LOGISTICS TECHNOLOGY IMPLEMENTATION PROCESS: A CAUSAL
MODEL
Abdullah Said Al Hajri*
School of Mechanical and Manufacturing Engineering, UNSW, Kensington, Australia
Email:z3115638@student.unsw.edu.au
Dr. Maruf Hasan
School of Mechanical and Manufacturing Engineering, UNSW, Kensington, Australia
Email: m.hasan@unsw.edu.au
Preferred Stream: Stream 16
Profile: A. Al Hajri is a fourth year PhD student in the School of Mechanical and Manufacturing
Engineering of University of New South Wales. He holds a master degree in transportation and
logistics management from the University of Arkansas, USA and a bachelor degree in operations
management from Sultan Qaboos University, Sultanate of Oman. He has over three years experience
in teaching operations management and statistics courses to first and second year students. He has
published two conference papers in ANZAM conference 2006. His current research interests include
adoption and implementation of logistics technology, utilization of technology benefits and supply
chain management network optimization.
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LOGISTICS TECHNOLOGY IMPLEMENTATION PROCESS: A CAUSAL MODEL
ABSTRACT
The resultant integrative nature of logistics technology as a response of
moving toward an integrated logistics concept makes successful
implementation of this technology a challenge. The current research begins
the task of addressing this issue by adopting and expanding a theoretical
model (Klein and Sorra 1996) from literature which has characteristics
similar to logistics technology implementation process. The research uses
data collected from Australian companies regarding the implementation of
recently adopted logistics technology in their organizations. The results
provide strong support for most of the relationships in the research model and
valuable insights for logistics and supply chain managers.
Keywords:
Managing technological/organizational change, Supply chain management,
Partial least squares, innovation-values fit, climate.
INTRODUCTION
The resultant integrative nature of logistics technology as a response of moving toward an
integrated logistics concept (Dawe 1994; Germain, Droge and Daugherty 1994) makes successful
implementation of this technology a challenge (Klein and Knight 2005; Klein and Sorra 1996).
Further, failure to implement this technology successfully could result in writing off major
investments in developing and implementing the technology or even in abandoning the strategic
initiatives underpinned by these innovations (Russell and Hoag 2004; Xue, Liang, Boulton and
Snyder 2005). Therefore, it is important to find out why some companies are more successful in
implementing the technology compared to others. This information should help managers draw the
path to better utilization of logistics technology (Lippert 2007).
Despite the early recognition of the challenge placed by the implementation of logistics
technology (Hall and Vollmann 1978; Miller and Sprague 1975; Schroeder, Anderson, Tupy and
White 1981), successful implementation is still of major concern (Nilsson 2006; Spekman and
Sweeney II 2006). An example of the challenge placed by logistics technology in recent years is the
adoption and implementation of the Radio Frequency Identification (RFID) Technology. A recent
study on RFID in the warehousing industry by Vijayaraman and Osyk (2006) found that high
percentage of respondents are not currently considering RFID technology and the reasons are the
potential for RFID to deliver cost savings or a positive ROI in the near future. While those companies
that are either considering or are actually implementing RFID technology are doing so because of the
compliance requirement from large retailers. On the contrary, Spekman and Sweeney II (2006) found
that RFID can deliver the benefits it promises, however this requires the commitment rather than the
simple compliance in logistics. Several other examples are available in literature which show that
despite the ability of logistics technology to deliver the promised benefits (Hitt, Wu and Zhou 2002;
Lummus and Duclos 1995; Sari 2007; Williams 2000), the path toward these benefits is often not
clear (Gattiker 2002; Kurnia and Johnston 2003; Xue et al. 2005).
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In this paper, we aim to shed light over the process of logistics technology implementation
through connecting the antecedents and outcomes of this process using a rationale approach adopted
from implementation process literature. Further, we intend to examine the generalization of the effects
of these antecedents on the outcomes over several logistics technologies.
The paper proceeds as follows. First, literature review of the implementation process studies
and the theoretical advancements offered by these studies are presented.
Second, the adopted
theoretical approach from implementation process literature for the application to logistics technology
is explained. Third, the research model is explained. Fourth, the methodology used to test the model
is presented. Fifth, the results are presented and findings are discussed. Finally, implications for
practice and research are drawn.
LITERATURE REVIEW
To understand implementation process literature, one should first define implementation as it
was used in previous studies. Implementation scholars differed about the start and the end of the
implementation process. One group considered the selection of the software package as part of the
implementation process (Gross and Ginzberg 1984; Lucas, Walton and Ginzberg 1988). A second
group considered implementation process to encompass the entire process from the original
suggestion to installation of the system (Lucas 1981: 14; Sabherwal and Robey 1993). A third group
(Klein and Sorra 1996; Leonard-Barton and Deschamps 1988) defined implementation process as “the
critical gateway between the decision to adopt the innovation and the routine use of an innovation.”
This group view implementation as one of last steps in the user-based models of the innovation
process (Linton 2002). User-based models follow innovations from initial awareness of an innovation
by a potential user to the time when the innovation is routinely employed by the user in a process
(Kwon and Zmud 1987; Rogers 2003; Thomson 1969; Tornatzky and Klein 1982; Zaltman, Duncan
and Holbek 1993). A recent study by Al Hajri and Hasan (2006) found that the selection of software
package of logistics technology falls after the adoption decision when the need for the technology is
initiated by an external factor (e.g. an order from headquarters or pressure from trading partner).
Therefore, implementation will be defined in this study as the period from which a formal decision by
top management to adopt the technology has already been made and the technology vendor has
already been selected to the routine use of it. This definition is very similar to the definition given by
the third group except for the explicit exclusion of the vendor selection process from implementation
process.
Implementation research is also governed by a set of assumptions which vary from study to
study depending on various factors such as the nature of the technology, the definition of innovation,
the users of the technology, and the use of the users. Logistics technology is perceived in this study as
“a technology or a practice being used for first time by members of an organization or organizations,
whether or not other organizations have used it previously” (Klein and Sorra 1996; Nord and Tucker
3
1987: 6). Given the integrative nature of logistics technology, use of logistics technology requires a
formal adoption decision at the management level and the accruing benefits from the technology
require the collective use of the affected users of the technology whether they are in the logistics
department or other departments or departments in other organizations. For a complete discussion of
the assumptions which apply to logistics context, the reader can refer to Klein and Sorra (1996) and
Leonard-Barton and Deschamps (1988).
The research on implementation process has been around for about four decades (Churchman
and Schainblatt 1965; Ginzberg and Schultz 1987).
A meta-analysis study of Management
Information Systems (MIS) research on implementation by Lai and Mahapatra (1997) found a
growing trend in the number of articles published since 1977. The analysis also indicated that
implementation research is maturing and using more theory building methods. A close examination
of the history of implementation research shows that seventies and eighties were periods of building
and testing several implementation models (Ginzberg, Lucas and Schultz 1986; Schultz, Ginzberg and
Lucas 1983).
Linton (2002) described previous efforts as unsuccessful in generating a
generalizeable theory (Downs and Mohr 1976; Klein and Sorra 1996; Wolfe 1994) since so many
firms’ efforts are either complete or partial failures. Earlier, Klein and Sorra (1996) attributed this to
the nature of previous studies of implementation where most of them have single site, qualitative case
studies of implementation. Each of these studies describes pieces of the implementation story. More
recently, Jacobs and Bendoly (2003) explained that “when research surrounds an emerging
technology it can be all too easy to pursue surveys that ultimately neither contribute to or test
academic theory, but rather simply provide descriptive statistics that capture the current state of a
popular idea.”
Notably, the building and testing periods of implementation models have resulted in two
implementation models which are receiving much of the attention from recent studies in this field.
These two models are the Technology Acceptance Model (TAM) (Davis 1986) and the
Implementation Effectiveness Model (Klein and Sorra 1996). TAM is an adaptation of Theory of
Reasoned Action (TRA) (Ajzen and Fishbein 1980) and specifically tailored for modeling user
acceptance of information systems (Davis 1989; Davis, Bagozzi and Warshaw 1989). TAM posits
that two beliefs, perceived usefulness and perceived ease of use are of primary relevance for computer
acceptance behavior. Perceived usefulness is defined as the prospective user's subjective probability
that using a specific application system will increase his or her job performance within an
organizational context. Perceived ease of use refers to the degree to which the prospective user
expects the target system to be free of effort. TAM has several strengths including its specific focus
on Information System (IS) use, its base in a theory of social psychology, the validity and reliability
of its instruments and its parsimony. Also, the model performs well empirically (Adams, Nelson and
Todd 1992; Amoako-Gyampah and Salam 2004; Davis 1986; Davis et al. 1989; Mathieson, Peacock
and Chin 2001).
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Implementation Effectiveness Model defines implementation effectiveness as the
consistency and quality of targeted organizational member’s use of a specific innovation.
Implementation effectiveness results from the dual influence of an organization’s climate for
implementation of a given innovation and the perceived fit of that innovation to targeted users’
values. In comparing the two models, TAM inherently assumes that use of information systems
technology is volitional, that is, there are no barriers to prevent an individual from using an
information system if he or she chose to do so (Mathieson et al. 2001). Another limitation of this
model is the level of analysis. The model is based on the perceptions of individual users of an
information system (Davis et al. 1989). Despite the existence of some attempts to introduce some
managerial interventions to the model (Amoako-Gyampah and Salam 2004; Mathieson et al. 2001),
the volitional assumption is directly related to the dependent variable of the model (i.e. Behavioral
intention to use the information system). However, this assumption doesn’t hold true in logistics
technology given the fact that the technology is adopted based on a formal adoption decision by top
management and the fact that several companies adopt the technology in a compliance basis.
Implementation effectiveness model, in contrast, relaxes those two limitations by introducing the two
main predictors of the model (i.e. climate and innovation-values fit) (Klein and Sorra 1996). The two
constructs describe the collective targeted users’ perceptions at the organizational level in which
climate encompass the collective perceived actions of management toward the implementation of the
technology whereas innovation-values fit describes the perceived organizational and group values
toward the implementation of the technology.
The direct influence of management
support/intervention on technology implementation success is one of the few consistent findings that
scholars agreed on over the past research (Leonard-Barton and Deschamps 1988; McAulay 1987; Nutt
1986; Schultz et al. 1983: p. 14; To and Ngai 2006)
Additionally, the authors conceptualize
innovation use as a continuum, ranging from avoidance of innovation use (non use), to meager and
unenthusiastic use (compliant use), to skilled, enthusiastic, and consistent use (committed use).
Therefore, we would like to introduce Klein and Sorra’s (1996) implementation effectiveness model
to logisticians as a suitable model for implementation of logistics technology.
IMPLEMENTATION EFFECTIVENESS MODEL
The fundamental challenge of organizational innovation implementation is to gain the use of
all targeted organizational members or a critical group of them. Thus, implementation effectiveness is
an organizational level construct which describes the overall or pooled use of innovation by
organizational members. Further, the benefits that the organization gains from implementing the
innovation depend on the overall and coordinated use of organizational members.
Thus,
implementation effectiveness is a homogenous construct describing the quality and consistency of a
specific innovation within the organization as a whole. Implementation effectiveness is considered a
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necessary, although not necessarily sufficient, condition for innovation effectiveness or organizational
performance due to innovation (e.g. improvements in productivity, customer service, and inventory).
Implementation effectiveness is a dual result of climate and innovation-values fit.
Organization Climate for implementation of a given innovation describes users’ shared summary
perceptions of the extent to which their use of the innovation is rewarded, supported and expected
within the organization. According to Klein and Sorra (1996), strong organization climate fosters use
by a) ensuring employee skill in innovation use, b) providing incentives for use and disincentives for
innovation avoidance, and c) removing obstacles to innovation use. Innovation-values fit describes
the extent to which the users perceive that innovation use will foster the fulfillment of their values.
Therefore, innovation-values fit is good when users regard the innovation as highly congruent with
their high intensity values.
The combined influence of varying levels of climate and innovation-
values fit results in varying levels of innovation use. For example, user resistance arises mainly
because of the existence of strong climate for implementation and poor innovation-values fit.
The implementation effectiveness model has feedback processes that connect organization’s
performance due to innovation use to the climate and innovation-values fit constructs. The authors
posit that the increase or decrease in performance affects subsequent climate for implementation and
employees values (Repenning 2002). For example, if the use of innovation increased organizational
performance, management support (i.e. climate) will increase and employees’ values will be
reinforced.
Current research on implementation effectiveness model shows only partial support for its
proposed relationships. Klein, Conn and Sorra (2001) tested the implementation effectiveness model
using a sample size of 39 companies except for one predictor; innovation-values fit which they didn’t
give justification for its exclusion.
In addition, the authors studied one antecedent of climate;
implementation policies and practices and two antecedents of the later; management support and
financial resource availability. Using a multilevel study of the implementation of Manufacturing
Resource Planning (MRPII), the authors found significant relationships for all the variables except for
management support.
Instead, management support was found to have a direct significant
relationship with climate. Holahan, Aronson, Jurkat, Schoorman (2004) examined the empirical
validity of the implementation effectiveness model through a multi-organizational study of the
implementation of computer technology in science education using a sample of 69 schools. The study
found a significant positive relationship between climate and implementation effectiveness and a nonsignificant relationship between innovation-values fit and implementation effectiveness. The authors
justified the non-significant relationship as an indication of absence of sufficient variance in the
innovation-values fit variable to capture the different levels of fit. In addition, the authors tested the
effect of receptivity to change on climate as a way to extend the model and a proposed variable in
explaining implementation effectiveness. The relationship was found significant.
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RESEARCH MODEL
A goal of our effort was to examine the influence of antecedent variables on the previously
mentioned Effectiveness Implementation Model constructs of Climate and Innovation-values fit.
Studying the influence of external variables on the constructs not only contributes to theory
development, but also helps in bringing to logistics/supply chain managers issues which are of
emergent concern in improving utilization of logistics technology.
Our model is shown in Figure 1. The model has as its core the Implementation Effectiveness
Model constructs and postulated relationships. We hypothesized that two external variables,
Education and training and importance of need/problem, would influence Climate and Innovationvalues fit and therefore Implementation effectiveness.
Need
Climate
Implementation
effectiveness
Education
Performance
Innovationvalues fit
Figure1: Research Model
Implementation effectiveness
Implementation effectiveness of technology is considered a necessary, although not sufficient,
condition for improvements in performance due to technology adoption (Klein and Sorra 1996).
Previous studies which investigated the effect of technology use on performance found significant
positive relationship across several productivity and performance measures. Wu, Zsidisin and Ross
(2007) found coordinated use of e-procurement application is significantly related through direct and
indirect effects on perceived efficiency gains. Grovera, Tenga, Segarsb and Fiedler (1998) found that
the use of Electronic Data Interchange (EDI) is significantly related to perceived productivity.
Rogers, Daugherty and Ellinger (1996) found firms utilizing more warehousing information
technology had significantly higher performance in the areas of quality improvement, cycle time
reduction, and productivity improvements. Hence,
Hypothesis 1. Implementation effectiveness has a positive relationship with performance
Climate for Implementation
A number of polices and practices have been proposed in literature as related to successful
implementation of logistics technology e.g. adequate training and technical support (Ang, Sum and
Chung 1995; Ormsby, Ormsby and Ruthstrom 1990; Sum, Ang and Yeo 1997), project management
orientation to implementation (Parr and Shanks 2000; Wasco, Stonehocker and Feldman 1991;
Westen 2001), executive involvement (Mabert, Soni and Venkataramanan 2003), talented and
7
competent champion (Emmelhainz 1988). A series of studies in psychology have concluded that it is
not a specific set of policies and practices that lead to a specific behavior, but rather it’s the perception
of the available climate which serve as a frame of reference for coherent set of adaptive behaviors
(Schneider 1975). Further, this climate is based on the collective intensity of those polices and
practices. Based on this, Klein and Sorra (1996) posited that climate for implementation affects
implementation effectiveness. Hence, we propose that:
Hypothesis 2. Climate for implementation has a positive relationship with implementation
effectiveness of logistics technology.
Innovation-Values Fit
Innovation-values fit represents the volitional part of technology implementation process
(Holahan et al. 2004).
Leonard-Barton and Deschamps (1988) propose successful innovation
implementation depends on the acceptance of the innovation by targeted end-users where these users
evaluations of the innovation are highly influenced by the collective perceptions of group or
organizational values toward innovation use (Klein and Sorra 1996). Past studies weren’t consistent
about the relationship between fit and implementation success; with some studies getting negative or
insignificant relationships (Cooper and Zmud 1989; Cooper and Zmud 1990; Holahan et al. 2004) and
others getting significant positive relationships (Hong and Kim 2002; Leonard-Barton 1988;
Premkumar, Ramamurthy and Nilakanta 1994; Rogers 2003). Due to the positive direction of the
postulated relationship between innovation-values fit and success and the larger number of studies
which show a significant positive relationship with success, we posit that:
Hypothesis 3. Innovation-values fit has a positive relationship with implementation
effectiveness
Importance of Problem/Need
The recognition of a genuine internal need within an organization is believed to occur when
Logistics/Supply Chain Manager perceives various forms of performance gaps in the internal or
external operating environment which inhibit further improvement in performance and lead to a
feeling of urgency to address these issues to mitigate adverse influences (Ramamurthy 1995).
Therefore, it is logical to expect that when managers are able to see deficiencies, they will seek to use
all the resources at their disposal to ensure that their solution is properly implemented to address the
identified deficiencies (Meredith 1981). Premkumar and Ramamurthy (1995) found that internal need
is one of the factors that significantly differentiated between firms which are proactive in their
decision to adopt EDI from reactive ones. Importance of clear definition of the needs of managers has
been recognized early in the implementation literature (Wolek 1975). In testing their integrative
model of implementation, Ginzberg et al. (1986) found urgency of need is one of the few factors that
affected use. Due to the apparent influence of need on management, we propose that:
Hypothesis 4. Importance of need has a positive relationship with climate
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Education and Training
Several studies have listed education and training as a critical success factor in
implementation studies (Harold 1997; Motwani, Mirchandani, Madan and Gunasekaran 2002; Somers
and Nelson 2001; Sum et al. 1997). Education and training was found important in showing people
the reason for the change and to help overcome the fear from computers (Sum et al. 1997). Linton
(2002) found through review of literature in implementation research that training and informal
learning reduce employee resistance by decreasing the threat and newness of an innovation.
Torkzadeh and Dwyer (1994) empirically found that computer user training has a direct relationship
with usage and indirect relationships through user satisfaction and user confidence. In discussing the
mechanism by which top management can support the implementation of computer systems, Meredith
(1981) found that this support must be meaningful. Top managers must be knowledgeable about the
difficulty of the project and the level of time and resources that are required to support it. One way to
accomplish this is through educating top management (Humphreys, McCurry and McAleer 2001;
Ormsby et al. 1990; Towers, Knibbs and Panagiotopoulos 2005). Duchessi, Schaninger and Hobbs
(1989) added that through education companies become aware of the need to develop formal policies
and procedures and apply formal project planning methods. Also, it stimulates managerial support by
helping personnel understand their roles and responsibilities. Hence, we posit that education and
training has a dual effect on climate and innovation-values fit.
Hypothesis 5a. Education and training has a positive relationship with climate
Hypothesis 5b. Education and training has a positive relationship with innovation-values fit
RESEARCH METHODOLOGY
Survey Design
A questionnaire survey was adopted as the key methodology for this study which used a five
point Likert scale (1= Not at all, 5= to a great extent). The choice of this methodology is best
supported by its use in the above studies and because the purpose of the study is to test and extend a
theory. In designing the survey instrument, several problems or discrepancies that shaped previous
research on innovation process have been alleviated. First, innovation-in-an-organization was used as
the unit of the analysis not the organization (Downs and Mohr 1976). This was achieved through
asking questions about a single innovation in each organization and this innovation shall be
determined by the respondent in each company.
Second, the questionnaire, as part of a larger study on initiation, adoption, and
implementation of logistics technology, sought to document several variables related to technology
adoption implementation process, such as whether the technology was developed in house or off-theshelf, the current stage in the adoption process and whether the technology is implemented within a
single organization or spans over several organizations (Wolfe 1994). Additionally, the questionnaire
was designed to capture the dynamic nature of the adoption and implementation process (Ginzberg
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1978). Therefore, each response is considered a valuable profile and documentation of a significant
portion of the adoption and implementation of a specific logistics technology within a single
organization.
Several studies in literature called for deciding priori whether a construct should be measured
using formative (causal) or reflective indicators (Diamantopoulos 1999; Jarvis, Mackenzie and
Podsakoff 2003; MacCallum and Browne 1993; Mathieson et al. 2001). According to Chin and
Newsted (1999: 310), reflective indicators are viewed as being influenced or affected by the
underlying latent variable, while formative indicators are viewed as causing rather than being caused
by the latent variable. This leads to one major difference between the two. Reflective indicators are
expected to correlate highly among each other because they are influenced by the same unobservable
construct whereas formative indicators can have positive, zero, or negative correlations with one
another because a change in one indicator doesn’t necessarily imply change in other indicators
(Haenlein and Kaplan 2004). Using this rule, each of the constructs initially developed for the
questionnaire was subjected to a theoretical justification of whether it should be measured using
reflective or formative indicators.
Measurements of Research Model
Importance of need: Based on the reviewed literature, we found that the managerial
perception of the importance of the problem rather than the problem itself is proposed to have a
significant effect on clime (Meredith 1981). Therefore, we operationalized importance of need as the
perception of management that the need is 1) crucial, 2) current, and 3) requires a significant amount
of resources. Three items adopted from literature were used to reflect this underlying construct. The
items of this construct as well as the items for subsequent constructs are listed in the appendix.
Education and training: Due to the proposed dual affect of education and training on climate
and innovation-values fit, the construct was developed to capture the different layers which will be
affected by the technology implementation project. Therefore, the items used measured the extent to
which the education and training covered the different layers within the organization (i.e. top
management, middle management, users of technology) as well as the variety of media used such as
courses and seminars. This last item captures the quality of the education and training program.
Climate: According Klein and Sorra (1996), organization climate fosters use by a) ensuring
employee skill in innovation use, b) providing incentives for use and disincentives for innovation
avoidance, and c) removing obstacles to innovation use. Further, the authors noted in Klein et
al.(2001) that several organizations can have the same level of climate by using a combination of
different policies and practices. Therefore, in order to avoid missing any policies or practices, we
operationalized the construct of climate using a more abstract level which captures the overall effect
of these policies and practices across different organizations. In doing so, we used the above three
factors to capture the climate construct. Additionally, we used accessibility of technology because it
was mentioned in the above sources as related to climate.
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Innovation-values fit: Using the same approach of operationalizing the climate construct,
innovation-values fit was modeled in an abstract level to capture the overall congruence between
logistics technology and organizational, department, and other departments’ values.
Implementation effectiveness: This construct goes beyond the normal use of technology as
was used in previous studies. It also includes enthusiastic, coordinated and creative use of technology.
Therefore, it captures the initial as well as the sustained or routinized use of technology (Kwon and
Zmud 1987; Zaltman et al. 1993). The operationalizations used were designed to cover these different
uses of the technology. A response of high on one of the items doesn’t necessarily mean high on
others.
Innovation effectiveness (performance): A set of six performance indicators from logistics
literature was used. The literature on benefits due to logistics technology adoption was consulted.
The benefits or performance indicators used cover different array of logistics technologies as well as
short vs. long term benefits.
Sample and Survey Administration
The developed questionnaire was pre-tested with five logistics managers which resulted in
several changes such as deletion in some of the indicators or shortening of the instrument. Following
that, the questionnaire was sent to 1,000 companies randomly selected from the database KOMPASS.
A process of using second mailing yielded a total of 35 questionnaires. The non-response rate was
attributed to the length and detail of the questionnaire because it contained other items related to the
adoption and implementation of logistics technology. Four companies were deleted because they
haven’t started implementing the technology or they were during implementation resulting in an
effective size of 31 companies.
Using the complete sample size, 66% of respondents are manufacturers, 31% are distributors
and the rest are retailers. Further, 55% of the respondents adopted organizational type of logistics
technologies (e.g. Enterprise Resource Planning (ERP)) and 45 % of them adopted interorganizational technologies (e.g. Electronic Data Interchange (EDI)). Respondents came from a
variety of industries with the electrical (17%) being the highest, followed by automotive (9%), fast
moving consumer goods (FMCG) (9%), and chemical (9%).
Validity and Reliability
The instrument was tested for various validity and reliability properties. Two types of
validities were checked; content and construct validities. Content validity assesses whether the
measurement model is sound and complete. As was discussed above, the questionnaire was pre-tested
to remove any ambiguities and to check the relevance of the questions to logistics managers’
activities. Several items were removed because they weren’t relevant to logistics managers. In
addition, the terminology used in some questions was changed to be easily understood by potential
respondents.
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Construct validity was assessed using convergent and discriminant validities.
While
convergent validity evaluates if all the items measuring a construct cluster together and form a single
construct, discriminant validity measures the degree to which a concept differs from other concepts
and is indicated by the items not correlating highly with other measures from which it should
theoretically differ.
Tenenhaus, Vinzi, Chatelin and Lauro (2005) indicated that only reflective indicators are expected
to be unidimensional or show convergent validity while formative indicators can be multidimensional.
To check the unidimensionality of the reflective indicators the authors suggested the following.
1. Principal component analysis of a block: A block is essentially unidimensional if the first
eigenvalue of the correlation matrix of the block indicators is larger than 1 and the second one
smaller than 1, or at least very far from the first one.
2. Cronbach’s alpha: A block of indicators is considered as unidimensional when the
Cronbach’s alpha is larger than 0.7.
3. Dillon–Goldstein’s ρ: A block is considered as unidimensional when the Dillon–Goldstein’s
is larger than 0.7. This statistic is considered to be a better indicator of the unidimensionality
of a block than the Cronbach’s alpha.
Based on the above, only three constructs need to be checked for unidimensionality; climate,
innovation-values fit and importance of need. Table one shows the results for the three measures. All
the statistics presented show an acceptation of the unidimensionality of all the reflective blocks except
for climate, which has a Cronbach’s α value of slightly less than 0.70. Given the above rule, this
shouldn’t cause a serious problem, because Dillon-Goldstein’s ρ value is more than 0.70.
Table 1: Check for block unidimensionality
Block
First Eigenvalue Second Eigenvalue Cronbach’s α Dillon–Goldstein’s ρ
Climate
2.247
0.802
0.697
0.829
Innovation-values fit
1.840
0.266
0.920
0.950
Importance of need
1.711
0.485
0.794
0.883
Discriminant validity was assessed by checking the cross loading of reflective indicators on
other indicators. Table 2 shows that all indicators have high discriminant validity except for climate
item #4, which relates to the accessibility item. This indicates that the item is tapped to a different
construct. Therefore, the item was removed from further analysis. The reliabilities of the reflective
constructs were assessed using Cronbach’s alpha. The updated results after deleting the accessibility
item show an improvement in the alpha value of climate construct from 0.697 to 0.765 and sufficient
reliability for other constructs.
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Table 2: Cross Loadings of reflective indicators
Indicators
Climate1
Climate2
Climate3
Climate4
Fit1
Fit2
Fit3
Need1
Need2
Need3
Climate
0.822
0.881
0.783
0.383
0.610
0.564
0.376
0.531
0.569
0.596
Fit
0.431
0.436
0.387
0.506
0.969
0.935
0.892
0.253
0.173
0.071
Need
0.631
0.503
0.624
0.168
0.283
0.253
-0.020
0.798
0.852
0.882
MODEL TESTING AND ANALYSIS
Partial least squares (PLS) was used to analyze the results (Lohmöller 1984; Wold 1982).
PLS is a general method for the estimation of path models involving latent constructs indirectly
measured by multiple indicators. As a components-based approach, PLS allows for the use of both
formative and reflective measures, which is not generally achievable with covariance-based structural
equation modeling techniques such as LISREL or EQS (Chin 1998; Chin and Gopal 1995; Chin and
Newsted 1999). PLS software used here is XLSTAT Version 2007.61.
Figure 2 presents the structured results of fitting the model to the data. The multiple R2
values given for each dependent construct are equivalent to the values derived from regression,
showing fraction of total variance in a dependent construct that is accounted for by those independent
constructs impacting it.
All the proposed relationships were found significant except for the
relationship between innovation-values fit and implementation effectiveness which is consistent with
Holahan et al.(2004). Holahan et al.(2004) attributed this to the absence of sufficient variance in the
0.616**
Need
Climate
(R2=0.555)
0.458*
Implementation
effectiveness
(R2=0.315)
0.313*
Education
0.601**
Innovationvalues fit
(R2=0.361)
0.700**
Performance
(R2=0.490)
0.169
** p<0.01 *p<0.05
Figure2: Results of PLS Model
predictors of innovation-values fit which can detect the different levels of implementation
effectiveness outcomes. Upon checking the data, we found that the average coefficient of variation
(i.e. standard deviation/mean) of the three predictors of fit (.22) is approximately the same as the
average coefficient of variation for the three predictors of climate (.25) which indicates that the
1
The software can be download from http://www.xlstat.com
13
insignificant relationship is not attributed to the absence of sufficient variance in the data. Building
on our discussion that innovation-values fit represents the volitional state or behavioral intention to
use the system, we suggest that measures of this construct should be based on the Technology
Acceptance Model’s (TAM) concept. In this model, behavioral intention is the result of perceived
usefulness and attitude toward using the logistics technology.
PLS doesn’t generate a single goodness of fit metric for the entire model. However, various
other measures can be used to check the predictive relevance of the model in addition to the R2 values
and the significant path weights. Wold (1982: 30) suggested the use of cross validated redundancies
(Stone-Geisser’s test) to check the causal-predictive relevance of the model.
CV-redundancy
measures the capacity of the path model to predict the endogenous measurement variables (indicators
of dependant variables) indirectly from a prediction of their own constructs using the related structural
relation, by cross validation (Tenenhaus et al. 2005). If the causal predictive relation has predictive
relevance, then cv-redundancy value is more than zero. Lack of predictive relevance is indicated by a
cv-redundancy value of less than zero. If the cv-redundancy value is very close to zero, the decision
is uncertain.
The cv-redundancy values in table 3 show good predictive relevance values for climate and
performance and poor values for innovation-values fit and implementation effectiveness.
This
indicates that climate and fit are not sufficient predictors for implementation effectiveness. This is
consistent with the low value of multiple R2 (0.315) for implementation effectiveness. The same
applies for innovation-values fit. Education and training, while it’s significantly related to innovationvalues fit, is not a sufficient predictor of this construct. On the contrary, indicators of climate are
predicted efficiently using importance of need and education and training. Further, results show that
implementation effectiveness is considered a good predictor for performance and this is evident from
the R2 value of performance (0.490).
Table 3: CV- redundancy values for the PLS model
Dependent constructs
Climate
Innovation-values fit
Implementation effectiveness
Performance
Redundancies
0.223
0.046
0.061
0.123
IMPLICATIONS FOR PRACTICE AND RESEARCH
The results of this study provide valuable information for logistics/supply chain managers.
First, the results of this study emphasize the importance of consistent, enthusiastic, and creative use of
technology in enhancing performance due to technology implementation. Creation of a strong climate
for implementation contributes significantly to the sustained use of logistics technology. Therefore,
logistics managers should explore the set of policies and practices that are relevant to their
14
organization and the technology being implemented which has the potential to create this strong
climate for implementation.
The perceived importance of need by top management and education and training programs
were found to be good predictors of strong climate for implementation. Thus, logistics managers
should be cautious about starting a logistics technology implementation project without the existence
of a genuine internal need. In a recent interview with one of the logistics managers, he stated:
I think a lot of companies have awareness that what they’ve got at the moment is not
working and they have a perceived need that we need to fix this and this. And then I think to
actually translate that into a project requires a lot in terms of defining what you need, what
are the areas of greatest benefit, what are the deliverables…etc.
This indicates that a genuine internal need requires more. It needs to be specifically
defined and connected to the areas of greatest benefit. This will ensure that the impetus
behind the project is strongly backed up with operational benefits and deliverables.
Education and training was found important in stimulating managerial support and in melting
down the resistance for change.
This can be made possible through educating top
management using seminars about the amount of resources required for the implementation
of a given logistics technology and about the expected areas of greatest benefit. It also helps
user groups to understand how the technology will fit with their organizational and group
values.
The results of this study also have something to say for logistics scholars. The
potential of the implementation effectiveness model for logistics can’t be underestimated.
The theoretical foundation of the model and the suitability of the underlying assumptions for
logistics make it a promising tool for future studies. Rather than contributing to a divergent
knowledge, studies in logistics technology adoption and implementation should build on this
model with data using larger sample sizes.
We believe only through following this
perspective, the identified information gap by Dawe (1994) will shorten.
Before adding other predictors to implementation effectiveness, the insignificant
relationship by innovation-values fit should be theoretically explored and treatments should
be provided. One way is to trace back the theoretical blocks behind this latent variable in an
aim to find a solid justification for the results. Another way is to redefine the measurement
indicators of the innovations-values fit. A third way is to combine the implementation
effectiveness model and the Technology Acceptance Model in a theoretically based study.
15
APPENDIX
Construct (Type)
Implementation effectiveness
(formative)
Climate (reflective)
Fit (reflective)
Importance of need
(reflective)
Education and training
(formative)
Performance (formative)
Survey items
-Employees use the technology enthusiastically
-Employees use the technology frequently
-Employees are committed, consistent and creative in technology use
-High organizational resistance to the new technology (reverse
coded)
-Users were encouraged to use the technology
-Management provided feedback channels to
report problems during initial use
-Training was readily and broadly available
-Employees can access the technology at any time
-The technology is congruent with organizational values
-The technology is congruent with our department values
-The technology is congruent with other departments’ values
-Top management considers this problem or opportunity crucial
-Top management considers this problem current and need to be
solved now than later
-Top management are willing to commit significant amount of
resources to solve this problem
-Education & training targeted several affected groups in our
organization
-Top and middle management went through an educational program
too
-The education & training program used a variety of media such as
courses, seminars, video courses and software
-Cycle time
-On-time delivery
-Inventory
-Customer service
-Information accuracy
-Productivity
Sources
Klein and Sorra
(1996)
Klein and Sorra
(1996)
Authors
Meredith (1981)
Authors
Authors
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