Factors influencing the use intention of port logistics ...

Int. J. Shipping and Transport Logistics, Vol. 4, No. 1, 2012

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Factors influencing the use intention of port logistics information system by ocean carriers Ching-Chiao Yang* Department of Shipping and Transportation Management, National Kaohsiung Marine University, No. 142, Haijhuan Rd., Nanzih District, Kaohsiung City 811, Taiwan Fax: +886-7-3647046 E-mail: [email protected] *Corresponding author

Chin-Shan Lu Department of Transportation and Communication Management Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan Fax: +886-6-2753882 E-mail: [email protected] Abstract: This study empirically investigates factors influencing ocean carriers’ use intention toward a port logistics information system (PLIS). Data collected from a survey of carriers in Taiwan were used to test a structural equation model examining the relationships among top management support, cost of adoption, perceived benefit, perceived complexity, security concern, and use intention. The results indicate that top management support, cost of adoption, perceived benefits, and perceived complexity are the dominant factors explaining the use intention of PLIS by carriers. However, the influence of security concerns on use intention was not shown to be significant in this study. Theoretical and managerial implications of the research findings are discussed. Keywords: port logistics information system; PLIS; use intention; structural equation modelling; Taiwan. Reference to this paper should be made as follows: Yang, C-C. and Lu, C-S. (2012) ‘Factors influencing the use intention of port logistics information system by ocean carriers’, Int. J. Shipping and Transport Logistics, Vol. 4, No. 1, pp.29–48. Biographical notes: Ching-Chiao Yang is an Assistant Professor of the Department of Shipping and Transportation Management at the National Kaohsiung Marine University. Chin-Shan Lu is a Professor of the Department of Transportation and Communication Management Science at the National Cheng Kung University.

Copyright © 2012 Inderscience Enterprises Ltd.

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1

C-C. Yang and C-S. Lu

Introduction

Information technology has increasingly played an important role in the interconnected global supply chain environment (Jin, 2006; Chu et al., 2010). In order to achieve speed, frequency, and reliability, it is imperative to frequently share information among supply chain partners (Sanders, 2007; Zhou and Benton, 2007). A logistics information system is a management information system that provides the management of a firm with relevant and timely information related to its logistics functions (Lambert et al., 1998; Lai et al., 2008; Ngai et al., 2008). Accordingly, logistics information systems play a central role in improving the supply chain to make it competitive by sharing information between suppliers, manufacturers, carriers, and distributors (Humphreys et al., 2006; Wong et al., 2009a, 2009c). Ports have been seen as key logistical elements in value-driven supply chain systems (Paixao and Marlow, 2003). Because of the information age, the competitive position of a port is not only determined by its internal strengths but is also affected by its links in a given supply chain. Accordingly, ports need to be flexible to ensure a better ability to compete against each other, and they must work more closely with their clients than ever before (Wong et al., 2009c). A logistics information system, therefore, could be very important for port operators or authorities with regard to improving services and increasing their competitive advantages (Almotairi and Lumsden, 2009; Wong et al., 2009b). To avoid the operational inefficiencies caused by the lack of information technology, real time information is required by port operators to increase their planning capacity and enhance their customer services (Kia et al., 2000). A logistics information system allows port authorities to identify customer requirements and trends and to subsequently communicate the gathered information throughout the supply chain quickly, decreasing both transit time in ports and lead times, and thereby creating greater utility and variety of the services being delivered (Soliman and Janz, 2004). On the other hand, carriers can experience the benefits of shorter ship waiting time, faster discharging and loading of containers, improved turn-around time container load rates, and a high level of accuracy of information by using a port logistics information system (PLIS) (Kia et al., 2000). The PLIS basically is one kind of logistics information system used in a port context that provides relevant, timely and accurate information to logistics decision makers (Ngai et al., 2008). There is already evidence that many international ports have engaged in developing a logistics information system and have benefited, for example, the TRADENET in Singapore, INTIS in Rotterdam, KL-Net in Pusan, SEAGHA in Antwerp, SHIPNET in Japan, and MTNet in Taiwan. These systems provide better control over information flows and quality, decreases the volume of paper usage, and reduces logistics costs, thus obtaining a higher service level, followed by gains in efficiency and better relations with partners (Lee et al., 2000). According to the Ministry of Transportation and Communication report (2009) in Taiwan, the proportion of responding carriers which were satisfied with port information service had grown from 60% in 2004 to 80% in 2009. However, this does not mean that port authorities should reduce their efforts to improve in these aspects; in general, the port information services had 80% of satisfactory level, indicating that carriers rated them ‘satisfaction’ marginally. This

Factors influencing the use intention of PLIS by ocean carriers

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implied that port authorities still need to make an effort to understand the factors affecting their intention to use port information system. A number of previous studies have evaluated critical factors influencing the adoption of new information technology and have suggested that users’ viewpoints and perceptions have a significant impact on use intention (Davis et al., 1989; Kim et al., 2007; Gunasekaran and Ngai, 2008). However, most of these studies are limited to internet services or e-commerce services with a focus on the acceptance of technology in a specific industry, including banking (Cheng et al., 2006; Yiu et al., 2007), liner shipping (Lu et al., 2007; Hsu et al., 2009), retailers (Olson and Boyer, 2003; Tsai et al., 2010), and manufacturing (Cheng et al., 2002; Wang et al., 2010). There seems to be a lack of empirical studies examining the factors affecting the adoption of a logistics information system in an international port context. To avoid this pitfall, it is important for port authorities to understand the factors influencing carrier intention to adopt a PLIS. By understanding ocean carriers’ needs, wants, and demands, port authorities can better formulate their marketing-mix strategies, such as executing the conception, pricing, promotion, and distribution of services to increase PLIS usage. Thus, this study aims to develop a model to explain the adoption of a PLIS from an ocean carrier’s perspective and provide implications for port authorities to formulate their marketing strategies to increase carriers’ use intention of a PLIS in the future. The next section reviews the literature and provides justifications for investigating the intention of carriers to use a PLIS, and discusses the conceptual framework and the related research hypotheses. Section 3, describes the research methodology. Section 4 presents the survey findings from structural equation modelling (SEM). Section 5 has the discussions of the findings, and the research and managerial implications are discussed in the final section.

2

Theory and research hypotheses

2.1 Factors influencing the adoption of a PLIS A large number of studies have identified the factors influencing the adoption of new technologies in major countries/regions. Specifically, as shown in Table 1, these studies were conducted in the USA (Premkumar and Roberts, 1999), UK (Doherty et al., 2003), Taiwan (Lu et al., 2007; Hsu et al., 2009), Hong Kong (Yiu et al., 2007; Ngai et al., 2008), South Korea (Kim and Lee, 2008; Kim and Garrison, 2010), and others (Lee, 2000; Brown and Russell, 2007; Ketikidis et al., 2008). For example, Lee et al. (2000) found the failure of Japan’s SHIPNET system was due to a lack of government involvement, no support from port authorities, no customs involvement, higher user costs due to high cost of transmission lines, no communication standard message adopted, and lack of technical support and standard software packages for shipping lines. Kim et al. (2007) found that perceived usefulness, enjoyment, technicality, and perceived fee were critical factors influencing the adoption of mobile internet in South Korea. Ngai et al. (2008) investigated the adoption of logistics information systems in Hong Kong logistics companies. Six factors, namely, direct benefits, indirect benefits, logistics benefits, resource barriers, cultural barriers, and firm size were identified as important to the adoption of logistics information systems.

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Table 1

Prior research on the adoption of new technology in major countries

Country/region

Previous studies

USA

Premkumar and Roberts (1999) and Soliman and Janz (2004)

UK

Doherty et al. (2003)

Taiwan

Lu et al. (2007), Hsu et al. (2009), Tsai et al. (2010) and Wang et al. (2010)

Hong Kong

Yiu et al. (2007), Ngai et al. (2008) and Gunasekaran and Ngai (2008)

South Korea

Kim et al. (2007) and Kim and Garrison (2010)

Others

Lee et al. (2000), Brown and Russell (2007) and Ketikidis et al. (2008)

Determinants Relative advantage, cost, top management support, external support, competitive pressure, external pressure, complexity, compatibility, pressure from trading partner, network reliability, data security, scalability, trust, and so on. Internet strategy, infrastructure and development capability, internet target segment, internet marketplace, internet communications, internet cost opportunity, market development opportunity, cost of internet trading, concerns, consumer sensitivity and so on. Security, perceived usefulness, perceived ease of use, price discount, IT training, relative advantage, complexity, compatibility, top management support, supply chain integration, organisational readiness, external pressure, and so on. Perceived benefits, perceived ease of use, personal innovativeness in information technology, perceived risk, perceived barriers, organisational context, financial and top management support, security, compatibility, and so on. Usefulness, enjoyment, technicality, perceived fee, ubiquity, job relevance, performance gaps, benefits, cost savings, financial resources, technological knowledge, and so on. Support, cost, compatibility, relative advantage, complexity, IT expertise, top management support, organisational size, external pressure, and so on.

Ketikidis et al. (2008) pointed out that the major problems faced by manufacturing and trading enterprises with regard to their use of information systems for logistics and supply chain management in Europe were resistance to change from employees, resource shortages, skills shortage, insufficient vendor support, hidden costs, integration with existing systems, integration with supplier’s systems and integration with customer’s systems. Tsai et al. (2010) claimed that relative advantage, complexity, supply chain integration, and organisational readiness were significantly in affecting RFID adoption in Taiwanese retail chains. Wang et al. (2010) employed the technology organisation environment (TOE) model to understand the determinants of RFID adoption in the manufacturing industry. Results indicated that complexity, compatibility, firm size, competitive pressure, partner pressure, and information intensity were significantly related to the adoption of RFID. In summary, this study applied the results drawing from the technology acceptance model (TAM) and TOE model and the previous studies in major countries. Hence, five factors which were frequently employed as the determinants of new technology adoption were identified. This study therefore evaluates the relationships of five sets of antecedent factors – top management support, cost of adoption, perceived benefit, perceived complexity, and security concern – on PLIS use intention. The research model and research hypotheses are discussed in the next section.


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Figure 1 A conceptual model of the five factors hypothesised to have a direct effect on carriers’ intention to use a PLIS

Top management support

H1 Cost of adoption

H2

H3 Perceived benefit

Use intention

H4 Perceived complexity

H5

Security concern

2.2 Research model and research hypotheses Figure 1 shows this study’s conceptual model comprised of five factors hypothesised to cause a direct effect on the use intention for PLIS. These relationships are well established based on previous studies as shown in Table 2 and rationale for the proposed linkages are elaborated below. Table 2

Rationale for the research hypotheses

Investigated relationship

Expected effect

Selected supporting literature

Top management commitment → Use intention (H1)

Positive

Byrd and Davidson (2003), Harrison and Waite (2005), Quaddus and Xu (2005), Jin (2006), Brown and Russell (2007), Law and Ngai (2007) and Kim and Lee (2008)

Cost of adoption → Use intention (H2)

Negative

Davis et al. (1989), Soliman and Janz (2004), Brown and Russell (2007), Kim et al. (2007) and Gunasekaran and Ngai (2008)

Perceived benefit → Use intention (H3)

Positive

Quaddus and Xu (2005), Cheng et al. (2006), Brown and Russell (2007), Hsu et al. (2009) and Kim and Garrison (2010)

Perceived complexity → Use intention (H4)

Negative

Teo et al. (1995), Cheng et al. (2002), Lu et al. (2007) and Wang et al. (2010)

Security concern → Use intention (H5)

Negative

Soliman and Janz (2004), Cheng et al. (2006) and Yiu et al. (2007)

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2.2.1 Top management support Top management support has been conducted to be critical for creating a supportive climate and providing adequate resources for adoption of new information technologies (Brown and Russell, 2007; Kim and Lee, 2008). Harrison and Waite (2005) pointed out that the influence of a senior manager within the company was a critical factor influencing the development of a website. Quaddus and Xu (2005) found that success of knowledge management system (KMS) diffusion depended on top management taking initiatives and supporting the diffusion stages all the way through. Accordingly, the greater the top management support for a PLIS, the more likely it will be adopted. Therefore, we propose that: H1

Top management support has a positive effect on a carrier’s intention to adopt a PLIS.

2.2.2 Cost of adoption Cost of adoption is another important factor that affects the adoption of a PLIS. Several studies have indicated cost of adoption is a major barrier with regard to the adoption of new information technologies (Davis et al., 1989; Kim et al., 2007; Gunasekaran and Ngai, 2008). Kim et al. (2007) pointed out that the perceived fee was a crucial factor negatively affecting the adoption of the mobile internet. Therefore, carriers that perceive greater cost may be more likely to not be adopters of a PLIS. Accordingly, the following proposition is formulated: H2

Cost of adoption has a negative effect on a carrier’s intention to adopt a PLIS.

2.2.3 Perceived benefit The primary motivation for firms to adopt new information technologies is the perceived benefits these technologies will bring to their company. Perceived benefit is generally similar to the dimension of perceived usefulness in the TAM (Davis et al., 1989). Venkatesh and Davis (2000) defined perceived usefulness as “the extent to which a person believes that using the system will enhance his or her job performance” (p.187). Hence, usefulness is the total value perceived by a user from using a new technology (Kim et al., 2007). Kim and Garrison (2010) found that perceived benefits had a significantly positive influence on the adoption of RFID. In general, adopters need to perceive the potential benefits of a new technology before they will adopt. Hence, the greater the benefits gained from a new technology, the greater the level of use (Vadapalli and Ramamurthy, 1997). Thus, we hypothesise that: H3

Perceived benefit has a positive effect on a carrier’s intention to adopt a PLIS.

2.2.4 Perceived complexity Perceived complexity is the degree of difficulty associated with understanding and learning to use an innovation. The complexity of the technology creates greater uncertainty for successful technological implementation and increases the risk of failure in the adoption process (Cheng et al., 2002). Davis et al. (1989) asserted that a technology which was perceived to be easier to use than another was more likely to be


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accepted. Perceived complexity is the opposite of perceived ease of use in the TAM. In general, complexity is found to have a negative impact on the adoption of information technologies (Cheng et al., 2002; Brown and Russell, 2007; Wang et al., 2010). Hence, it is an important factor to influence the use of a logistics information system. Therefore, we argue that: H4

Perceived complexity has a negative effect on a carrier’s intention to use a PLIS.

2.2.5 Security concern Security is also one of the important factors of concern by uses who wish to use e-commerce or internet services. The risk in carrying out commercial activities over the internet lies in the transmission of data via the internet and the World Wide Web. Users may refrain from using the internet if they feel that the level of risk is not acceptable (Strader and Shaw, 1997). Hence, perceived risk was found to have a negative impact on the adoption of new technologies (Yiu et al., 2007; Gunasekaran and Ngai, 2008). Therefore, we hypothesise that: H5

3

Security concern has a negative effect on a carrier’s intention to use a PLIS.

Methodology

3.1 Questionnaire and measures Data for the study were collected from a questionnaire designed according to the stages outlined by Churchill and Iacobucci (2002). In order to ensure the instrument’s accuracy and the content validity of the questionnaire, a comprehensive review of the literature and interviews with practitioners were used in this study, i.e., questionnaire items were based on previous studies and discussions with a number of executives and experts in liner shipping. All the measurement factors and indicators for the acceptance of the PLIS, as shown in Appendix, were drawn from previous studies and slightly revised after interviewed with shipping executives. The measures for top management support, perceived complexity, and security concern were drawn from Cheng et al. (2002) and Soliman and Janz (2004). The measures of cost of adoption and perceived benefit were drawn from Doherty et al. (2003), Olson and Boyer (2003), and Lu et al. (2007). In this study, each questionnaire item was measured in a seven-point Likert scale, where r 1 corresponds to ‘strongly disagree’ and 7 to ‘strongly agree’.

3.2 Sampling techniques The container port traffic in Taiwan for 2009 was 11,710,266 TEUs (equivalent 20 feet box). Taiwan has four international container ports: Kaohsiung Harbour, Keelung Harbour, Taipei Harbour, and Taichung Harbour. Kaohsiung Harbour is the largest container port in Taiwan, which accounted for 73.3% of the total container traffic. On the other hand, Keelung Harbour, Taichung Harbour, and Taipei Harbour accounted for 13.5%, 10.2%, and 3.0% of total container traffic, respectively. In the world container port traffic league, Kaohsiung Port has remained in the top 12th position in 2009. Among

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the major liner shipping companies, Evergreen Marine Corporate was ranked the fourth largest container carrier in the world in 2010, while Yamg Ming Lines and Wan Hai Line were ranked 17th and 21st, respectively. This study examines the PLIS from a port user perspective (i.e., ocean carriers). Typically, ocean carriers include liner services (container and general cargo transport) and non-liner services (e.g., bulk, tanker, and passenger). The population of ocean carriers was therefore drawn from the Directory of National Association of Chinese Ship owners and Shipping Agencies. In total, 306 container shipping companies and 146 bulk shipping companies were identified. An initial mailing of the survey with a cover letter, a copy of the survey, and a postage-paid return envelope was therefore sent to 452 managers in Taiwan in 2009. The effective population size was reduced to 437, as 15 managers had left the company or the business no longer being in existence according to the addresses. The initial mailing elicited 80 usable responses. A follow-up mailing was sent two weeks after the initial mailing. An additional 48 usable responses were returned. The total number of usable responses was 128. Therefore, the overall response rate for this study was 29.3%. A comparison of early (those who responded to the first mailing) and late (those who responded to the second mailing) respondents was carried out to test for a non-response bias (Armstrong and Overton, 1977). The 128 respondents were divided into two groups based on their response wave (first and second). T-tests were performed on the responses of the two groups. At the 5% significance level, there were no significant differences between the two groups’ perceptions of the agreement for the various items. Although the results did not rule out the possibility of non-response bias, they suggested that non-response bias was not a problem since late respondents’ responses were similar to those of the first wave respondents.

3.3 Research methods This study used SEM to investigate the impact of five sets of antecedent factors – top management support, cost of adoption, perceived benefit, perceived complexity, and security concern – on the adoption of a PLIS. SEM is a modelling technique that can handle a large number of endogenous and exogenous variables, as well as latent (unobserved) variables specified as linear combinations (weighted averages) of the observed variables (Golob, 2003). In order to minimise the problem of common method bias, several statistical remedies were employed, including item-total correlations [or corrected item-total correlations (CITC)], estimation of reliability using Cronbach’s α, and confirmatory factor analysis (CFA) (Podsakoff et al., 2003). Accordingly, two-step approach suggested by Anderson and Gerbing (1988) was employed to analyse the data. In the first step, the CFA was performed to assess the validity of the measurement model. Once the measurement model is validated, then the researcher proceeds to the second step, estimating the structural model between latent variable. All analyses were carried out using SPSS 18.0 for Windows and AMOS 18.0 statistical packages.

3.4 Characteristics of responses The profiles of the respondent companies and their characteristics are displayed in Table 3. 49% of the respondent firms were involved in liner shipping business, while 51% of them were with bulk shipping companies. Nearly 65% of the respondents were classified according to title, either vice president or above or manager/assistant manager.


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Only a few respondents were in the position of director/vice director (14.8%), sales representative (4.7%), clerk (9.4%) or other (6.3%). This finding was important since managers may be involved in and anchor operations in their businesses. A high percentage of the respondents from this group or those in higher positions had abundant practical experience with which to answer the questions and endorsed the reliability of the survey’s findings. Table 3 also shows the age of the responding firms. Most responding firms (60%) have been in operation for more than 20 years, whereas 25 (or 19.8%) respondent firms were between 11 and 20 years old. Only six (or 4.8%) responding firms were less than five years old, whereas 19 (or 15.1%) of them were between six and ten years old. In terms of the ownership pattern, the vast majority of the firms responding to the survey were local companies, followed by joint venture with local and foreign companies, and foreign companies. The local companies accounted for 76.4% (97 firms) of responding firms, while local-foreign and foreign companies shared 15.7% (20 firms) and 7.9% (ten firms), respectively. Table 3

Respondents’ profiles Number of respondents

Percent of respondents

Liner shipping company

63

49.2

Bulk shipping company

65

50.8

Vice president or above

32

25.0

Manager/assistant manager

51

39.8

Director/vice director

19

14.8

Characteristics Type of business

Job title

Sales representative

6

4.7

Clerk

12

9.4

Other

8

6.3

Age of firm (years) Less than five

6

4.8

6 to 10

19

15.1

11 to 15

14

11.1

16 to 20

11

8.7

21 to 25

13

10.3

26 to 30

18

14.3

Greater than 30

45

35.7

Local company

97

76.4

Foreign company

10

7.9

Joint-venture with local and foreign companies

20

15.7

Ownership pattern

Note: Two respondents did not provide this information.

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4

Results of empirical analysis

4.1 CITCs and reliability test Prior to purification of the measurement model, a CITC and reliability test of the survey data is presented in Table 4. Item-total correlation refers to a correlation of an item or indicator with the composite score of all the items forming the same set. CITC does not include the score of the particular item in question in calculating the composite score, and thus it is labelled ‘corrected’ (Koufteros, 1999). Recommendations typically suggest that items from a given scale exhibiting item-total correlations should exceed 0.3 (Churchill and Iacobucci, 2002; Lai et al., 2002). CITC analyses were performed for each construct. Table 4 shows that all CITC scores were well above 0.3, concluding that these 18 items measured the same underlying construct. Table 4

Descriptive statistics, alpha values, and range of CITC coefficients No. of items

Mean

S.D.

Alpha

Range of corrected item-total correlation

MS

3

5.273

1.026

0.753

0.562 to 0.591

CO

3

4.089

1.164

0.773

0.436 to 0.742

PB

3

5.341

1.025

0.708

0.485 to 0.572

PC

3

4.568

1.191

0.789

0.573 to 0.706

SC

3

3.563

1.303

0.880

0.736 to 0.812

UI

3

5.211

1.197

0.937

0.842 to 0.894

Notes: MS: top management support; CO: cost of adoption; PB: perceived benefit; PC: perceived complexity; SC: security concern; UI: use intention; all significant at p < 0.01.

A reliability test based on the Cronbach alpha statistic was used to determine whether each of the six dimensions was consistent and reliable. The coefficient alpha provides a summary measure of the intercorrelation that exists among a set of items. As shown in Table 4, the Cronbach alpha values of each measure were well above the suggested threshold of 0.7, which is considered adequate for confirming a satisfactory level of reliability in research (Nunnally, 1978; Churchill and Iacobucci, 2002). However, these techniques do not allow for assessing unidimensionality, convergent validity, nor discriminant validity (Anderson and Gerbing, 1988). A CFA with a multiple-indicator measurement model was therefore used to ensure validity (Anderson and Gerbing, 1988; Segars, 1997). Results, as shown in Table 4, also show that the most agreement for factor affecting the use intention of a PLIS was perceived benefit, followed by top management support, perceived complexity, cost of adoption, and perceived security.

4.2 Analysis of the measurement model The path diagram presented in Figure 2 implies a measurement model where there are six latent variables (constructs) made up of their corresponding multiple indicators (measures or items). The statistical criteria for model modification decisions


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include offending estimates, squared multiple correlations, standardised residual covariances, and model fit indices (Koufteros, 1999; Min and Mentzer, 2004). Once the proposed model was purified, tests of validity, reliability, and unidimensionality were performed. Figure 2 Path diagram representing the measurement model

δ1 δ2 δ3

δ4 δ5 δ6

δ7 δ8 δ9

δ10 δ11 δ12

δ13 δ14 δ15

δ16 δ17 δ18

1 1 1

1 1 1

1 1 1

1 1 1

1 1 1

1 1 1

MS1

1

MS2

ξ1 Top management support

MS3

CO1

1 ξ2 Cost of adoption

CO2 CO3

PB1

1 ξ3 Perceived benefit

PB2 PB3

PC1

Φij 1

PC2

ξ4 Perceived complexity

PC3

SC1

1

SC2

ξ5 Security concern

SC3

UI1 UI2 UI3

1

ξ6 Use intention

Perceived benefit

Perceived complexity

Security concern

Use intention

ξ3:

ξ4:

ξ5:

ξ6: 1.160 1.074

UI2 UI3 2

1.000

UI1

0.903

SC2

1.258

PC3 1.000

1.308

PC2

SC1

1.000

1.076

PC1

1.000

PB3

1.027

CO3 PB2

1.000

CO2

0.879

MS3

0.931

0.937

0.869

0.828

0.872

0.743

0.825

0.688

0.752

0.669

0.916

0.864

0.697

0.761

Completely standardised factor loading

0.070

0.075

---

0.140

---

0.178

0.175

---

0.178

---

0.096

---

0.129

---b

Standard error

15.380

15.554

---

6.431

---

7.072

7.496

---

6.059

---

10.751

---

6.837

---

Critical ratio

0.866

0.877

0.754

0.686

0.761

0.552

0.680

0.473

0.566

0.448

0.839

0.746

0.485

0.579

R2 (item reliability)

0.937

0.839

0.797

0.672

0.884

0.695

Construct reliabilityc

Notes: Fit indices: χ = 76.047 (p = 0.108), df = 62, χ /df = 1.227, GFI = 0.925, AGFI = 0.873, CFI = 0.985, RMR = 0.065, RMSEA = 0.042. b Indicates a parameter fixed at 1.0 in the original solution. c Construct reliability = (sum of standardised loading)2 / [(sum of standardised loading)2 + (sum of indicator measurement error)]; Indicator measurement error can be calculated as 1 – (standardised loading)2. d Average variance extracted (AVE) = (sum of squared standardised loading) / [(sum pf squared standardised loadings) + (sum of indicator measurement error)]; Indicator measurement error can be calculated as 1 – (standardised loading)2.

2

Cost of adoption

ξ2:

1.000

Unstandardised factor loading

MS1

Item

0.833

0.723

0.569

0.507

0.793

0.532

AVEd

Table 5

a

Top management support

ξ1:

Latent variable

40 C-C. Yang and C-S. Lu

Parameter estimate, standard errors, critical ratios, and R2 for the revised modela


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The initial model was found to be discredited. The χ2 statistic (χ2 = 185.959; df = 120; p = 0.000) was statistically significant at the 0.05 level of significance, indicating that differences between model-implied covariance matrix Σ and data-observed S were significantly large. Due to the fact that the three squared correlations values (MS2, CO1, and PB1) did not exceed the recommended cut-off point of 0.4 and items MS2 and SC3 had the highest residuals (value = 1.306), the variables MS2, CO1, PB1, and SC3 were eliminated in the revised model. As shown in Table 5, the resulting model provided an adequate model fit (χ2 = 76.047; df = 62; p = 0.108 > 0.05), indicating that the proposed model was purified and credible. A number of goodness-fit indices recommended by researchers were used to evaluate the measurement model (Bagozzi and Yi, 1988; Koufteros, 1999). The goodness-of-fit index (GFI) and the comparative fit index (CFI) had a value of 0.925 and 0.986, respectively. Both measures of incremental fit exceeded the recommended level of 0.9, thus, marginal acceptance can be given to this measure. The root mean square residual (RMR) was 0.065, close to the recommended level of 0.05 indicating marginal acceptance. The root-mean-square error of approximation (RMSEA) was 0.042 below the recommended level of 0.05. In summary, the various measures of overall goodness-of-fit for the model provided sufficient support for the results to be deemed an acceptable representation of the hypothesised constructs. Convergent validity can be assessed by the critical ratio (CR) values that are all statistically significant on the factor loadings (Dunn et al., 1994). As a rule of thumb, the CR needs to be greater than 1.96 or smaller than –1.96 at the 0.05 level of significance (Byrne, 2001; Hair et al., 2009). Results, shown in Table 5, indicated that all CR values were significant at the 0.05 level, effectively suggesting all indicators measured the same construct and providing satisfactory evidence of convergent validity and unidimensionality for each construct (Anderson and Gerbing, 1988). Moreover, the proportion of variance (R2) indicates how much of each variable’s variance is explained by its respective underlying factor. The item reliability (R2 value) was therefore used to estimate the reliability of a particular observe variable or item (Carr and Pearson, 1999; Koufteros, 1999). The values for all fourteen items met the 0.4 criterion, providing evidence of convergent validity (Carr and Pearson, 1999; Hair et al., 2009). Construct reliability provides a measure of the internal consistency and homogeneity of the items comprising a scale (Churchill, 1979). The reliability is the degree to which a set of two or more indicators share the measurement of a construct. Highly reliable constructs are those in which the indicators are highly intercorrelated, indicating they are all measuring the same latent construct. The range of values for reliability is between 0 and 1. Results, as shown in Table 5, indicated that all constructs displayed construct reliabilities in excess of the 0.6 recommended value (Churchill, 1979; Bagozzi and Yi, 1988; Sanchez-Rodriguez et al., 2005). In addition, a complementary measure to construct reliability is the average variance extracted showing directly the amount of variance that is captured by the construct in relation to the amount of variance due to the measurement error. Table 5 shows that among the average percentage of variance extracted (AVE) of the measures, perceived benefit had the lowest value of 0.507, indicating that 50.7% of the variance in the specified indicators was accounted for by the construct. The average variance extracted value of each construct in our model was higher than the recommended level of 50% (Fornell and Larcker, 1981).

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Table 6

Assessment of the discriminant validity Pair of constructs (Φ = 1)

Base model χ2 = 76.047(62)

2

χ (df)

χ2 difference (df)

1

MS-CO

100.796(63)

24.749(1)* *

2

MS-PB

80.402(63)

4.355(1)*

3

MS-PC

95.932(63)

19.885(1)**

4

MS-SC

106.759(63)

30.712(1)**

5

CO-PB

93.513(63)

17.466(1)**

6

CO-PC

187.336(63)

111.289(1)**

7

CO-SC

85.155(63)

9.108(1)**

8

PB-PC

104.517(63)

28.47(1)**

9

PB-SC

108.963(63)

32.916(1)**

10

PC-SC

85.155(63)

9.108(1)**

2

2

Notes: χ difference (df); difference in χ values and degree of freedom between models. *p < 0.05 and **p < 0.01.

Discriminant validity was assessed by constraining the correlation parameters between constructs to 1.0. The difference of the chi-square values between the constrained and unconstrained models was significant, suggesting the achievement of discriminant validity. A series of pairwise CFAs were conducted to assess the discriminant validity of the subdimensions using chi-square difference tests (Anderson and Gerbing, 1988). Table 6 shows the results of the chi-square difference tests on nine pairs of latent variables. Results indicated that all differences in χ2 between the fixed and free solutions were very significant, providing the evidence of discriminant validity. To summarise, the overall results of the goodness-of-fit of the model and the assessment of the measurement model lend substantial support for the proposed model.

4.3 SEM: hypotheses testing After confirming the fitness of the proposed model, the hypothesised relationships were examined. As can be seen from Table 7, the top management support (estimate = 0.696, CR > 1.96), cost of adoption (estimate = –0.371, CR < –1.96), perceived benefit (estimate = 0.472, CR > 1.96), and perceived complexity (estimate = –0.462, CR < –1.96) were found to have significant influences on carriers’ intention to use a PLIS at the p = 0.05 significance level. However, there was a lack of support for a significant positive relationship between security concern (estimate = 0.063, CR < 1.96) and use intention. Results indicated that top management support and perceived benefit had a significant positive relationship with use intention, respectively; whereas, the cost of adoption and perceived complexity had a significant negative relationship with use intention, respectively. The above findings are consistent with those from the studies of Davis et al. (1989), Soliman and Janz (2004), and Lu et al. (2007).

Factors influencing the use intention of PLIS by ocean carriers Table 7

43

Results of the SEM

Relationships

Estimate

SEa

CRb

P

Sign Supported

Top management support → Use intention

0.696

c

0.232

2.994 0.003

+

Yes

Cost of adoption → Use intention

–0.371

0.125

–2.962 0.003

–

Yes

Perceived benefit → Use intention

0.472

0.208

2.272 0.023

+

Yes

Perceived complexity → Use intention

–0.462

0.190

–2.437 0.015

–

Yes

Security concern → Use intention

0.063

0.082

0.764 0.445

+

No

a

Notes: SE is an estimate of the standard error of the covariance. b CR is the critical ration obtained by dividing the covariance estimate by its standard error. c Italics values are CRs exceeding 1.96 at the 0.05 level of significance. Fit indices: χ2 = 76.047 (p = 0.108), df = 62, χ2/df = 1.227, GFI = 0.925, AGFI = 0.873, CFI = 0.985, RMR = 0.065, RMSEA = 0.042.

5

Discussions of findings

The purpose of this study was to develop a model of the use of a PLIS from an ocean carrier’s perspective. Results indicate that a positive significant relationship was found between carrier perceptions of top management support and their use intention for a PLIS (H1). This implies that shipping managers who enthusiastically support and encourage employees to use PLIS increase carriers’ PLIS use intention. The proposed path from perceived benefit to use intention was also supported in this study (H3). This result suggested that the perceived benefits of reducing firm operational costs and providing timely information for decision making are important concerns that may lead carriers to use a PLIS. Specifically, the results also indicated that a significant negative relationship was found between cost of adoption and PLIS use intention (H2). This suggests that the cost of maintenance and investment in training employees are important concerns for carriers to consider regarding the use of a PLIS. They will be more willing to use a PLIS if the cost is not high for their businesses. Perceived complexity was similarly found to have a significantly negative relationship with regard to PLIS use (H4). This implies that skills required and difficult to integrate into users’ work practices are the major barriers stopping the use of a PLIS. If the system is simpler and more easily integrated into their work, carriers will be more willing to use it. However, the effect of security concern (H5) on use intention was not supported in this study. The finding is consistent with that recorded in the Lu et al. (2007) report implying that security concern is not an important factor influencing carrier use intention toward a PLIS in this study. Based on the subsequent interviews with the respondent carriers, the major reasons are since the carriers and port authorities have increasingly emphasised the security issue, they believe the security measures provided by port authorities to protect online transactions and data transmissions through internet services are reliable. Last but not least, legal protection of online transactions has been established in Taiwan and in turn makes carriers feel safe to use a PLIS. Accordingly, carriers tend not to worry that their commercial secrets will be released.

44

6


Research and managerial implications

The model has a number of implications for research and practice. First, the results confirmed that top management support and perceived benefit are important factors influencing carriers’ adoption of a PLIS. In particular, port authorities should timely increase the awareness of advantages and benefits of adopting a PLIS in ocean carrier settings. By frequently communicating or meeting with ocean carriers, the top managers of ocean carriers could perceive the benefits gained from adopting a PLIS and in turn encourage their employees to use it. Second, perceived complexity had a strong direct effect on use intention. This suggests that efforts to improve perceived ease of use could have a strong influence on use intention. The port authority could provide technical support and training programmes to promote the use of a PLIS. Also, software developers must address quick responses as an important design objective when developing systems. Finally, the influences of security concern on carriers’ intention to use PLISs were not supported in this study. In this respect, port authorities could make an effort to mitigate negative feelings regarding security by advertising that their logistics information systems are safe and by publicly explaining what sorts of security controls they utilise. Needless to say, port authorities should strengthen the security of their port internet services so as to decrease the likelihood of negative experiences. The findings indicated that the proposed model on the adoption of a PLIS is acceptable. Future researchers may build on this model to identify and examine other factors that may influence carriers’ use of a PLIS, such as information intensity, technology competence, and external pressures. The integration of these constructs into the model will help researchers and shipping practitioners to further grasp the factors that influence the development of a logistics information system or the internet services in their industry. This study specifically examines the major factors affecting the use intention in the PLIS context. According to the TAM, both direct and indirect effects might exist among the factors (i.e., perceived ease of use, perceived usefulness, and attitude toward using) influencing the adoption of new technologies. TAM model has been a prominent theoretical model widely used to explain the attitude and behavioural intention of using new technology or the internet. Further research therefore, could employ the TAM model to examine both direct and indirect effects of determining factors on carriers’ attitudes and behavioural intention with regard to the adoption of a PLIS.

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Appendix The factors influencing the acceptance of the international port logistics information system Top management support MS1

The manager enthusiastically supports the adoption of a port logistics information system.

MS2

The manager has allocated adequate resources to support the adoption of an international port logistics information system.

MS3

Top management actively encourages employees to use a port logistics information system in their daily tasks.

Cost of adoption CO1

The costs of adoption of a port logistics information system are far greater than the benefits.

CO2

The cost of maintenance and support of a port logistics information system are very high for our business.

CO3

The amount of money and time invested in training employees to use a port logistics information system are very high.

Perceived benefit PB1

The use of a port logistics information system is useful with regard to communication with the port authority.

PB2

The use of port logistics information system will reduce firm’s operational costs.

PB3

The adoption of a port logistics information system will provide timely information for the purpose of decision making.

Perceived complexity PC1

The skills required to use a port logistics information system are too complex for our employees.

PC2

Integrating a port logistics information system into our current work practices will be difficult.

PC3

Implementing the changes caused by the adoption of a port logistics information system is not compatible with our firm’s values and beliefs.

Security concern SC1 SC2 SC3

I believe that the use of a port logistics information system is not safety. I believe that the use of a port logistics information system is not protected by the law. I believe that the use of a port logistics information system will release our company’s commercial secrets.

Use intention UI1 UI2 UI3

My firm intends to use a port logistics information system. My firm likes to use a port logistics information system. My firm will inform others to use a port logistics information system.