Evaluating Measurement Models for Web ...

International Journal of Information Systems for Logistics and Management Vol. 5, No. 2 (2010) 21-30

http://www.knu.edu.tw/academe/englishweb/web/ijislmweb/index.html

Evaluating Measurement Models for Web Purchasing Intention Bing-Yi Lin1, Ping-Ju Wu2 and Chi-I Hsu3 1,3Department 2Department

of Information Management, Kainan University, of Information Management, National Sun Yat-Sen University

Received 20 June 2009; received in revised form 31 November 2009; accepted 5 February 2010

ABSTRACT This study is mainly to evaluate measurement models for web purchasing intention, which reflects to a tendency of attitudes and behaviors toward the online purchasing behaviors. Four dimensions of web purchasing intention are proposed based on a literature review, including (1) Information Provision: product-related information that e-retailing users receive from the Internet; (2) Price: the final dealing prices of products on e-retailing websites; (3) Customer Service: services that e-retailing websites offer for customers in the e-transactions and post-sale process; (4) Alternative Evaluation: issues that affect an individual's decision making when purchasing online. This study developed a questionnaire and delivered to consumers with web purchasing experience. The method of Structural Equation Modeling (SEM) is adopted to verify the proposed models. Among the constructs, “Information Provision” especially can reflect the web purchasing intention significantly, and in turn the “Alternative Evaluation”, the “Price” and the last is the “Customer Service”. The results confirm the impact of web purchasing intention on actual customer behavior and the single second-order factor model has also been proven to have better capacity to explain customer loyalty. Keywords: E-Commerce, web purchasing intention, measurement, structural equation modeling.

1. INTRODUCTION As internet has the advantage of being accessible without time and space limit, plus all kinds of convenient applications, it has attracted a big number of users. With the blooming development of the internet, several new operation models have been used to innovate and improve commercial activities, and hence to create new marketing opportunities. Web purchasing is one fastincreasing market example According to Market Intelligence Center (MIC, 2006), the web purchasing market scale of Taiwan was NT$89.3 billion in 2006, and will keep growing at a 2-digit rate. It was estimated that the market scale will be up to NT$131.1 billion in 2007. The web purchasing market is becoming a main shopping channel for consumers with its ample scope and stable

growing speed. By providing a variety of products and services through the internet, buyers and sellers can proceed with different transaction activities, such as searching for information, placing orders, making payments, delivering digitized products, or controlling physical logistics. Although many business practitioners have established their own industries on the internet, many of them merely have the basic functions of e-catalogue provision and products distribution, with insufficient offers for a complete e-transaction process and post-purchasing customer services. This highlights the “low entry threshold” and “high ratio of being weeded out” nature of the web purchasing market. Web purchasing intention can reflect the degree to which an individual intends to act such behavior during

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International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010)

the web purchasing process, and further can lead to his/ her actual purchasing behavior (Shim et al., 2001; Hsu and Chiu, 2004). In order to survive in a highly competitive environment, it becomes very important for web purchasing practitioners to understand customers’ purchasing intention and provide them with appropriate products and services. This research tends to develop measurement models for web purchasing intention to understand the factors contributing to customers’ web purchasing behaviors. To our best knowledge, this is the first study that attempt to examine and compare measurement models for web purchasing intension. As for e-retailing websites, the main source of profits comes from loyal customers (Srinivasan et al., 2002), this research aims to investigate the structural relationship between web purchasing intention and customer loyalty, in order to understand the influence of web purchasing intention on customer loyalty. SEM and LISREL 8.71 are used to analyze the data collected from questionnaires. Through verifying the fitness of the established research constructs and developed measurement models, the influential correlation between web purchasing intention and customer loyalty can be recognized. 2. LITERATURE REVIEW Over the last decade, internet commerce has been growing dramatically, and online shopping is becoming almost commonplace. A number of attractive attributes of internet shopping have been pointed out, including time- and money-saving; convenience or easy accessibility; the shopper's ability to screen and select a wide range of alternatives, and the availability of information for making purchasing or ordering decisions (Breitenbach and Van Doren, 1998; Then and Delong, 1999; Crawford, 2000; Schaeffer, 2000; Ray, 2001). However, when one purchases on the web, a variety of factors and concerns may contribute to customers' intention to buy products online. As for the availability of information for making purchasing or ordering decisions, factors involved in online decision making are e-information search, e-evaluation and e-post purchase evaluation (Jayawardhena et al., 2003). Due to the communication structures provided by the internet, customers can become more empowered as they surf the net for the best prices, product information, the latest consumer innovations or special offers. The internet serves as a channel allowing users to gather information; bulletin boards and news groups provide the opportunity for consumers to exchange their information. Searching for and chatting with someone is one of the major uses of the internet and e-shoppers can turn to each other for any kind of advices they need. E-consumers share the need for post purchase reassurance with their traditional counterparts and hence internet

virtual communities provide a forum for online shoppers to share their experiences and recommendations. Ramus and Nielsen’s (2005) conducted a research investigating online grocery retailing and found out that although online shopping enabled customers to have a wide range of available information about stores and products, certain product categories such as food that is sensitive to shipping time and heavy set natural can limit the realistic range of stores and products to choose from. Since consumers can have all kinds of products information available online for comparison, companies tend to use those with the most competitive prices to attract consumers (Covaleski, 1997). Kannan and Kopalle (2001) also mentioned that price is a key factor of web purchasing. For example, price on sales (Kim and Kim, 2004), special offers for members (Yen et al., 2005), and discounts activity (Kim and Kim, 2004) may significantly influence customers’ purchasing intention. Kalakota and Whinston (1997) indicated that by including customer services into the main procedure of web purchasing can elevate customers’ purchasing intention during the interactive process. Customer services for online shopping include responses from retailing websites to the requests of customer problems (Yen et al., 2005), prompt post-sale service (Lim and Palvia, 2001), complete post-sale service (Lim and Palvia, 2001), and et al. Other than the information, price, and customer service factors mentioned above, it has been suggested that convenience is the main reason why consumers use the internet for the purpose of purchasing (e.g., Jarvenpaa and Todd, 1997). Brown et al. (2003) conducted a research to investigate whether internet shoppers are primarily motivated by convenience, and the empirical results showed that other factors such as product type, prior purchase, and gender are more likely to influence online customers’ purchase intention. Consumers’ online purchasing experience may be enhanced by incentive programs such as saved shopping lists or personalized help, point- and incentive-based premium and gift programs and cumulative discounts or rebates based on purchase amount (Breitenbach and Van Doren, 1998). Kim and Kim (2004) conducted a survey research by mailing to 303 adults who had a computer at home with the access to the internet in the USA to investigate the perceived four factors of online shopping: transaction/cost; incentive programs, site design and interactivity. The interactivity of the web allows users to personalize and customize their experience through keyword search, dealer locator, comments and online ordering, and this experience has had a significant positive effect on the quality rating of web sites (Ghose and Dou, 1998). Along with the attributes discussed above, the concept autonomy defined as no salesperson interrupting as what usually happens in a physical shop has become a reason consumers make online purchases over the internet (Chang et al., 2005).

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B. Y. Lin et al.: Evaluating Measurement Models for Web Purchasing Intention

Table 1. Items of all dimensions Dimension

Item

Content

Information Provision

INF1 INF2 INF3 INF4

searching for product information new-product information using the information service functions provided on the website searching for specific products

Price

PRI1 PRI2 PRI3 CUS1 CUS2 CUS3

on sales special offers for members discounts activity responses from retailing websites to the requests of customer problems prompt post-sale service complete post-sale service

ALT1 ALT2 ALT3

customization convenience autonomy

Customer Service Alternative Evaluation

Table 2. Item placement ratios

3. METHODOLOGY

Actual Categories

3.1 Research Constructs

Target Category INF ALT CUS PRI N/A Total % Hits

Based on related literatures reviewed, four constructs are established in this research (as shown in Table 1); they are information provision, price, customer service, and alternative evaluation. Information provision is defined in this research as “providing product related information for e-retailing users through the internet”. Price is defined as the actual list price of products sold on e-retailing websites. Customer service is defined as the services provided by e-retailing websites for consumers during the whole transaction and post-purchasing process. Alternative evaluation is defined as the factors affecting an individual’s evaluation on alternative selections when purchasing products online. 3.2 Scale Development As for the scale development, following the steps suggested by Davis (1989), we invited two online purchasing consumers, a website manager, and a graduate student from MIS doctoral program to categorize all measurement variables into four constructs according to their individual cognition, in order to ensure the construct validity and examine whether there is any ambiguous construct item. The categorized results are shown in Table 2, which indicate that the correct hit ratios of the four subjects are all higher than 84%, with an overall hit ratio of 91%. Since most of the items can be precisely classified into the estimated constructs, the measurement scales developed in this research for the four constructs have been recognized to have sufficient convergent validity and discriminate validity.

INF ALT CUS PRI

14 1

2 1 12

10

1

Total Item Placement: 52

11 Hits: 47

16 12 12 12

87 84 100 92

Overall Hit Ratio: 91%

3.3 Measurement Models This research develops four measurement models for web purchasing intention and SEM is adapted to verity the validity of the proposed measurement models. According to the measurement scales developed in this research, four models are proposed as shown in Figs. 1~4 below. In Model 1 (M1), a single factor - web Purchasing Intention is used to represent all 13 items; in Model 2 (M2), four independent factors are adopted; in Model 3 (M3), these four factors are made to be correlative to each other; model 4 (M4) shows a structure with single second-order factor. 3.4 Structural Models As loyal customers are the main profit source for retailing websites (Srinivasan et al., 2002), this research adds “loyalty” to the research structural model in order to understand the impact of web purchasing intention on the loyal behaviors of web users. Based on the definition proposed by (Stum and Thiry, 1991), the items developed for web users’ loyalty include: repeated usage, extensive consumption, reputation establishment, and

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INF 1

NFI 1

INF 2

INF 2

INF 3

INF 3

INF 4

INF 4

INF

ALT 1

ALT 1 Web Purchasing Intention

ALT 2 ALT 3

ALT 3

CUS 1

CUS 1

CUS 2

CUS 2

CUS 3

CUS 3

PRI 1

PRI 1

PRI 2

PRI 2

PRI 3

PRI 3 Fig. 1. M1: Single first-order factor

INF 3

INF 3

INF

INF 4 ALT 1

ALT 1 ALT

ALT 2

ALT Web Purchasing Intention

ALT 3

ALT 3

CUS 1

CUS 1 CUS 2

PRI

Fig. 3. M3: Four correlated first-order factor

INF 2

INF

INF 4

ALT 2

CUS

INF 1

INF 1 INF 2

ALT

ALT 2

CUS

CUS 2

CUS

CUS 3

CUS 3 PRI 1

PRI 1 PRI 2

PRI 2

PRI

PRI

PRI 3

PRI 3 Fig. 2. M2: Four uncorrelated first-order factor

Fig. 4. M4: Single second-order factor

immunity against promotions from other competitors. “Repeated usage” refers to the adoption of using the same website when a user develops a fixed preference for that website (LOY1). “Extensive consumption” refers to the action for further obtaining related services of certain preferred websites (LOY2). “Reputation establishment” refers to the active recommendation of certain website by a user who promotes others to use the website for shopping (LOY3) and to purchase the products sold on this website (LOY4). “Immunity against promotions from other competitors” refers to the immunity a user has against the promotions (such as providing more services

at the same or lower price) from other competitors of the website which the user decides to continue to use based on his/her own preference (LOY5). 4. DATA ANALYSIS 4.1 Data Analysis Process The data analysis process in this research is to introduce the analysis method - SEM first, and then to explain the questionnaire development, descriptive statistics of samples, and the analysis results of normal

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Table 3. Descriptive statistics of respondents’ characteristics (N = 412) Characteristic

Range

Frequency

Percentage (%)

Gender

Male Female

183 229

44.4 55.6

44.4 100.0

Age

20 21-25 26-30 31-35 36-40 41

84 243 47 23 8 7

20.4 59.0 11.4 5.6 1.9 1.7

20.4 79.4 90.8 96.4 98.3 100.0

Career

Student Military, Civil servant, Teacher Finance Industrial and Commercial Services Law service Informatics Medical industry Public communications Manufacturing Free agency Others

337 32 3 8 1 26 1 4 1 2 7

79.4 7.8 0.7 1.9 0.2 6.3 0.2 1.0 0.2 0.5 1.7

79.4 87.1 87.9 89.8 90.0 96.4 96.6 97.6 97.8 98.3 100.0

Education

Junior college College Graduate school

13 310 89

3.2 75.2 21.6

3.2 78.4 100.0

Purchase frequency within one year

1-2 3-4 5-6 7-8 9-10 11

158 109 63 19 22 41

38.3 26.5 15.3 4.6 5.3 10.1

38.3 64.8 80.1 84.7 90.0 100.0

Average of purchase expenditure

NT$500 NT$501-1,000 NT$1,001-2,500 NT$2,501-5,000 NT$5,001-10,000 NT$10,001

139 150 72 35 10 6

33.7 36.4 17.5 8.5 2.4 1.5

33.7 70.1 87.6 96.1 98.5 100.0

distributed test, exploratory factor analysis (EFA), questionnaire reliability and SEM. In the field of social science research, SEM is often used to construct and test a research model. Through verifying the fitness degree between the model and real data, the theoretical architecture is examined to decide whether it can be established or not. In the field of multivariate statistics, SEM is mainly used to investigate the relationship between variables, and to clearly display the factor loadings and intercorrelations between the research model and variables by path diagrams. It combines both the concepts of factor analysis and path analysis and is especially appropriate for analyzing the data of theory-based studies in social and behavior areas, which

Cumulative percentage (%)

contributes to the increasing number of studies using SEM in various fields (Chiu, 2004). SEM adopts Confirmatory Factor Analysis (CFA), which more reinforces the reasonability of theory and logics compared to exploratory factor analysis; by integrating CFA and the conventional path analysis, the investigation of the cause-and-effect relationship between latent variables can also include the measurement of disturbance. SEM analysis is to estimate the measurement model of each construct first, and then test the theoretical structure and hypotheses, which includes two steps: (1) measurement model analysis: the loading relationships between latent variables and their corresponding observable variables is analyzed first and then

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Table 4. Test of normal distribution

Table 5. The result of exploratory factor analysis

Construct

Item

Mean

SD

Skewness

Kurtosis

Construct

Item

INF

ALT

CUS

PRI

LOY

INF

INF1 INF2 INF3 INF4

3.80 3.94 3.66 3.91

.821 .790 .841 .809

-.232 -.543 -.081 -.339

-.377 .229 -.488 -.425

INF

INF1 INF2 INF3 INF4

.758 .828 .700 .652

.116 .143 .130 .283

.157 -.030 .255 -.086

.187 .027 .030 .232

.223 .193 .207 .083

ALT

ALT1 ALT2 ALT3

3.68 3.85 3.72

.855 .866 .929

-.441 -.569 -.533

.104 .227 .115

ALT

ALT1 ALT2 ALT3

.127 .201 .292

.767 .654 .630

.107 -.082 .221

.071 .261 .205

.173 .271 .058

CUS

CUS1 CUS2 CUS3

2.76 3.14 2.75

.895 .942 .960

.000 .025 .065

-.076 -.450 -.201

CUS

CUS1 CUS2 CUS3

.003 .074 .137

-.025 .399 .038

.765 .594 .820

.143 -.010 .104

.233 -.263 .128

PRI

PRI1 PRI2 PRI3

3.71 3.58 3.44

.944 .918 1.015

-.387 -.216 -.226

-.191 -.178 -.271

PRI

PRI1 PRI2 PRI3

.390 .184 -.054

.230 .027 .198

-.018 .189 .125

.711 .833 .805

.021 .197 .140

LOY

LOY1 LOY2 LOY3 LOY4 LOY5

3.54 3.82 3.43 3.58 3.04

1.001 .810 .841 .863 1.036

-.617 -.735 -.331 -.504 -.140

.027 1.007 .236 .382 -.468

LOY

LOY1 LOY2 LOY3 LOY4 LOY5

.146 .316 .195 .305 .026

.005 .469 .252 .175 .037

-.002 -.143 .061 .206 .419

.114 .011 .225 .041 .118

.799 .632 .755 .731 .605

KMO: 0.815

the measurement models are evaluated by the Confirmatory Factor Analysis (CFA) method; (2) structural model analysis: path diagrams are used to display the structural relationships between latent variables and the structural model is then examined to test the hypothetical relationships and the path coefficients. 4.2 Data Collection The survey method is used to collect the empirical data. Through statistic index, the measurement can be ensured to achieve the expected quality, and SEM is used to analyze data and test the measurement models. A questionnaire is developed and a five-point scale is applied to the measurement of total 18 questionnaire items. In order to effectively represent the constructs in the research model, this study adopts certain procedures to ensure that the measurement can achieve the expected quality. In respect of the content validity, this study refers to the viewpoints of the TPB theory and relevant papers to develop the questionnaire items. In respect of the face validity, ten users with internet shopping experiences were invited to discuss the questionnaire wording in order to ensure that each of the questionnaire items is clear and understandable. To overcome the difficulty of conducting a survey based on the huge scope of online shopping matrix, this research collected data samples by random questionnaire distribution aiming at users with online shopping experiences. 450 questionnaires were distributed to

college students (including undergraduate and graduate), staffs and faculties with internet shopping experiences from a university in northern Taiwan. Out of the total 450 questionnaires distributed, 412 valid samples were collected after deducting the samples with more than three items not answered. The effective response rate is 91.6%. The descriptive statistics of respondents’ characteristics are shown in Table 3. As for normal distribution test, from Table 4 we can see that all Skewness and Kurtosis coefficients are between 2 and –2. (Tabachnick and Fidell, 2001). In the pretest, 100 out of 412 samples were selected randomly to conduct EFA. All the 18 items were used to run factor analysis by SPSS 13.0 for Windows; principal components were adopted to select those with eigenvalues bigger than 1, and varimax was used to turn the axle. From Table 5, the result of EFA supports the fitness of the factor structure proposed in this research. 4.3 Measurement Models Through CFA, SEM tests the validity of each questionnaire item by evaluating the convergent validity and discriminate validity in two parts: (1) whether all the regression coefficients are significant? (2) whether the measurement model itself is sufficient to explain the data variations? The significance of coefficients can be decided by examining if the t-values are greater than 1.96 at the significant level of 0.05. The values of the factor loading between all latent variables and

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their first observable variable are fixed as 1 for the purpose of standardization. The overall fitness degree of the measurement model and real data can be assessed by Comparative Fit Index (CFI) (Joreskog and Sorbom, 1993). The measurement model is accepted when the CFI value is greater than 0.95 (Bentler, 1992; Bentler, 1995). The fitness of the whole model may also be judged by means of multiple indexes (Chiu, 2004). Some are described as follows: ➢ Chi-square (χ2) ➢ χ2/df: smaller than 3 for good of fit (Chin and Todd, 1995). ➢ Normed Fit Index (NFI) and Non-Normed FI (NNFI) (Bentler and Bonett, 1980): NFI uses the concept of nested model to reflect the divergence degree between hypothetical model and independent model. NNFI includes the factor of degrees of freedom (df); if the value is greater than 0.9, then it’s for good of fit (Hu and Bentler, 1999). ➢ Goodness of fit (GFI) (Subhash, 1996) uses a hypothetical model to explain the ratio of the variance to covariance, which should be greater than 0.8 for good of fit. Adjusted GFI (AGFI) (Subhash, 1996): compared to GFI, the degree of freedom is added and the value should be greater than 0.8 for good of fit. ➢ Parsimony Goodness-of-fit Index (PGFI) (James et al., 1982): explains the hypothetical model can reflect the ratio of variance to covariance, and considers the estimated number of parameters to express the parsimonious degree of the model, which should be greater than 0.5 for good of fit (Mulaik et al., 1989). ➢ Root Mean Square Error of Approximation (RMSEA) (Browne and Cudeck, 1993). Used to evaluate the divergent degree of the theoretical model and saturated model, which is suggested to be smaller than 3 for acceptable fit (McDonald and Ho, 2002). Standardized Root Mean square Residual (SRMR): used to reflect the overall disturbance number, which should be smaller than 0.08 for good of fit (Hu and Bentler, 1999). Lrangian Multiplier Test (LM test) is adopted to examine if an individual parameter should be included into the model, and the results show that all the items

Table 6. Correlation matrix & reliability analysis PRI LOY Cronbach’s α

INF ALT CUS INF ALT CUS PRI LOY

1.00 0.64 0.20 0.55 0.50

1.00 0.32 0.40 0.53

1.00 0.27 0.38

1.00 0.49

0.808 0.705 0.653 0.785 0.832

1.00

C.R. 0.83467 0.68073 0.68936 0.79043 0.80792

are appropriate to be included into the model (as listed in Table 7). After goodness-of-fit index analysis, M1 and M2 failed the empirical data test, whereas M3 and M4 satisfied all the evaluation standards, with significantly better goodness-of-fit. This indicates that M3 and M4 can both be used as measurement models for web purchasing intention studies. The correlation matrix and reliability values for all constructs are shown in Table 6. The analysis result of reliability values shows that except for the construct of Customer Service, the values of Cronbach’s α are all above the 0.7 level (Cronbach, 1947) and thus satisfy the reliability requirement. In addition, composite reliability (CR) is used to measure the internal consistency of latent variables and the CR coefficients are all greater than 0.6 as suggested by Fornell and Larcker (1981), which means that the developed measurement items have significant CR on latent constructs. 4.4 Structural Models This research establishes two structure models M5 and M6 based on M3 and M4 (as depicted in Fig. 5 and Fig. 6 below) to test the impact of web purchasing intention on loyal behaviors. The analysis results of the structure models are shown in Table 8, which lead to two conclusions: (1) according to the goodness-of-fit index, these two models are acceptable; (2) the goodness of fit in M5 is partially better than M6. It is worth noticing that the construct of “Information Provision” in M5 does not have significant impact on customer loyal behavior, whereas all the factor loadings in M6 are significant (see Table 9) and the SMC (Square Multiple Correlations) for the structural equations (which indicate

Table 7. The result of measurement model χ2 Recommended Value M1 (single first-order factor) M2 (four uncorrelated first-order factor) M3 (four correlated first-order factor) M4 (single second-order factor)

507.05 302.54 100.03 107.08

df

χ2/df

CFI

NFI

NNFI

GFI

AGFI

PGFI

RMSEA

SRMR

0.95

>0.90

>0.90

>0.80

>0.80

>0.50

0.90

>0.80

>0.80

>0.50