Effects of Logistics Capabilities on Performance in Manufacturing Firms

Effects of Logistics Capabilities on Performance in Manufacturing
Firms
LIU Li, LUO Dingti
School of Economics & Finance, Hunan University of Technology, Taishan Road, Zhuzhou, Hunan
Province, China
[email protected]
Abstract: This study, based on a survey of 1000 manufacturing firms in central south, south and
central China regions, examines the relationship among logistics capabilities, competitive advantage
and firm performance. By exploratory and confirmatory factor analyses, the scale of manufacturing
firm’s logistics capabilities is obtained. The results show that logistics capabilities can be
conceptualized as a three dimensional construct: process capability, flexibility capability and
information integration capability. Furthermore, after testing research hypotheses by LISREL, we find
that process, flexibility and information integration capabilities all have significant effects on
competitive advantage, and that only process capability has significant effects on firm performance.
The implications for our findings are also presented for the improvement of manufacturing firm’s
logistics capabilities.
Keywords: Manufacturing firm, Logistics capabilities, Process capability, Flexibility capability,
Information integration capability
1 Introduction
Dierickx and Cool (1989) suggest that competitive advantage may be gained from two main sources:
assets and the capabilities that enable assets to be deployed advantageously. With the quick
development of information technology and globalization, manufacturing firms have been in a new era
of competition between supply chains. Logistics and supply chain management have become important
sources of sustainable competitive advantage. And logistics has been the most crucial element for
supply chain success. For sustainable competitive advantage, the supply chain should not only better
deploy logistics assets and coordinate the dispersed manufacturing and marketing activities, but also
make related logistics capabilities created by these resources the focus of efficient supply chain
operation.
Accordingly, researches on logistics capabilities have been highlighted in academic and practical fields
at home and abroad. Scholars define logistics capabilities from different perspectives, and put forward
their measurement. Daugherty and Pittman (1995) , Fawcett, Stanley and Smith (1997) find that the
time-based capability is the most important. Eckert and Fawcett (1996) believe that human resource,
quality and time are the most important elements of logistics capabilities. Bowersox and Closs (1996)
adopt such logistics capabilities measures as responsiveness, consistency and flexibility. Morash, Dröge
and Vickery (1996) divide strategic logistics capabilities into demand-oriented and supply-oriented
capabilities. Zhao, Dröge and Stank (2001) classify logistics capabilities into customer-focused and
information-focused capabilities. Lynch, Keller and Ozment (2000) discuss that logistics capabilities
include operational capability and value-added service. Shang and Marlow (2005) suggest that
information integration and general integration capabilities comprise logistics capabilities. Compared
with related researches abroad, those in China are still at an early stage. Ma Shi-hua and Meng Qingxin (2005) point out that the essential elements of supply chain logistics capabilities include tangible,
intangible and synthesized elements. Ma Shi-hua and Shen wen (2005) analyze the influence factors
from the viewpoint of logistics resources and systematical structure and discuss some interactive
mechanism of the factors. Gui Hua-ming and Ma Shi-hua (2005) analyze the elements influencing
logistics capabilities and the outsourcing strategy of enterprises with different capabilities. Yan Xiu50
xia, Sun Lin-yan and Wang Kan-chang (2005) propose Logistics capabilities Maturity Model
(LCMM). Gong Feng-mei, Ma Shi-hua and Tan yong (2007) set up the model of relationship among
logistics information capabilities, distribution capabilities, flexibility capabilities, and supply chain
performance. However, there are relatively less empirical studies of manufacturing firms’ logistics
capabilities, and in particular the measurement is not effectively supported by data in practice.
This paper, based on a survey of 1000 manufacturing firms in central south, south and central China
regions, attempts to develop logistics capabilities for manufacturing firms by factor analysis, and then
discusses the causal relationship among manufacturing firm’s logistics capabilities, competitive
advantage and firm performance by LISREL. Some helpful conclusions may be drawn to present
feasible implications and suggestions for the improvement of manufacturing firm’s logistics
capabilities and the enhancement of their competitiveness and firm performance in China.
2 Research Hypotheses
Based on the above literature review, this study divides logistics capabilities into three categories:
process, flexibility and information integration capabilities. Process capability means that the firm aims
at achieving the minimum total logistics costs by effective operation, simplifies firm’s workflows,
establishes related standards for key logistics process, and provides consistent, low-price and highquality services for customers. Flexibility capability emphasizes adaptability to unexpected
circumstances. Information-integration capability can improve logistics performance, facilitate logistics
integration, and contribute to supply chain success. Information integration can improve services and
reduce costs simultaneously, and significantly influence overall logistics capabilities, thus leading edge
firms strongly invest in information integration within the firm and information sharing between
members in the supply chain.
Accordingly, the following hypothesis is presented:
H1: In China’s manufacturing firms, logistics capabilities include multi-dimensions.
A firm’s logistics capabilities can be regarded as a key strategic resource or capability for acquiring
sustainable competitive advantage, and may have significant impacts on firm’s and even supply chain’s
competitiveness and performance. Although many scholars have demonstrated that various logistics
capabilities are positively associated with competitive advantage and/or financial performance,
empirical studies have rarely focused on logistics management in China but mainly have concentrated
on firms in western developed countries. There is still insufficient evidence to conclude that logistics
capabilities such as process, flexibility and information integration capabilities have significant effects
on firm performance.
Thus, the following hypotheses are introduced :
H2: In China’s manufacturing firms, logistics capabilities have a positive effect on competitive
advantage.
H2a: In China’s manufacturing firms, logistics process capability has a positive effect on competitive
advantage.
H2b: In China’s manufacturing firms, logistics flexibility capability has a positive effect on competitive
advantage.
H2c: In China’s manufacturing firms, logistics information integration capability has a positive effect
on competitive advantage.
H3: In China’s manufacturing firms, logistics capabilities have a positive effect on firm performance.
H3a: In China’s manufacturing firms, logistics process capability has a positive effect on firm
performance.
H3b: In China’s manufacturing firms, logistics flexibility capability has a positive effect on firm
performance.
H3c: In China’s manufacturing firms, logistics information integration capability has a positive effect
on firm performance.
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H4: In China’s manufacturing firms, competitive advantage has a positive effect on firm performance.
To summarize the research structure, the relationships among the latent variables in Hypotheses 1–4
are shown in Figure 1.
Figure 1: research structure
3 Research Methodology
A questionnaire survey was administered to a multi-industry sample of manufacturing firms in central
south, south and central China regions. A survey population was selected from a consultancy
membership listing which include many manufacturing firms in China who has made some
achievements in logistics or supply chain management. Questionnaires were sent to the presidents or
chief executive officers of the firms targeted since such people were considered appropriate respondents
to provide related information due to their knowledge of firms’ strategies, as were managers
responsible for logistics or SCM related activities. Through the process of original questionnaires
design, questionnaires modification, a pilot study and pre-testing, the questionnaires are finalized.
The items comprising the process, flexibility and information integration capabilities measures are
based upon past logistics studies. Five-point Likert-type scale anchors are used. Respondents were
asked to indicate their level of agreement with each item, where 1 represented “Strongly Disagree” and
5 represented “Strongly Agree”.
In addition, competitive advantage is measured on a five-item scale including such dimensions as
product quality, total cost, manufacturing flexibility, innovation and time, and firm performance is
measured by a balance scorecard performance system including financial performance, learning,
growth and customer measures. Respondents were asked to provide a five-point rating of the firm’s
performance relative to its major competitors for each item, where 1 represented “Much Worse” and 5
represented “Much Better”. All measured items are shown in Appendix A.
The questionnaires were sent directly to or mailed to the 1000 firms targeted. After two weeks, followup mailings were sent to those respondents who had not returned questionnaires in the first wave
survey. 306 questionnaires were returned and 26 of them were discarded because respondents had put
the same answers on all Likert-scale items. Therefore, the total response rate was 28%, an acceptable
response rate for such logistics empirical studies on the manufacturing industry.
According to a detailed analysis of the demographic characteristics of respondents’ firms, 95% of
sample firms are state-owned, private, China-foreign joint venture and foreign sole source investment
enterprises. 33.6% of sampled firms have over 2000 employees, whereas 36% of sampled firms have
less than 500 employees. Over 60% (66.8%) of firms’ sales are one hundred million RMB, 32.1% of
sampled firms have sales over one billion RMB.
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The top five industries sampled are the machinery equipment industry (17.1%), the information and
communications industry (13.4%), food & beverage industry (12.5%), the chemical product industry
(11.5%) and the metal refinery industry (9.7%).
The majority of the respondents (86.3%) have worked in their present firms for longer than 4 years and
consequently have sufficient knowledge to answer the questionnaire accurately and reliably. In
addition, 80% of questionnaires were filled in by vice presidents (19.7%), department and senior
managers (39.3%) and junior managers (22.1%) responsible for logistics or supply chain activities,
which further reinforced the reliability of the survey’s findings.
This study adopts exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to assess
the reliability and validity of the measures. Then LISREL can be used to estimate the structural model.
4 Empirical Analysis and Results
4.1 Validity and reliability
First, 100 of all the sampled firms were selected for exploratory factor analysis. Principle component
analysis (PCA) with Varimax rotation is the most commonly used in factor analysis. Items with factor
loadings of less than 0.5, a minimum threshold value recommended, or items that do not load any
factor or load on different factors are eliminated. Eigenvalues for all factors should be higher than 1.
Cronbach’s alpha for each construct with value above 0.7 are considered acceptable.
Data analysis shows that the KMO values (Kaiser-Meyer-Olkin measure of sampling adequacy) of the
constructs are suitable for factor analysis. Factor analysis for logistics capabilities items yields three
factors explaining 60.610% variance. The three factors are named as process, flexibility and
information capabilities. Factor analyses of both competitive advantage and firm performance items
identify only one factor explaining 69.832% and 58.348% of variance, respectively. All Cronbach’s
alpha values are above 0.80, exhibiting acceptable reliability.
Table 1: Analyses of latent variables reliability and validity
Standardized coefficients
Cronbach’s
CR
Latent variables
Items
(t-value)
alpha
(AVE)
PC01
0.823(14.278**)
PC02
0.827(14.499**)
0.709
Process capability
PC03
0.753(9.861**)
0.859
(0.532)
**
(PC)
PC04
0.718 (12.163 )
**
PC05
0.768(13.008 )
PC06
0.713(12.076**)
FC01
0.773(12.852**)
FC02
0.750(12.432**)
Flexibility
0.859
FC03
0.727(11.911**)
capability
0.855
(0.670)
FC04
0.712(12.067**)
(FC)
FC05
0.736(12.474**)
FC06
0.708(12.206**)
Information
IIC01
0.816(13.752**)
integration
IIC02
0.773(12.781**)
0.765
0.843
capability
IIC03
0.752(12.263**)
(0.521)
(IIC)
IIC04
0.714(11.240**)
Model
fit χ2/df=2.334 ;RMSEA=0.095 ;GFI=0.912 ;AGFI=0.852 ;NFI=0.883 ;IFI=0.908 ;C
indices
FI=0.904
Competitive
LA01
0.704(11.105)
0.914
0.878
advantage
LA02
0.803(10.567)
(0.572)
53
(CA)
LA03
0.733(9.278)
LA04
0.788(10.368)
LA05
0.757(9.582)
Model
fit χ2/df=2.449 ;RMSEA=0.089 ;GFI=0.921 ;AGFI=0.866 ;NFI=0.906 ;IFI=0.907 ;C
indices
FI=0.905
FP01
0.704(9.143)
FP02
0.702(9.117)
Firm performance
FR03
0.692(8.872)
0.912
0.892
(FP)
FR04
0.714(9.917)
(0.565)
FR05
0.787(10.221)
FR06
0.753(9.779)
Model
fit χ2/df=2.670 ;RMSEA=0.086 ;GFI=0.922 ;AGFI=0.854 ;NFI=0.923 ;IFI=0.911 ;C
indices
FI=0.924
Then, the construct validity is evaluated. The results show that all standardized factor loadings for
corresponding latent variables are above 0.60, and are considered significant when t-values exceed
+3.72 at a 0.001 significant level. Chin (1998) recommends that composite reliability (CR) and
average variance extracted (AVE) also be used to determine if the scales used in a study are indeed
reliable. The lower bound of CR is 0.70 while the suggested lower bound for AVE is 0.50. Table 1
provides the analyses of reliability and validity of all scales. Inspection of this table indicates that the
minimum acceptable values for each constructs on each of the measures are met, showing that all of
the scales demonstrate sufficient convergent validity. Chin (1998) argue that the square root of the
AVE for a construct should be larger than the correlations between constructs. In the scale of logistics
capabilities, the correlations between the three constructs are 0.52, 0.43 and 0.58, respectively. We can
see that the square root of the AVE is greater than the correlations among the latent variables.
Therefore, we can be assured that sufficient discriminative validity is present. In addition, model fit
indices used in this study are χ2/df, RMSEA, GFI, AGFI, NFI, IFI, and CFI. Hou Jie-tai, Wen Zhonglin and Cheng Zi-juan (2004) propose that the χ2/df value should be less than 3, its upper bound be 5,
and that a RMSEA value of less than 0.1should indicate good model fit. The recommended value for
other indices should be no less than 0.9, and the lower bounds are 0.80. As shown in Table 1,
goodness-of-fit indices are all well within recommended limits. In conclusion, all of the scales’
reliability and validity are verified.
4.2 Full structural model and hypotheses testing results
Then, the model in Figure 1 is tested using LISREL 8.71based on the rest 180 sampled firms. This
paper presents two models. Model 1 considers 3 factors including logistics capabilities, competitive
advantage and firm performance. The model fit indices are as follows: χ2/df=3.065(p=0.000),
RMSEA=0.106, GFI=0.862, AGFI=0.805, CFI=0.885, NFI=0.850, IFI=0.890. Model 2 considers 5
factors including process capability, flexibility capability, information integration capability,
competitive advantage and firm performance.
The model fit indices are as follows:
χ2/df=2.702(p=0.000), RMSEA=0.083, GFI=0.930, AGFI=0.900, CFI=0.948, NFI=0.925, IFI=0.945.
So, model 2 is well-fitted.
In path analysis, the parameter estimates shown in Figure 2 are standardized solutions. Estimated path
coefficients and significance levels for each path that are* for p<0.05, ** for p<0.01 and *** for
p<0.001 are given in Figure 2. The relationships between process, flexibility and information
integration capabilities and competitive advantage are significant (0.321(t=5.441); 0.216(t=3.483);
0.117(t=2.017)). H2 is supported. The significant and positive relationship between process capability
and firm performance (0.122, t=2.103) provides partial support for the positive effects of logistics
capabilities on firm performance. Thus, H3 is partly supported. The effect of competitive advantage on
54
firm performance (0.431,t=7.839) is significant, suggesting support for H4. Hypotheses testing results
are shown in Table 2.
***
0.321
PC
*
CA
0.122
FC
**
0.216
***
0.431
*
0.117
FP
IC
Figure 2: Results of model testing
Table 2: Hypotheses testing results
Coefficients
Hypotheses
Results
(t-value)
H1
Supported
H2:
Supported
LC →CA
H2a:
0.321***
Supported
PC→CA
(5.441)
**
H2b:FC
0.216
Supported
→CA
(3.483)
*
H2c:
0.117
Supported
IIC→CA
(2.017)
H3:
Partly
LC →FP
supported
*
H3a:
0.122
Supported
PC→FP
(2.103)
H3b:
0.098
Not
FC →FP
(1.633)
supported
H3c:
0.084
Not
IIC →FP
(1.400)
supported
H4:
0.431***
supported
CA →FP
(7.836)
5 Conclusions and Managerial Implications
5.1 Conclusions
This study leads to two main conclusions. First, the scale of logistics capabilities is developed.
Logistics capabilities of manufacturing firms in China can be conceptualized as a three dimensional
construct: process capability, flexibility capability and information integration capability. Second,
process, flexibility and information integration capabilities are positively associated with higher level
of competitive advantage. Process capability is positively related to firm performance, and flexibility
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and information integration capabilities have no direct impacts on firm performance, but have indirect
impact on firm performance through competitive advantage. These conclusions may contrast with
results from previous work concerning different countries and different sectors of industry.
5.2 Managerial implications
These findings have some important implications for theory and managerial practice. Although the
importance of logistics capabilities of manufacturing firms is well accepted, there are barriers to its
construction. At present, China’s manufacturing firms have paid much attention to process capability,
thus, controlling logistics costs, improving logistics management and providing better services are
important factors of logistics capabilities. This implies that firms should promote commitment in
process capability.
While the study sheds light into the measures of logistics capability, it also suggests that logistics
capabilities are higher level capability created by firms’ resources and competencies. For a long-term
development, manufacturing firms should understand that flexibility and information integration
capabilities are tremendously important. Flexibility and information integration capabilities are the
firm specific assets and capabilities which enable firms to build lasting distinctiveness and are difficult
to move and imitate. The results reveal no direct link between flexibility and information integration
capabilities and firm performance. However, the two capabilities do influence competitive advantage,
which in turn is shown to improve firm performance. Therefore, best practice firms should focus on the
three logistics capabilities.
Two important study limitations should be highlighted. First, the framework of logistics capabilities in
this study may need further testing. Future studies may yield different insights. Second, the sample in
this study was drawn from manufacturing firms in central south, south and central China regions, thus,
the conclusions inferred can not be generalized to other regions or non-manufacturing firms.
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Appendix A : Items and latent variables
Process capability:
PC01: The firm can achieve the minimum of its total costs through effective operation and technology
or scale economy.
PC02: The firm can develop its logistics development strategy according to target markets and the
firm’s conditions.
PC03: The firm can effectively simplify the logistics processes related to manufacturing, shipment,
assembly and delivery.
PC04: The firm can provide standardized operations for key processes.
PC05: The firm has established good coordination between logistics department and other ones.
PC06: The firm can effectively deal with affairs related to reverse logistics.
Flexibility capability :
FC01: The firm can accomplish the logistics operations according to customers’ requirement more
quickly than its competitors.
FC02: The firm can make special logistics plans according to customers’ special orders.
FC03: The firm can properly adjust its logistics processes according to its employees’ and customers’
advice.
FC04: The firm can respond to the urgent needs of key customers.
FC05: The firm can effectively improve its logistics capabilities through supply chain coordination.
FC06: The firm can provide different logistics service from its competitors’ through continuous
innovation and improvement.
Information Integration capability :
IIC01: The firm can effectively collect and process related logistics information.
IIC02: The firm can share related logistics information between departments.
IIC03: The firm can upgrade related logistics information and assure the stability of information
system.
IIC04: The firm has established information integration with its suppliers and customers.
Competitive advantage :
LA01: Compared with its competitors, the firm can provide high-quality products for customers.
LA02: Compared with its competitors, the firm can provide low-price products for customers.
LA03: Compared with its competitors, the firm has manufacturing flexibility for customers’ special
needs.
LA04: Compared with its competitors, the firm can quickly introduce new products or new functions.
LA05: Compared with its competitors, the firm has the capability to quickly response to the market
according to customers’ needs.
Firm performance :
FP01: ROI
57
FP02: ROA
FR03: Market share growth
FR04: Ratio of new product sales to total sales
FR05: Average manufacturing cost
FR06: Total customer satisfaction
58