the
modern day supply chains instead resort to dynamic managerial action. Managerial action
has gained even more emphasis after the adoption of such supply side initiatives as lean
manufacturing which has reduced the inventories, even for critical parts, to near zero.
Thus, the goal of modern day organizations is to develop superior organizational
capabilities as DM agility (the ability to respond to changed business conditions), rather
than having redundant capacities as buffer. The development of DM agility for managing
customer demand is thus based on the ﬁrm’s response to these changed business
conditions.

Impact on DM Agility
Rapid changes in business environment call for continuous changes in product

designs and real time demand management. Similar to technologies, various demand

56

management initiatives often help ﬁrms respond to the changes. For example, many years
of industry wide experience and practice has led to the development of such supply side
methodologies as Just-in-time (JIT) production, cellular manufacturing, quick response
(QR) initiatives, and lean principles. These practices facilitate rapid response by the ﬁrm
in its manufacturing ﬁmctions and have been found to impact manufacturing agility
(Narasimhan et al. 2006). Similar to these supply side practices, various demand practices
are detrimental to fast response and agility in supply chains.

The rationale for the effect of these practices on DM agility of the ﬁrm is the vast
managerial experience that is codiﬁed into these practices. These practices, such as VMI
initiatives and S & OP planning (Crum and Palmatier 2004, Lapide 2007, Hewitt 2001),
have been applied and reﬁned by their successful and not so successful adaptations by
various ﬁrms. These previous experimentations have helped capture the tacit learning in
their application and codiﬁed these in the form of best practices, formal procedures, and
management techniques. The knowledge related to ‘what works and what does not’ is
available in the management literature and the practitioners’ experiences. Further, besides
learning from outside the ﬁrm, various initiatives involve tapping the wide knowledge
and experience within the ﬁrm. It is well known that experience facilitates a deeper
understanding of the activities and hence helps in the development of more complex and
effective routines (Argote 1999; Zander and Kogut 1995). Further, the complex routines
that evolve through learning and experience are often deﬁned as the source of dynamic
response capabilities (Nelson and Winter 1982; Zollo and Winter 2002; Teece et al.
1997). Thus, the various demand management practices (such as those that involve

sharing and coordinating information across partners, or within the ﬁmctional

57

departments of a ﬁrm) facilitate a coordinated and quick response from the ﬁrm in case
of a demand change due to business events such as changed business conditions or other
regulative events that lead to changes in consumer demand.

Further, the role of demand management practices in enhanced agility is also due
to the division of labor aspect. As organizations are limited in their information
processing abilities, some of the work practices are better done outside of the ﬁrm, or
within the ﬁrm but in different departments (such as logistics, sourcing etc.). These
vendor ﬁrms/allied departments are able to realize economies of scope in their learning
and performance of these activities and hence develop specialized competencies. This
might facilitate DM agility as each ﬁrm can focus on its own core competence and hence
enhance the speed and effectiveness with which a ﬁrm can respond (Hamel and Prahalad
1994). However, effective business initiatives are needed to coordinate the work done
across these departments and integrate it with the ﬁrm’s demand management efforts.

While the demand management initiatives are lesser known, the impact of various
supply side initiatives on supply chain agility has been well established. Christopher and
Towill (2001), for example, document these effects. They categorize the initiatives into
actions, programs and principles, and study initiatives as setup time reduction, cross
ﬁrnctional teams, process management, standardization, lean practices, ﬂexible and
response, postponement, rapid fulﬁllment. Similarly other manufacturing practices that
have been attributed to agility include supplier alliances, high skill employee training,
customer sensing, and sales linkages (Brown and Bessant 2003; Prince and Kay 2003;
Nagel and Bhargava 1994). McCullen and Towill (2001) have argued for highly skilled

workers, partnership arrangements and closer relationships with suppliers, and JIT

58

manufacturing as being associated with greater agility. The empirical research has found
these effects. Narsimhan et al. (2006) for example, surveyed 354 manufacturing plants in
two phases and did a cluster analysis to ﬁnd the impact of various supply chain best
practices on the agility of the plant operations. They found that agile performers are at par
with lean performer group on statistical quality control and benchmarking. However, for
other practices (supplier base rationalization, customer orientation, integrated product
design, supplier development, manufacturing strategy integration, cellular manufacturing,
supplier information sharing, supplier partnerships, strategic supplier selection, JIT ﬂow,
TQM, and workforce development), the agile performers were associated with a greater
degree of implementation of these initiatives than the ﬁrms in lean or low performer
groups. Similar to these earlier studies of the supply side agility we proposed the impact
of the four types of demand management initiatives (internal supply side, external supply
side, internal demand side, external demand side) on both the dimensions of demand
management agility:

H 4a: The greater use of demand management initiatives will lead to greater speed of DM agility.
H 4b: The greater use of demand management initiatives will lead to greater effectiveness of DM agility

Complementarities between the use of demand management IT and initiatives

While routinization of process is aided by the information technologies and
supply chain practices (hypothesis 2 and 3 above), these two themselves are intricately
linked. The demand management practices are heavily aided by the digitization of
information. For example, the automatic replenishment programs rely heavily on the real
time data captured using bar coding or RFID technologies and transferred over the

telecom networks. The realization of superior performance, however, involves complex

59

dynamics to realize synergies between the two. These dynamics are often explained using
complementarities theory according to which the IT systems are not able to realize
desired effects in the absence of their alignment with the existing strategy, structure and
activities of the ﬁrms (Barua and Mukhopadhayay 2000; Milgrom and Roberts 1990,
1995). This is because even though the IT systems have embedded business routines
these routines are generic in nature and need to be customized to the ﬁrm speciﬁc
business processes. The synchronization with demand management processes, that are a
set of complex mechanisms for managing supply chains, thus is essential for realizing IT
value.

This interdependence between the IT and demand management practices is
intricately linked to the notion of an activity systems. The synchronization of IT with the
business practices helps realize super additive effects in the activity systems (Wade and
Hulland 2004; Milgrom and Roberts 1990). For example, the demand management
process for forecasting is more responsive if the ﬁrm leverages the capability to
frequently run IT aided simulations and optimization routines. The overall value
realization of both the IT and forecasting initiatives together is much more than the
additive effect of having either one of them (i.e. VAB > VA + VB).

These super additive value synergies arise only when the synchronization of the
two resources in the activity systems occurs after repeated adaptations and retention of
the mechanisms with best results (20110 and Winter 2002). After, the digital capabilities
of IT systems are routinized through these complex adaptations with the demand
management practices the organization develops a rich knowledge and know-how (often

tacit in nature) which is path dependent. This knowledge is often in the form of complex

60

routines specifying interdependencies between the available IS capabilities and the
knowledge of the demand management practice. The organizational actors, aided with
these routines, are able to do quick mental simulations of the various cause and effect
sequences that might be evoked in case of sudden change in business conditions
(Siggelkow 2002; Weick 1979). The mental models, so developed for managing demand,
are not a result of the combined knowledge of the IT know how and demand management
processes but an intertwined set of routines and rules that are co-produced during the
application of IT systems to demand management processes. This unique familiarity and
knowledge to develop organizational knowledge affects the institutional structures related
to signiﬁcation, legitimation and domination. Thus, the knowledge and learning gives the
overall activity system codes for action through the norms, power structures and rules of
action that result from the institutionalization of combined application of IT technologies
and supply chain practices (Scott 2001). The end result is a set of complex routines that
give the organization ability to quickly respond to changes in the business conditions
(Teece et al. 1997; Eisenhardt and Martin 2000). Hence, we propose the impact of four
types of complementary interactions (internal supply side, external supply side, internal

demand side, external demand side) between the IT and corresponding initiatives as:

H 5a: The greater complementarities between demand management IT and initiatives will lead to greater
speed of DM agility

H 5b: The greater complementarities between demand management I T and initiatives will lead to greater
effectiveness of DM agility.

CONTROLS
The responsiveness of a supply chain is well established to vary according to the

external environmental conditions as well (Mendelson and Pillai 1998, 1999, Pavlou and

61

Sawy 2006). Industry turbulence for example, may inﬂuence performance as well as DM
agility. Thus, we control for turbulence in assessing the DM agility and performance of
the ﬁrm. We also control for the impact of other exogenous factors on a ﬁrm’s
capabilities and their impact on performance. Similar to Rai et al. (2006) we control for
the size of the organization (Hitt 1999). Further we also control for the product volume.
Finally, given the surge and impact of postponement strategies, we also control for the
extent to which the ﬁrm uses postponement initiatives in its operations (Li e al. 2005).
Postponement can help ﬁrm to meet with such demand changes that occur even as the
ﬁrm has planned its production but not yet ﬁnalized the customized aspects. Hence, it can

be an important factor in a ﬁnn’s ability to meet customer demand changes.

In the next chapter, we deﬁne the details of the research methods used to test the

hypotheses, as depicted in Figures 5 and 6.

62

Figpre 5: Determinants of speed of DM agility and its impact on supply chain

performance

 

 

 

DM IT

 

Side IT

 

 

External

I Internal Demand
| Demand Side IT

 

 

Internal Supply
Side IT

 

External Supply
Side IT

 

 

 

 

 

DM Business
7 Initiatives
Internal Demand
Side Initiatives

External Demand
Side Initiatives

Internal Supply
Side Initiatives

l
l
1

 

External Supply
Side Initiatives

 

 

63

 

Controls

Size

Product Volume
Postponement
Turbulence

 

 

 

Supply Chain
Performance

Business

 

Agility
Speed

Performance

 

l

 

l—~

Service
Performance

 

 

 

 

Figpre 6: Determinants of effectiveness of DM agility and its impact on supply

 

chain mrformance

 

 

DM IT

 

Internal Demand
Side IT

 

l

External
Demand Side IT

Side IT

 

External Supply

Internal Supply |
Side IT I

 

 

 

 

DM Initiatives

 

Internal Demand
Side Initiatives

 

 

External Demand
Side Initiatives

 

Internal Supply
Side Initiatives

 

 

External Supply
Side Initiatives

 

 

 

 

 

 

 

64

 

Controls
Size
Product Volume
Postponement
Turbulence
Supply Chain
Performance
Business
, Performance
Agility
Effectiveness

 

 

    
 

  

 

 

 

 

 

 

  

Service
Performance

 

 

 

 

CHAPTER 4: RESEARCH METHODS

Data for testing the hypotheses was gathered through a large scale survey of supply chain
managers responsible for demand management. Given the lack of any secondary data that
could help us get an in depth information about the usage of these technologies and
initiatives, survey was considered the most important way to examine the hypotheses.
Further, given that certain variables, such as resilient, adaptive, and entrepreneurial DM
agility, are being assessed for the ﬁrst time in the literature, we wanted a broad-based
survey instead of case-based or a limited interview to enhance the range of ﬁrms giving

inputs.

Data Collection

Sample and populations

The data for the study was collected through a survey of the professionals in the
supply chain. The members of the Association for Operations Management (APICS)
were used as the target population. Participation was sought only if a manager had
signiﬁcant interaction with demand management in their job. APICS has gained
increased attention by researchers in the ﬁeld of information systems (Bharadwaj et al.
2007). Given our interest to measure the actual use of technologies and initiatives, APICS
members were considered to be especially suitable for collecting data to assess our
research questions since they have the most representative set of respondents responsible
for demand planning and fulﬁllment as compared with other member organizations such
as Council of Supply Chain Management Professionals (CSCMP) which was found to

have relatively greater representation of strategic managers.

65

A pilot study was conducted before the main data collection. A panel of ten
working professionals and academicians with varied backgrounds took the survey and
gave suggestions on improve the clarity, meaning, composition, and ordering of the
survey. The suggestions lead to the addition and rewording of items being used in the
scales. The respondents were also asked to time themselves and majority of the
respondents took less than twenty minutes to ﬁnish the survey.

Email was used to solicit participation from the respondents. The average click
through rates for the surveys conducted by APICS is 3% and average open rate (i.e. the
percentage of people who opened the email) is 22%. The invitation for participation was
received and opened by 1283 respondents and 303 respondents clicked on the survey
link. This leads to an open rate of 29.3% and click through rate of 6.7% both of which are
higher than the average rate typical of surveys done by APICS. Aﬁer the two mail email
reminders a total of 159 members’ responses were received for the survey leading to a
14.7% conversion or response rate. This is a signiﬁcantly high rate because there is a
very niche segment of supply chain managers who work in demand management.
Response bias was tested by assessing the difference in the size of the ﬁrms responding
in the ﬁrst stage of emailing (early respondents), and those responding after second and
third follow-up emails (late respondents). There was no signiﬁcant difference in size and
hence response bias was ruled out. The various job proﬁles represented include customer
service manager & planning manager, demand analyst, supply chain planner, supply
chain analyst, and director of logistics, etc. The average numbers of employees in the

ﬁrms that participated are 7622. However, besides these large ﬁrms there are many small

66

ﬁrms in the sample as well and the number of employees of the ﬁrms vary from a

minimum of l to a maximum of 350,000 (see Table 5 for more details).

Table 5: Number of Employees in ﬁrms in the sample

 

 

Number of

Employees Frequency Percentage
Less Than 100 20 13.3
100 — 249 18 12.0
250-499 19 12.7
500-999 17 11.3
1000-2499 32 21.3
2500-4999 16 10.7
Greater than 5000 28 18.7
Total 150

 

Measures

Since the context of demand management has not been explored in IS research
previously, a number of measures were developed anew for this study. Two distinguished
professors, one an expert in the ﬁeld of information systems and the other in the ﬁeld of
logistics and demand management, along with the author of the dissertation developed
these measures following an iterative approach. We started with the existing measures
and modiﬁed these according to the demand management context. For over six months
the team was engaged in brainstorming and discussion regarding the constructs and items
that best reﬂect the measures. In the end, we developed items for each of the constructs
by customizing the theoretical underpinning from existing literature to ﬁt the context of
demand management. The existing measures were used as the starting point but most of
the items were developed anew. Wherever possible, existing measures were adopted and

customized as well.

67

Based on this exercise, items for information technologies and corresponding
demand management initiatives were developed for all four types: Internal supply side,
external supply side, internal demand side, and external demand side. Most of the
technology constructs were developed anew but our operationalization was similar to that
of Pavlou and Sawy (2006). Further, we assessed the frequency of use of information
technologies, and demand management initiatives. Frequency was considered a good
indicator of extent of use, given the ubiquitous nature of technologies, and considering
the need to constantly reevaluate one’s demand plans. Likert type scale with the range of
1 to 7 was used for response of the survey items.

Measures for Internal Suppﬂ Side IT and Initiatives

Given the focus on coordination in the internal supply chain, our items for the
internal supply chain were based on the coordination initiatives (Malone and Crowston
1994). We assessed the frequency of use of information technologies to facilitate
coordination between demand management function and other functions such as
production, logistics, ﬁnance, and marketing (see Table 6). We also assessed the extent to
which information technologies supported the sales and operations (S & OP) planning
which is critical element of demand planning and management. The respondents were
asked to rate the frequency of use of information technologies in their demand
management process (Table 6). Similarly, for the business initiatives, respondents were
asked to rate the frequency of use for their largest product line. Business initiatives
assessed were those used by the ﬁrm to assimilate and use the information from across

the departments and communicate demand related changes back to them (see Table 6).

68

ﬂable 6: Intern_al supply side information technologies and initiatives

 

 

INTERNAL SUPPLY SIDE IT

ITINCOl: IT applications to coordinate demand management activities with activities of
production department

ITINCOZ: IT applications to coordinate demand management activities with activities of
sourcing department

ITINCOB: IT applications to coordinate demand management activities with activities of
logistics department

ITINCO4: IT applications to coordinate demand management activities with the
activities of the ﬁnance department

ITINCOS: IT applications (such as'those used in Sales and Operations Planning (S&OP)
meetings) to facilitate coordination between supply chain and marketing.

INTERNAL SUPPLY SIDE INITIATIVES

We acquire and integrate the following information in our activities to meet changes
in customer demand

INTIAQIl'n: Operational production information (e. g. production schedules)
INTIAQIZ: Operational sourcing information (e. g. sourcing contracts)

INTIAQIB: Operational logistics information (e. g. route plans)

INTIAQI4: Operational ﬁnance information (e. g. proﬁtability of market segments)
INTIAQIS: Changes in supply chain structure (e. g. addition of new plant capacities,
addition or dropping of partner companies).

INICOM: We communicate changes in demand forecasts or customer preferences to the
following in our organization3:

a. The production function.

b. The sourcing function.

c. The logistics ﬁinction.

d. The ﬁnance function.

 

Measures for External Suppu Side IT and Initiatives

The external supply side initiatives and information technology construct
measures the extent to which the ﬁrm shares information and collaborates work with its
external suppliers partners (Lee et al. 1997, Malhotra et al. 2005). The technologies, such
as EDI and XML, or those used for collaboratively executing work processes, planning
and, forecasting were assessed in this domain. Similarly, for initiatives we assessed the

nature of information shared by the ﬁrm with its external suppliers (Malhotra et al. 2005)

 

2 These items were dropped from the analysis after the factor analysis.
3 The average across all four was used for analysis

69

 

(see Table 7). If we use the metaphor of a “pipeline” to conceptualize these technologies,
then the demand management initiatives to share information act as the material that
ﬂows through these pipelines. Thus, mere pipelines may not specify and it would be
essential to have both the material and pipeline together to realize their performance
effects. Similar to internal supply side IT and initiatives we assessed the frequency of use

for external supply side IT and initiatives.

Table 7: External supply side inform_ation technologies and initiatives

 

 

EXTERNAL SUPPLY SIDE IT

ITEXSPl: Electronic Data Interchange (EDI) to share demand related information (e. g.
forecasts, volume or mix changes) with external suppliers

ITEXSPZ: Extensible Markup Language (XML) to share demand related information
(e. g. forecasts, volume or mix changes) with external suppliers

ITEXSP3: IT applications (such as application or desktop sharing) for simultaneously
working in real time with external suppliers

ITEXSP4: IT applications to synchronize production plans across the external suppliers
ITEXSPS: IT applications for collaborative planning and forecasting of demand with
external suppliers.

EXTERNAL SUPPLY SIDE INITIATIVES

We exchange the following with our external suppliers:

EXTSPII: Details of upcoming product or service related changes

EXTSPIZ“: Details of future promotion or marketing plans

EXTSPI3: Information related to market demand trends or forecasts

EXTSPI4: Information on demand shifts or changes in customer preferences

EXTSPIS: Information about long-term production plans, capital investments or capacity
utilization

 

Measures for Internal Demand Side IT and Initiatives
The measures for internal demand side IT and initiatives were created to assess

the ﬁrm’s technological and organizational competencies in assessing the demand for

their products. The technologies used those used for forecasting of new, shorter lifecycle

70

 

products, and those that use analytics and data mining to forecast demand. These
technologies were selected as demand forecasting is the core element to assess demand
(Table 8). However, we also included the technologies that were used to tap into the
marketing information about consumer demand matching with these technologies were
the corresponding initiatives related to acquisition and integration of knowledge from the

ﬂble 8: Internal dem_and side inform_ation technologies and initiatives

 

 

INTERNAL DEMAND SIDE IT

ITDMMGI: IT applications (such as analytics or data mining) for creating or changing
demand plans

ITDMMGZ: IT applications for creating or changing demand plans for products that are
new, have short life cycle or strong seasonality patterns

ITDMMG3: IT applications for creating or changing demand plans for products that use
scenarios management functionality (e. g. what if analysis)

ITDMMG4: Spread sheet applications to plan demand

ITDMMGS: IT applications (e. g. Intranet) to acquire marketing information relevant for
creating or changing demand plans

INTERNAL DEMAND SIDE INITIATIVES
We acquire and integrate, from marketing department, information about:

MKTINIl: Marketing events (e. g. trade promotions, quantity discounts or trade deals)
that impact demand for our products

MKTINIZ: Business changes (e. g. new substitutes for our products) that impact demand
for our products

MKTINI3: Competitor’s activities (e. g. new product launches) that impact demand for
our products

MKTINI4: Key changes in customer preferences that impact the demand for our
products

MKTINIS: Changes in marketing strategies (e. g. product pricing or distribution
channels) that impact the demand for our products

 

marketing department. The acquisition and integration of knowledge is a key component
of organizational learning (Huber 1991, Hurley and Hult 1998, Lafferty and Hult 2001).

Further, included is the information available with the marketing department regards the

71

 

external business conditions, and customer preferences (Jaworski and Kohli 1993, Slater
and Nervar 1995, Kohli and Jaworski 1990) (see Table 8). Consistent with the previous
measures frequency of use was assessed for internal demand side IT and initiatives as
well.

Measures for External Demand Side IT and Initiatives

The external demand side measures assess the extent to which the ﬁrm is able to collect
information regards customer sales and preferences. The items for technologies used in
this domain included the bar coding and RFID technologies that are used to capture sales

_T_able 9: Extempl demand side information technologies and initiatives

 

 

EXTERNAL DEMAND SIDE IT

ITMKINl: Bar Coding technologies to acquire customer sales data

ITMKINZ: Radio Frequency Identiﬁcation Tag (RFID) applications to acquire customer
sales data

ITMKIN3”: IT applications (such as EDI or XML) to share production data (e. g.
inventory levels, and production schedules) with vendor managing automatic
replenishment of customer sales

ITMKIN4”: IT applications to share changes 1n demand plans with the vendor managing
the automatic replenishment of customer sales

EXTERNAL DEMAND SIDE INITIATIVES
We use the following customer related practices:

CUSRELPl: We analyze the sales information from customer sources (e. g. retail
outlets) to anticipate changes in customer demand,

CUSRELPZ: We often talk with or survey those who can inﬂuence our customer sales
(e. g. retailers, distributors) to anticipate changes in customer demand,

CUSRELP3: We integrate sales information from customer sources (e. g. retail outlets)
to plan our activities to meet changes 1n customer demand

CUSRELP4”: We use automatic replenishment methods (such as Vendor Managed
Inventory (V MI) initiatives) to meet changes 1n customer demand

 

data, and other technologies to exchange customer data with the vendor managing the

inventory (Table 9). However, the latter items were found to have signiﬁcant cross

72

 

loadings during the factor analysis (see below) and hence were dropped from future
analysis.

Similarly, the initiatives included the extent to which the ﬁrms engaged with its
customers in assessing the demand for its products. The survey items assessed how
frequent was the ﬁrm in surveying its customers and including their information in its
analysis and planning activities to meet demand. An item related to vendor managing the
inventory was dropped from the analysis as similar to the items for technology it was

found to have in appropriate loading.

Measures for DM Agih‘ty

DM agility was measured as the second-order formative construct and two
different dimensions of response were measured: speed and effectiveness. It was
considered essential to assess these two as separate dimensions due to the debates regards
the usefulness of just assessing the speed with which the ﬁrm is able to respond to
changes in its environment (Zain et al. 2005). Thus, according to the current thinking we
also evaluated how effective the ﬁrm was in its response. Respondents were asked to rate
the nature of their response on these two dimensions distinctly. The response for speed of
DM agility was measured on a seven point scale with the following categorization: 1-
Very Slow, 2-Slow, Somewhat slow, 4- Neutral, 5-Somewhat Fast, 6-Fast, 7-Very Fast.
For effectiveness the following categorization was used: 1- Very Ineffective to 7-Very
Effective.

The respondents were asked to think of cases where a demand ﬂuctuation exceeded more
than 20% of the product volume. This percentage was chosen as it is now considered to
be a standard by the Council for Logistics Management to categorize a ﬂuctuation as non

random. Six different events were considered as a reason for this change in demand (see

73

Table 10), and the respondents were asked to rate their response to these events for both
the speed and effectiveness of response.

Table 10: The events which call for an agile response from the ﬁrm

 

Agl: Changes in general business environment (e. g. decline in economic activity)

Ag2: Changes in regulative environment (e. g. increased taxation on raw materials that
increases the cost of your products)

Ag3: Changes in technological environment (e. g. that lead to cheaper substitutes for your
products)

Ag4: Competitor's new product launches (e. g. that offer more features than your
products)

Ag5”: Unplanned events by marketing department (e. g. trade promotions)

Ag6”: Unplanned changes in marketing strategy (e. g. distribution channel or product
pricing) for your products

 

 

 

Further, three different dimensions were assessed for agility- adaptive DM agility,
entrepreneurial DM agility, and responsive DM agility. The formation of these
dimensions was based on the framework created by APICS (Crum and Palmatier 2004).
The critical difference between the three is based on the time (or the stage in demand
cycle) when the ﬁrm has to respond. The respondents were explained that the three
components differ in terms of the period when the changes in customer demand occur.
Thus, adaptive agility is the ﬁrm’s response during the,

“Period when the demand plans have been ﬁnalized but execution on them has not
bSeifruiiiarly, resilient agility is the ﬁnn’s response in the time period when

“Period when the execution on demand plans has begun and some value has already
been added to the products ”

Finally, entrepreneurial agility was deﬁned as the ﬁrm’s response to changes in

“Future demand periods for which the demand plans are not yet created ”

Table 11: The items used for three types of agility

 

| ADAPTIVE

 

74

 

 

Rate your response for the demand changes that occur DUE TO THE FOLLOWING
REASONS in the PERIOD WHEN THE DEMAND PLANS HAVE BEEN F INALIZED
BUT EXECUTION ON THEM HAS NOT BEGUN:

Adl: Changes in general business environment (e. g. decline in economic activity)

Ad2: Changes in regulative environment (e. g. increased taxation on raw materials that
increases the cost of your products)

Ad3: Changes in technological environment (e. g. that lead to cheaper substitutes for your
products)

Ad4: Competitor's new product launches (e. g. that offer more features than your
products)

Ad5#: Unplanned events by marketing department (e. g. trade promotions)

Ad6“: Unplanned changes in marketing strategy (e. g. distribution channel or product
pricing) for your products

RESILIEN T

Rate your response for the following demand changes that occur DUE TO THE
FOLLOWING REASONS in the PERIOD WHEN THE EXECUTION ON
DEMAND PLANS HAS BEGUN AND SOME VALUE HAS ALREADY BEEN
ADDED TO THE PRODUCTS:

RAl: Changes in general business environment (e. g. decline in economic activity)

RA2: Changes in regulative environment (e. g. increased taxation on raw materials that
increases the cost of your products)

RA3: Changes in technological environment (e. g. that lead to cheaper substitutes for your
products)

RA4: Competitor's new product launches (e. g. that offer more features than your
products)

RAS”: Unplanned events by marketing department (e. g. trade promotions)

RA6”: Unplanned changes in marketing strategy (e. g. distribution channel or product
pricing) for your products

EN TREPRENURIAL

Rate your firm's response for the following demand changes that occur DUE TO
THE FOLLOWING REASONS in the FUTURE DEMAND PERIODS FOR
WHICH THE DEMAND PLANS ARE NOT YET CREATED:

EAl: Changes in general business environment (e. g. decline in economic activity)
EA2: Changes in regulative environment (e. g. increased taxation on raw materials that
increases the cost of your products)

 

Table 1 1 (Cont’d)

 

 

EA3: Changes in technological environment (e. g. that lead to cheaper substitutes for your
products)

 

75

 

 

 

 

EA4: Competitor's new product launches (e. g. that offer more features than your
products)

EAS“: Unplanned events by marketing department (e. g. trade promotions)

EA6#: Unplanned changes in marketing strategy (e. g. distribution channel or product
pricing) for your products

 

After explaining to them the differences in the three components of agility, respondents
were asked to rate separately, for the three time periods, the speed and effectiveness of
their response. They were asked to do this for the above mentioned six demand events
leading to the eighteen questions (see Table 11). The last two events were not found to be
signiﬁcant for adaptive agility and hence were dropped from future analysis for all the
types of agility.

ﬂble 12: Measures of service and business performance

 

 

Service Performance

ServPrl: Perfect Order Fulﬁllment: The percentage of orders delivered “on time and in
full” without any quality issues

ServPr2: Order Fulﬁllment Cycle Time: Amount of time from customer authorization of
a sales order to the customer receipt of product.

ServPr3: The overall satisfaction of the customer with the demand fulﬁllment activities
of the ﬁrm

ServPr4: Customer Expectations: The extent to which the ﬁrm’s demand fulﬁllment
activities exceed the expectations of the customer.

ServPrS: The performance of ﬁrm’s demand ﬁrlﬁllment activities versus the
competitor’s activities in the category

ServPr6: Customer Complaints: The customer complaints (formal or informal) about the
ﬁrm’s demand fulﬁlhnent activities.

Business Performance

BusPerl: Total supply chain costs

BusPer2: Cash to cash cycle time: The number of days of working capital tied up in
managing the supply chain.

BusPer3: Inventory Turnover (Ratio of sales to total inventory)

BusPer4: ROS: Return on Sales (Ratio of Net operating income to sales)

BusPerS: ROA: Return on Assets (Ratio of Net operating Income to aggregate total
assets)

BusPer6: Sales Grth (Average rate of change of sales over the last three years)

 

Measures for Supply Chain Performance

76

 

 

Finally, supply chain performance was measured using the indicators of service and
business performance that were based on the performances metrics produced by Supply
Chain Operational Reference (SCOR) model (see Table 12). Likert type scale with the
range of 1 to 7 was used for response of the survey items, and the respondents were asked
to rate their performance with respect to their major competitor.

Finally, turbulence was measured using the scale from Jaworski and Kohli (1993)
and Pavlou and Sawy (2006). Respondents were also asked about the size of the ﬁrm
(number of employees). Further a seven point likert scale was used to assess the extent of

postponement, and product volume (for the product they used to mark their responses).

Analysis

Data analysis was done in two stages. In the ﬁrst stage we did a factor analysis to
assess the appropriate factors underlying the constructs. Principal component analysis
was used to extract the factors, with the criterion of eigenvalues greater than 1. Varimax
rotation was used to create orthogonal factors. The items with cross loadings greater than
0.4 were dropped. The resulting set of factor structure revealed four factors each for
demand management initiatives and technologies (see Table 13 and 14). Both dimensions
of DM agility were assessed using a two factor structure. Firstly, each of the dimension
was factored for three different types separately, and then in the second stage the factor
scores for all the three types were used to extract the two dimensions — speed of DM
agility and effectiveness of DM agility (see Table 15). Similarly, factor structures were
assessed for the performance scale that revealed two set of underlying factors (Table 16).

The communalities for most items were found to be greater than 0.5, however a few

77

items with communalities greater than 0.4 were retained while creating the ﬁnal factors
due to theoretical reasons. The only reason to drop the items was if they were found to
have signiﬁcantly high cross loadings. After each factor analysis we also calculated the
Kaiser-Meyer-Olkin measure to assess sampling adequacy. In all the cases this was found
to be greater than 0.5 hence indicating that the sample size is not a problem. Similarly,
the Barlett’s test of sphericity was found to be signiﬁcant in all cases indicating that non-
collinearity was not a problem in our case, and the factoring was appropriate. Similarly,
no Heywood cases (i.e. items with loadings greater than 1) were found in any of the
factor results. Following the factor analysis we assessed the reliabilities of each of the
construct. The reliabilities were assessed for each of the factors and were found to be

appropriate (see Table 17).

Table 13: Factor analysis for demand management initiatives
Commnent

External lntemal External
lntemal Supply Demand Demand
Supply Side Side Side Side

Initiatives Initiatives Initiatives Initiatives

        

INTIAQIZ

INICOM 0.033 0.091
|NT|AQ|3 0.069 0.163
INTIAQI4 0.248 0.217
INTIAQIS 0.198 0.067
EXTSP|1 0.084 0.231 0.014
EXTSPI3 0.060

EXTSP|4 0.088

EXTSPI5 0.131

MKTINI1 0.139

MKTINIZ 0.048

MKTINI3 0.095

MKTINI4 0.142

MKTINI5 0.181

CUSRELP1 0.124

CUSRELP2 0. 059

CUSRELP3 0.151 .
Principal Component Analysis Is used as the extraction method and varimax method is used for
rotation.

78

Table 14: Factor analysis for demand management technologies
Compgnent

External lntemal External
Internal Supply Demand Side Demand
Su I Side IT Side IT IT Side IT

     
   
     
      

   

ITINCO1 0.223 0.154 -0.077
ITINCO2 0.155 0.020 0.233
ITINCOS 0.375 0.002 -0.057
ITINCO4 0.019 0.240 0.225
ITINCOS ' ‘9 ~ * 0.379 -0.282
|TEXSP1 0.283 0.073 0.328
ITEXSP2 0.184 0.033 0.390
ITEXSP3 0.160 0.063
ITEXSP4 0.170 0.099
ITEXSP5 0.268 ‘ 0.269
ITDMMG1 0.383 0.163 0.218
ITDMMGZ 0.395 0.115

ITDMMG3 0.222 - 0.222

ITDMMG4 0.006 0.008

ITDMMG5 -0.076 0.348

ITMKIN2 0.074 0.134

ITMKIN3 0.037 0.263 0.193

 

 

Principal Component Analysis is used as the extraction method, and
varimax method is used for rotation.

79

Table 15: First order and second order factor analysis for two dimensions of DM agilig

 

First Order Factor Analysis : Speed of DM Agility

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Component Component Component
Adaptive Resilient Entrepreneurial
Speed Speed Speed
AAS1 0.773 RAS1 0.837 EAS1 0.828
AASZ 0.770 RASZ 0.866 EASZ 0.872
AAS3 0.840 RAS3 0.878 EA83 0.882
AAS4 0.831 RAS4 0.856 EAS4 0.861
First Order Factor Analysis : Effectiveness of DM
1.9in
Component Component Component
Adaptive Resilient Entrepreneurial
Effectiveness Effectiveness Effectiveness
AAE1 0.712 RAE1 0.852 EAE1 0.856
AAE2 0.743 RAE2 0.846 EAE2 0.867
AAE3 0.784 RAE3 0.892 EAE3 0.896
AAE4 0.741 RAE4 0.854 EAE4 0.855
Second Order Factor: Speed of DM agility
Speed of DM agility
Adaptive Speed 0.901
Resilient Speed 0.900
Entrepreneurial Speed 0.851
Second Order Factor :Effectiveness of DM agility
Effectiveness of DM
_agility
Adaptive Effectiveness 0.933
Resilient Effectiveness 0.861
Entrepreneurial
Effectiveness 0.842

 

Principal Component Analysis is used as the

extraction method, and varimax method is used for

rotation.

80

Tab]; 16: Factor analysis for Supply
Chain performance

Service Business
Performance Performance

ServPr1
ServPr2
ServPr3
ServPr4
ServPr5
ServPr6
BusPer1
BusPer2
BusPer3
BusPer4
BusPer5
BusPer6

Principal Component Analysis 15 used as the extraction method, and varimax method is used for
rotation.

 

Analysis Approach

Two separate models were assessed one for each dimension of speed and
effectiveness of DM agility (see Figures 5 and 6). Since the equations were not
independent, simultaneous equation modeling was used for analysis. Three equations (1,
2, and 3; and 4, 5, and 6) were simultaneously estimated in two model runs using three
stage least square (3SLS) procedure. The three stage procedure is efficient as we propose

a model where the endogenous variable in one equation is an exogenous variable in the

other.
SERVPR = [30 Constant + BIAGILSPEED + [32 logSize + [33 Turbulence + cl (1)
BUSPR = [30 Constant + B, AGILSPEED + 82 logSize + 83 Turbulence + 82 (2)

81

AGILSPEED = 130 Constant + B, lTINCO + [32 ITEXSP + [33 ITDMMG + [34 ITMKIN + [35
INTIAQ + B, EXTSPI + [37 MKTINI + [38 CUSRELP+ [59 ITINCO* INTIAQ + Bio ITEXSP“
EXTSPI + B” ITDMMG* MKTINI + [3.2 ITMKIN * CUSRELP + [313 logSize + [314 Turbulence +
[315 Postponement + [3,6 PrdVolume + £3 (3)

and,

SERVPR = [30 Constant + BIAGILEF F ECTIV E+ 82 logSize + [33 Turbulence + 84 (4)
BUSPR = [30 Constant + 81 AGILEFFECTIVE + [32 logSize + 83 Turbulence + 85 (5)
AGILEFFECTIVE = 80 Constant + B. ITINCO + [32 ITEXSP + 83 ITDMMG + 84 ITMKIN + 85
INTIAQ + B, EXTSPI + [37 MKTINI + [33 CUSRELP+ [39 IT INCO" INTIAQ + 1310 ITEXSP"
EXTSPI + 1311 ITDMMG" MKTINI + [312 ITMKIN * CUSRELP + 813 logSize + [in Turbulence +

815 Postponement + [3.6 PrdVolume + 86 (6)

where, SERVPR: Service Performance
BUSPR: Business Performance:

AGILSPEED: Speed of DM Agility
AGILEFFECTIVE: Effectiveness of DM Agility:
ITINCO : lntemal Supply Side IT

ITEXSP : External Supply Side IT
ITDMMG : lntemal Demand Side IT
ITMKIN : External Demand Side IT
INTIAQ : lntemal Supply Side Initiative
EXTSPI: External Supply Side Initiative
MKTINI : lntemal Demand Side Initiative
CUSRELP: External Demand Side Initiative
logsize: Log of Number of employees
Turbulence: Turbulence

Postponement: Extent of Postponement
Prdvolume: Product Volume

In the case of these simultaneous equations the error term obtained from the regression of
DM agility on demand management technology and initiatives 83 (or 86) might be
correlated with the error of regression with performance as the regress and - 31 or 32(or

84 or 85). Thus the OLS might not have correct estimates of the standard errors and the

82

results might be biased. 3SLS is the systems counterpart of two-stage least squared
method (ZSLS) and uses full information while estimation.

Table 17: Reliabilities
Demand Management initiatives

Internal Supply Side Initiatives 0.74
External Supply Side Initiatives 0.82
lntemal Demand Side Initiatives 0.90
External Demand Side Initiatives 0.83

 

Demand Management IT

 

 

 

 

lntemal Supply Side IT 0.78
External Supply Side IT 0.80
lntemal Demand Side IT 0.74
External Demand Side IT 0.68
First Order DM Agility

Adaptive Speed 0.82
Resilient Speed 0.88
Entrepreneurial Speed 0.88
Adaptive Effectiveness 0.73
Resilient Effectiveness 0.88
Entrepreneurial Effectiveness 0.89
Second Order Agility

Speed 0.86
Effectiveness 0.853
Supply Chain Performance

Service performance 0.942
Business Performance 0.887

 

83

Ipble 18: 3SLSan_alvsis of the two models for speed, and effectiveness of DM agilig

 

 

 

 

 

 

 

 

 

 

 

 

 

om AGILITY SPEED our AGILITY EFFECTIVENESS
Complementarities and Complementarities and
Moderation Model Moderation Model
Coeff Std Error Coefl' Std Error
ServPr
DM agility 0.41 *** 0.144 0493*” 0.176
Turbulence -0.022 0.037 -0.04 0.04
Log(Size) -0.06 0.081 -0.003 0.084
_cons 0.161 0.264 0.258 0.287
R Squared 0.06 0.04
Adj R Sq 0.041 0.03
BusPr
DM agility 0.419” 0.14 0497*” 0.162
Turbulence -0.021 0.036 -0.039 0.037
Log(Size) -0.039 0.079 0.013 0.077
_cons 0.137 0.258 0.229 0.263
R Squared 0.09 0.109
Adj R Sq 0.071 0.091
DM
agility
ITINCO 0.148“ 0.073 0.073 0.069
ITEXSP 0228*“ 0.07 0231*“ 0.067
ITDMMG 0.142” 0.069 0.137” 0.065
ITMKIN -0.001 0.07 0.009 0.066
MKTINI 0.149“ 0.067 0.057 0.063
EXTSPI 0.106 0.077 0.05 0.073
CUSRELP 0.135 0.069 0.159“ 0.066
INTIAQI 0.103 0.069 0.053 0.065
Log(Size) 0.02 0.032 0.057 0.032
Turbulence 0.043 0.07 0.045 0.069
ITINCO'leTIAQ 0.037 0.076 0.036 0.071
ITEXSP*EXTSPI 0.004 0.075 0.022 0.071
ITMKIN*CUSRELP 0.05 0.063 0.128“ 0.061
ITDMMG*MKTINI 0.044 0.073 0.071 0.069
Postponement 0.069 0.04 0.054 0.038
Prdvolume 0.165“ 0.056 0.141” 0.054
_cons -1 273*“ 0.368 -1.298*** 0.357
R Squared 0.334 0.287
Adj R Sq 0.256 0.202

 

Note: A total of 150 observations were used for analysis
OLS was used to calculate the r-squared as 3SLS estimates might give erroneous
estimates. All F statistics were found to be signiﬁcant for the regression runs
** p< 0.05
mm P<-01

84

To estimate 3SLS parameters, the transpose of matrix of all observations is multiplied by
each of the equation (similar to that done in 2SLS) and then the set of equations is
estimated jointly. The results of the study are reported in Table 18. Two set of 3SLS
regressions were run, one each for speed of DM agility and effectiveness of DM agility.

For each run, all the three equations were estimated simultaneously.

Performance impacts:
The results indicate that service performance is signiﬁcantly inﬂuenced by both speed of

DM agility (B = 0.41; p>0.01) and effectiveness of DM agility (0 = 0.493; p<0.01).

Table 19: Summg of results of Hypothesis Testing

 

 

 

 

 

 

Service Business
Impacts on supply Performance Performance
Hypothesis chain Performance (Hyp a) (Hyp b)
1 Speed of DM Agility Supported Supported
2 Effectiveness of DM
Agility Supported Supported
Speed Effectiveness
Impacts on Agility (Hyp a) (Hyp b)
Internal Supply Side Supported Not Supported
External Supply Side Supported Supported
Internal Demand Side Supported Supported
Not
3 Technology External Demand Side Supported Not Supported
Not
Internal Supply Side Supported Not Supported
Not
External Supply Side Supported Not Supported
Internal Demand Side Supported Not Supported
Not
4 Initiative External Demand Side Sppported Sgpported
Not
Internal Supply Side Supported Not Supported
Not
External Supply Side Supported Not Supported
Not
Internal Demand Side Supported Not Supported
Not
5 Complementarities External Demand Side Sgpported Stﬂported

 

 

 

 

85

Similarly, business performance is inﬂuenced by both speed (B = 0.419; p<0.01) and
effectiveness (B = 0.497; p<0.01) dimensions of DM agility. Hypothesis 1 and 2 are thus
fully supported (Table 19). For the speed and effectiveness dimensions of DM agility, the
parameter coefﬁcients were estimated simultaneously. The R squared for the two were
0.334 and 0.287 respectively (Adjusted R Square: 0.256 and 0.202 respectively).

Demand Management Information Technology Impacts

The effects of information technology are found to be relatively more signiﬁcant
for the two agile outcomes. For the speed of DM agility, external supply side IT (B =
0.228; p<0.01), internal supply side IT (B = 0.148; p<0.01), and internal demand side IT
(B =0.142; p<0.05) are found to have signiﬁcant impacts. Hypothesis H3a is thus
supported with signiﬁcant impacts of three out of four technologies on the speed of DM
agility. For the effectiveness of DM agility dimension (Hypothesis 3b), external supply
side IT (B = 0.231; p<0.01) and internal demand side IT (B = 0.137; p<0.05) are found to
have positive impacts.

Demand Management Initiative Impacts

The hypothesis 4a and 4b have weak support (for both 4a and 4b one out of four
initiatives were found to have a signiﬁcant impact), with internal demand side initiatives
inﬂuencing speed of DM agility (B = 0.149; p<0.05) and external demand side initiatives
inﬂuencing effectiveness of DM agility (B = 0.159; p<0.05). Other initiatives are not
found to have a signiﬁcant impact on either speed or effectiveness of DM agility.

C omplementarities Impacts

External demand side technologies are not found to have a direct impact on speed

or effectiveness of DM agility; however, their complementarities with the corresponding

86

business initiatives are found to have a signiﬁcant impact for effectiveness (B = 0.128;
p<0.05) (114a and H4b). 4The other complementarities between technology and initiatives
for internal demand side, internal supply side, and external supply side are not found to
be signiﬁcant, suggesting weak support for hypothesis 5a and 5b.

We also ﬁnd signiﬁcant impacts of product volume on both speed of DM agility
(B = 0.165; p<0.05) and effectiveness of DM agility (B = 0.141; p<0.05) and marginally
signiﬁcant impacts of ﬁrm size on the two dimensions of DM agility.
Regression Diagnostics and Robustness Analysis

To test the validity of our results we did various tests. Firstly, we assessed the
normality of the sample used for analysis using the Shapiro Wilks Lambda test. The
residuals were used to assess the normality from both the tests and this was not found to
be a problem in our analysis. Further, to test for the heteroskedasticity, we used the
Breusch-Pagan test and the results rule out any threat to our analysis due to
heteroskedasticity. Finally, we tested the speciﬁcation using the Linktest and found that
the models were appropriately speciﬁed with the given set of variables (hhat was found to
be signiﬁcant at p<.05 for all cases), and there was no gain in speciﬁcation by the
inclusion of another set of variables (hhat squared insigniﬁcant with p>.10 for all cases).
To guard against plausible multicollinearity, we used the ORTHOREG procedure in SAS
and the results were found to be consistent. PROCPOWER procedure was used to assess
the power of the test and the power was found to be good (greater than 0.8). Finally, we
also tested the interaction effects by using the standardized variables and the results are

still found to be same.

 

‘ The external demand side complementarities were assessed as an interaction between external demand
side IT and external demand side initiatives. Similarly, other complementarities were assessed as the
interaction between the respective information technology and demand management business initiative.

87

CHAPTER 5: DISCUSSION AND CONCLUSIONS

The research tests the impacts of four different types of information technologies
and business initiatives used in demand management process on organization’s ability to
respond to changes in customer demand. The four types of information technologies and
initiatives studied in this research are based on two orthogonal dimensions: scope of
technology/initiative and its functionality. Based on these two dimensions, four
categories of IT and business initiatives are identiﬁed — internal supply side, external
supply side, internal demand side, and external demand side. The impacts of these IT
systems and initiatives on the ability of the ﬁrm to respond to customer demand changes
(DM agility) are assessed in this research. Two different dimensions of DM agility are
assessed. Firstly, we assess the speed of DM agility — how fast is the ﬁrm able to respond
to changes in customer demand; and secondly effectiveness - how effective is the ﬁrm’s
response to these demand changes. The role of IT and business initiatives is assessed
separately for these two types of ﬁrm response capabilities. The study ﬁnds the varying
impact of IT, business initiatives, and their synergies across the domains.
External Supply Side

Direct impacts are found to be signiﬁcant predictors of speed and effectiveness of
DM agility for external supply side information technologies. These technologies are
found to enhance bothithe speed and effectiveness of a ﬁrm’s response. Thus, the ability
of the ﬁrm to exchange information and digitally coordinate a process is found to
signiﬁcantly inﬂuence both the speed and effectiveness of their response to changes in
customer demand (Lee et al. 1997). These information technologies that help exchange

information with the external suppliers such (as EDI and XML), and that help a ﬁrm to

88

work simultaneously in real time with external suppliers, synchronize production plans,
are all found to impact the speed and effectiveness with which the ﬁrm is able to respond
to changes in customer demand. Similarly, technologies that aid the collaborative
forecasting and replenishment are signiﬁcant antecedents to DM agility. However, the
impacts of corresponding initiatives are not found to be signiﬁcant. Similarly, we don’t
ﬁnd evidence of a signiﬁcant interaction between the two. There are two possible
alternatives for these missing impacts. Firstly, it is possible that the external supply side
initiatives are hard wired into the technology itself. For example, it is plausible that the
demand forecasting information to be shared is already programmed into the technology
at the time of implementation and the technologies do not have more ﬂexibility to
customize the information that can be shared through them. Future research might test
alternate model speciﬁcations to test these possibilities. The second possibility is that the
value ampliﬁcation in the IT impacts, in the presence of demand management business
initiatives, is not linear in nature. The use of alternate methodologies in future research,
such as that can detect non-linear relationships, might suggest a different outcome (Oh

and Pinnsoneault 2007)

lntemal demand side

Similar to external supply side IT, direct impacts are also found to be signiﬁcant
predictors of speed and effectiveness of DM agility for internal demand side
technologies, i.e. the ability of the ﬁrm to use information technologies for planning,
forecasting, and managing demand is a signiﬁcant predictor of how well the ﬁrm is able

to respond. Speciﬁcally, we examine the use of technologies for special product types

89

such as those with short life cycle, or those are new or are seasonal in nature. This
indicates that effective planning and forecasting activities are critically inﬂuenced by the
use of digital technologies (such as those used for analytics). The impact of internal
demand management initiatives is also found to be signiﬁcant for speed of DM agility
(but not effectiveness of DM Agility). Thus initiatives to acquire information about the
competitor’s actions, business changes, and changing customer preferences from the
marketing department are found to have an increased impact on the speed with which the
ﬁrm is able to respond. Similarly, information regarding changes in marketing strategies
or upcoming marketing events (such as promotions) helps enhance the speed with which
the customer demand can be fulﬁlled. However, the complementary interaction between
the two is not found to be signiﬁcant for either. A possibility for missing impact is that
since the use of these two types of technologies and business initiatives is highly
intertwined, the synergistic impact is subsumed in the direct impacts. For example, while
it can not be claimed that was so the case, it is possible that the nature of information
being collected from the marketing department is based on the capabilities available in
the technologies being used for predicting and forecasting demand. Hence, nothing is
‘hidden’ or unknown and our interaction effect (which assumes this underlying
interaction between the two as a hidden source of value) is not found to be signiﬁcant.
Thus, future research might explore the relative impact of synergistic interaction based on
the ‘intertwinedness’. While our research did not test this element, our results indicate
that this is an important concept to be explored while assessing synergies between
technologies and processes. The presence of this factor will determine whether the locus

of adaptation and the nature of coordination at the time of implementation of technology

90

into the works process have an inﬂuence on the signiﬁcance of complementarities.
Alternatively, the technology and process are located and implemented independently
and their mutual effect would be seen in the interaction. IS research has not explored this
dimension and future research would explore this dynamics related to digital

complementarities.

External demand side

The mutual effect due to co-presence of technology and business initiatives is
found to be signiﬁcant in the case of external demand side IT and initiatives. These
complementarities are found to inﬂuence the effectiveness with which the ﬁrm is able to
respond to the customer demand changes. Thus, ﬁrms are able to respond effectively to
changes in customer demand if they use technologies such as RFID or bar coding
technologies to bring in customer-related information and combine these with active
processing and assessment of customer interests. However, the direct impacts of these
technologies are not signiﬁcant on either the speed of DM agility or effectiveness of DM
agility. Thus, the results indicate that while the technologies such as RFID may not have
any direct impacts on their own, they do contribute to the effectiveness of DM agility in
combination with the corresponding business initiatives, i.e. when the information
brought in by these technologies is complemented by efforts to integrate it with the
demand planning activities. Mere information technologies without any active business
efforts to assess customer preferences or to integrate this information to examine and plan
response to demand changes may not aid the DM agility of the ﬁrm. This is in stark

contrast to the internal demand side and external supply side technologies that are found

91

to have an impact on their own. Prim facie it indicates that the sophistication of the
technology and its life cycle might determine the extent to which the advantages of
process initiatives have been inbuilt into the technology itself. However, more research is
needed to determine the underlying factors that explain the differences in the nature of

impacts across these different technology types.

Internal supply side

Finally, the technologies used in internal supply side for coordination with
production with other functional areas are found to have a positive impact on the speed of
a ﬁrm’s response. Thus, the ﬁrm’s ability to change their product proﬁles through a
digitally-coordinated response within the firm affects its ability to speedily respond to
changes in customer demand. However, these technologies are not found to have a
signiﬁcant impact on effectiveness. The complementary impacts of these technologies in
the presence of corresponding business initiatives are not found to be signiﬁcant either.
The result suggests that following the interaction approach being used in the study, the
hypothesis of synergistic relation between the supply chain technologies used within the
ﬁrm and the corresponding business initiatives is not supported. This indicates that the IT
and business initiative may not have a synergistic relationship. To further explore this
unexpected ﬁnding, we used the alternate alignment (or matching) approach that has been
proposed to study the relationship between contingent variables (Sabherwal and Kirs
1994; Chan et al. 1997; Oh and Pinnsoneault 2007). Using this approach from Oh and
Pinnsoneault (1997), we operationalized the alignment as the absolute difference between

the two contingent variables. These alignment factors were then used instead of the direct

92

impacts of independent variables as the antecedents in our research model (Oh and
Pinnsoneault 1997). The analysis reveals that for internal supply chain technologies the
impact is based on the alignment of these technologies with the corresponding business
initiatives. Alignment effect is not found to be signiﬁcant for any other pair of technology
and initiatives. Thus, while the synergies are not obtained in the case of internal supply
side IT, alignment is the key to realization of the impacts of the two. The technologies in
this domain included the technologies for coordination with the logistics, ﬁnance,
sourcing, and operations departments, and those used for sales and operations planning.
The corresponding initiatives were those used to acquire and integrate operational
information from these other departments and to communicate demand changes to them.
The results of positive impact using the aligmnent approach allude to the importance of
adjustment or matching between the two to synchronize the technology with the process.
Thus, favorable results may not be obtained if the focus is on viewing the two
(technology and business initiatives) as complements. i.e. the impact of one will not
increase in the presence of the other (Milgrom and Roberts 1995). Instead the goal is to
decrease the gap between the two. The greater the gap the worst the performance; very
high use of one and very low use of other is better than the moderate use of both as the
alignment is better in the latter case. While it is beyond the examination being done in
this research, it would be worthwhile to explore the role of organizational culture and
relative power dynamics between departments in inﬂuencing this alignment effect in case
of the internal supply side technologies and initiatives.

To summarize, while external supply side IT and internal demand side IT have

predominantly direct impacts, the impact of the other two types are contingent in nature.

93

While external demand side IT have synergistic inﬂuence with corresponding initiatives,
the internal supply side IT have matching or alignment relationship with the
corresponding initiatives. Thus, the results indicate that alternate pathways characterize
the way IT impacts accrue in a given process. Assuming all technologies to have similar
patterns of impacts may be a broad and unrealistic assumption. The reasoning, similar to
the earlier assumption that was made with regards to the overall impacts of IT
investments on ﬁrm performance, may not hold. Different types of IT systems might
have very different pathways in which their impacts are realized on agile performance.
While some of the IT systems might have a direct impact, others are found to have an
impact only when considered with the corresponding business initiatives. Further,
alignment perspective (studied for internal supply side technologies and initiatives) also
corroborates the difference in the nature of relationship between the technology and
initiatives. While we have indicated the presence of these differences, future research is
needed to more thoroughly tease out how different perspectives pan out across different
technology types and complements. Before elaborating on the contributions and

implications of the study it is worth while to elaborate on its limitations.

LIMITATIONS

The study has two key limitations. Firstly, the data for the study was collected by
surveying only a single respondent (the demand planning manager) within each
organization. There might thus be respondent bias that cannot be ruled out. The dual

respondents within organizations help reduce the variability due to personal preferences.

94

However, given that demand management is a niche ﬁeld within a ﬁrms, we had to
forego this option. While the research results would have been more robust in light of a
dual response design, we believe that the results. make signiﬁcant contributions by
exploring an area of research that has hitherto been very hard to study primarily due to
the limited availability of people in this ﬁeld of work. Thus, our single respondent design,
while not ideal, helps optimize on the tradeoff between rigor and relevance for the
context of demand management being studied in this research.

The second limitation of this research is that the research uses subjective
measures for assessing agility. As common with survey approach, this leaves the
possibility of differential interpretation by different ﬁrms. Though a pilot test was done to
test the respondent, understanding the threat can never be ruled out in subjective
measures. The objective measures to assess how well the ﬁrm responds was ruled out
because of two primary reasons. First, given the unique deﬁnition of constructs, the
record keeping of the ﬁrm of these measures is highly limited. Secondly, the comparison
of these measures across ﬁrms would be highly erroneous due to different methodologies.
Thus, while objective measures are a better indicator of true performance, in this context
our subjective measures might do a better job of consistently assessing agility across the
ﬁrms. Further, past research has concluded that managerial assessments are consistent
with objective internal performance and also with external secondary data (Dess and

Robinson 1984, Venkataraman and Ramanujam 1986).

 

5 Author himself has worked in the domain and this assertion is based on his personal knowledge of the
industry standards.

95

CONTRIBUTIONS

The research makes three key contributions to the IS literature. Firstly, it tests the
role of different types of IT applications and differentiates four different types of
complements. Results indicate that differences across technology types are important to
consider. While prior research has focused on classiﬁcation such as infrastructural IT,
transformation IT, etc. (Aral and Weill 2007), this research looks at the role of four
different functional applications in the context of demand management. The research not
only establishes the contribution of IT to the demand management process but also
highlights the nature of impacts of four different types of IT systems relevant to the
demand management process.

Secondly, this research assesses DM agility not just as the speed of DM agility
but also as the effectiveness of DM agility . As the results indicate, two types of IT
(lntemal demand side IT and External supply side IT) enhance both speed and
effectiveness. Also, while internal supply side IT enhances speed of DM agility, they do
not have any inﬂuence on the effectiveness. Similarly, neither speed nor effectiveness is
inﬂuenced by the external demand side IT. However, effectiveness is inﬂuenced by the
joint use of both external demand side technologies and initiatives. Thus, the research
contributes by establishing different dimensions of DM agility, and more importantly, by
assessing how different IT types have different effects on these two dimensions. While
some IT systems might impact speed, others might impact effectiveness and a subset
might have inﬂuence on both. Similarly, the results indicate that synergies between IT
and initiatives may be important for effectiveness even when they are not found to be

signiﬁcant for speed of DM agility. We hope these ﬁndings will move the research

96

beyond mere assessment of how fast the ﬁrm was able to respond and explore the
antecedents of the effectiveness dimension of a frrm’s response as well.

Finally, the research establishes the impact of DM agility on supply chain
performance. We ﬁnd positive impacts for both the speed of ﬁrm response and the
effectiveness of ﬁrm response on the overall performance of the supply chain. The
impacts are found to be signiﬁcant for both the service and overall business performance.
Given the widespread interest and attention to agility in the contemporary research
(Sambamurthy et al. 2003), the empirical evidence demonstrating these effects is
signiﬁcant and has been overlooked till now. This result linking DM agility to ﬁrm
performance will give further impetus to research that links the factors under which the
agility might impact ﬁrm performance. Thus, this research would form the basis for
future research to explore contingency factors that determine the role of agility in the ﬁrm

performance.

MANAGERIAL IMPLICATIONS

The study has important implications to help managers in the ﬁeld of demand
management and order fulﬁllment. Given the increased competition in business markets,
customer demand has increasingly gained greater signiﬁcance. Ironically, the research
focus has been limited to enhancing the performance of a ﬁrm’s supply side and relations
with partners. Given the very niche, yet highly important, role played by demand
management, this research adds new knowledge that is highly valuable due to the limited

scope of improvements by realizing supply side efﬁciencies.

97

Further, the research contributes to the managerial understanding of the role of
information technologies in realizing greater responsiveness to changes in customer
demand. Given the complexity identiﬁed by researchers in leveraging IT, the
establishment of value pathways helps managers focus on appropriate actions required to
realize value out of their IT investments. Siggelkow (2002) emphasizes that the
knowledge of complex interactions between organizational variables is often internalized
by managers for quick actions in compressed time frames that are typical of
organizational work. Thus, our research identiﬁes the critical pathways to value. For
managerial relevance, the results indicate that while some technologies might have a
direct impact, others might amplify responsiveness only in the presence of corresponding
business initiatives. Speciﬁcally, we ﬁnd that the internal demand side and external
supply side technologies have direct impacts while the interaction with corresponding
business initiatives is important for the internal supply side and external demand side
technologies. While the internal supply side technologies need to be aligned with the
corresponding business initiatives, the impact of external demand side is interactive and

complementary in nature.

In conclusion, given the enhanced focus on process level of analysis, this research
assesses the IT systems that lead to the creation of DM agility in the demand
management process. The study ﬁnds that different types of IT systems have very
different impacts on the performance of a ﬁrm’s ability to respond to changes in customer
demand. The nature of complementary interaction with the process element varies

according to the nature of the technology as well. Further, the study also shows which IT

98

systems and their interactions are signiﬁcant for speedy and effectiveness of DM agility
respectively. Finally, the research empirically establishes the performance impacts of a

ﬁrm’s response capabilities, and will form the basis for future research on this topic.

99

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