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Omega 31 (2003) 349 – 363 www.elsevier.com/locate/dsw
Assessing the consumer decision process in the digital marketplace Thompson S.H. Teo∗ , Yon Ding Yeong Department of Decision Sciences, School of Business, National University of Singapore, 1 Business Link, 117591 Singapore Received 21 November 2001; accepted 7 April 2003
Abstract This research focuses on the consumer decision process in the context of the online shopping environment in Singapore. An Internet survey was implemented and 1133 responses were received. Using structural equation modeling, our 3ndings show that perceived risk has a negative relationship with consumers’ overall evaluation of the deal, and overall evaluation of the deal has a positive relationship with consumers’ willingness to buy online. In addition, there is a positive relationship between perceived bene3ts of search and overall deal evaluation. The implications of the above results are discussed and suggestions for future research are also proposed. ? 2003 Elsevier Ltd. All rights reserved. Keywords: Consumer decision process; Internet; Search; Deal evaluation; Risk; Electronic commerce
1. Introduction The Internet o8ers many advantages to businesses, such as the ability to reach new segments since products can be sold globally rather than locally or regionally, and the potential to reduce cost through streamlining of supply chain. In addition to the attractiveness of a global reach, the Internet also allows businesses to have virtually unlimited shelf space compared to traditional stores where decisions regarding product assortment and shelf space allocation need to be made. The US online retail sales are expected to reach US$104 billion in 2005 and US$130 billion in 2006, up from US$34 billion in 2001 [1]. The projected value of worldwide e-commerce is expected to surge to US$5 trillion in 2005, up from US$354 billion in 2000 [2]. Similarly, the Asia-Paci3c e-commerce market is growing rapidly. E-commerce in the Asia-Paci3c is projected to grow from US$39.4 billion in 2000 to US$338 billion by 2004. Asia is on the verge ∗ Corresponding author. Tel.: +65-874-3036; fax: +65-7792621. E-mail address:
[email protected] (T.S.H. Teo).
of an unprecedented e-commerce boom with the number of active Internet users expected to increase to more than 27% of the world’s online population by 2004. Speci3cally, the number of Internet users will increase from 49 million in 2000 to 173 million in 2004, a 38% compound annual growth rate [3]. Overall, the Asia-Paci3c region appears ready to take the e-commerce plunge, with strong Internet penetration in countries such as New Zealand, Australia as well as Singapore. This strong growth in e-commerce makes it necessary for marketers to know their customers well. Marketers can perform their function better if they understand what decision process potential customers go through when considering the adoption of e-commerce [4]. With this in mind, this research is aimed at understanding the consumer decision-making process of online shoppers in order to better design marketing strategies and create more e8ective websites for achieving marketing objectives. As research in marketing is increasingly being marginalized and focuses on speci3c areas such as strategy, quality, satisfaction and product design, Lehmann [5] suggested the need for a broader focus, building on more general theories that link multiple constructs. Narrowness of perspective may help the advancement of academic knowledge, but
0305-0483/03/$ - see front matter ? 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0305-0483(03)00055-0
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it can also be destructively limiting in a problem-oriented 3eld such as marketing [6]. Therefore, in this research, we applied consumer behavior in a more generalized context. This research attempts to 3ll some of the gaps in the research on consumer decision making by focusing on several di8erent stages of the consumer decision process in the context of the virtual shopping environment in Singapore where speed, cost, scope and quality might vary from that of a retail store [5]. Further, an attempt is made to secure empirical data on the applicability of the Engel, Blackwell and Miniard (EBM) model [7] in an online context. The intention is to examine the decision-making and choice behavior of consumers prior to their purchase online. 2. Literature review Business use of the Internet is still in the early stages of development, and much needs to be learned about how consumers will respond to these new forms of electronic retailers. In recent years, online shoppers have been recognized as a specialized segment of the market for a variety of products and services and their behavior as consumers have now started to receive increasing attention among marketers and public policy makers (e.g. [8–12]). Research on consumer behavior in interactive settings is relatively new. Peterson and Balasubramanian [13] compared the Internet to conventional retail and catalog distribution channels on dimensions of product features and kinds of consumer decision process. Lynch [14] presented a study in which price and quality search costs were manipulated in an electronic store. It was found that lowering search costs increased price sensitivity only when quality search costs were high and comparison with stores was diKcult. Rangaswamy [15] presented 3ndings on how consumer choice in electronic settings di8ered from traditional in-store choice. Likewise, research on consumer decision making has been far from systematic, focusing on a speci3c stage rather than several stages of the process. For the past 20 years, the dominant paradigm in consumer behavior has been information processing. Empirical research on consumers’ information-search behavior has a long tradition in marketing. For example, Beatty and Smith [16] showed that search e8ort is related positively to purchase involvement, time availability and attitude towards shopping. Srinivasan and Ratchford [17] attempted to develop and build a comprehensive search model that provides insights into the determinants of search. Moorthy et al. [18] presented a theoretical model that identi3es not only what factors affect consumers’ search behavior but also how these factors interact with each other. They explored the e8ect of prior brand perceptions on information search. Similarly, Bei and Widdows [19] ascertained the extent to which consumers achieved highest value for money under di8erent conditions and examined the inLuences of information on consumers’ purchase decisions in an experimental setting,
with allowance for the e8ects of prior product knowledge and product involvement. Also, Hoque and Lohse [20] provided a theoretical basis for predicting how subtle differences in the user interface design inLuences information search costs. Practitioners and academics alike have speculated that as consumer decision move online, the cognitive and social context of decision-making will change in ways that are as yet only partially understood [21–23]. For instance, at the individual cognitive level, the proportion of consumers that engage in a sub-optimal degree of search is likely to decrease with a lower information search cost for the typical online consumer. This is due to the increasing availability of extensive, easily retrievable and easily stored databases relevant to product or service purchases. Alba et al. [24] speculate that if online retailing reduces the information search costs for price information, consumers will become more price sensitive. Others have explored consumer behavior at the evaluation stage [25–30]. Some studies examined consumer behavior at the purchase stage and one factor that research has identi3ed as a critical determinant of consumers’ willingness to buy a new product or brand is the perceived risk associated with the purchase [31]. Previous research has also attempted to integrate these various individual stages of the decision-making process. For example, Hollander and Rassuli [32] attempt to integrate these stages by examining consumer decisions of surrogate shoppers who shopped on behalf of clients and had a 3duciary responsibility to them. They also provided a framework for investigating the marketing management implications of the shoppers. 3. Research framework Consumer behavior involves a very wide variety of personal and situational variables. Many models that attempt to explain or predict consumer decision-making and resulting actions have been proposed. They are an abstract representation of the consumer decision process and simplify the description of complex consumer behavior. With this simpli3ed picture, the marketer can be much more e8ective in understanding how consumers respond to his marketing effort. Knowing this, he can better design marketing plans to encourage sales. Some of the so-called grand models of consumer behavior which attempt to comprehensively explain all those aspects of the buying situation which their creators deem to be signi3cant are: Nicosia model, Howard–Sheth model and EBM model. They provide profound insights into the nature of consumer buying and consuming which the human mind, otherwise cannot grasp the immense complexities of this phenomenon [33]. Here, we will examine the EBM model [7], which is a development of the original Engel, Kollat and Blackwell (EKB) model, 3rst introduced in 1968 [34].
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The process begins with the stimulation of a need where the consumer is faced with an imbalance between the actual and desired states of a need, which may be suKciently large to stimulate search. After identifying the need, the consumer searches for information about the various alternatives available to satisfy the need. The consumer’s information search will eventually generate a set of preferred alternatives. The consumer will use the information stored in memory and those obtained from outside sources to develop a set of criteria. These criteria will help the consumer evaluate and compare alternatives. Purchase is made based on the chosen alternative. Post-purchase evaluation is carried out with a view to aid future decision-making. Good experience with a brand will provide information that may lead the customer to that brand when a similar product is to be purchased. Dissatisfaction may result in post-purchase dissonance. In this study, we focus on the core decision-making process, i.e., the information search, alternative evaluation and purchase. The hypothesized model in Fig. 2 incorporates the e8ects of the perceived bene3ts of search, the 3nancial and performance risk to the overall evaluation of the deal and, ultimately on the willingness to purchase online. The arrows indicate the proposed relationships and the plus and minus signs indicate the nature of their relationships. The various terms in the model are de3ned in Table 1.
Need Recognition
Information Search
Alternative Evaluation
Purchase
After Purchase Evaluation Fig. 1. Consumer decision process model.
The EBM model is an attempt to theoretically stimulate the decision-making process of consumers [35]. One of the advantages of the EBM model is its generality and its applicability to a wide range of situations. It is also coherent in its presentation of consumer behavior and it speci3cally introduces memory, information processing and consideration of both positive and negative purchase outcomes, which may not be explicitly considered in other models [33]. It is explanatory but can be used to predict and is suitable for comparison of unrelated alternatives, assumes rational consumer and considers post-purchase dissonance. As such, its strength lies in its ability to help interpret a wide range of research 3ndings in almost any situation [36]. The model is plausible in that on the surface it seems to make sense. This quality is essential for the theory to be easily accepted by other people. Although there are several variations of the EBM model, we focus on the core decision process (that captures the essence of the EBM model) as shown in Fig. 1 [36].
4. Research hypotheses 4.1. Total amount of external search e4ort External search e8ort is the degree of attention, perception and e8ort directed toward obtaining environmental data or information related to the speci3c purchase under consideration [16]. In other words, it is a buyer’s willingness to search for additional information. This e8ort is a8ected by information that consumers obtain prior to considering the purchase. According to this de3nition, information obtained from memory and information obtained passively are not part of the external search e8ort. Conceptually, these are
+H4
Perceived Benefits of Search
+H1
External Search Effort
+H5
+H2
Overall Deal Evaluation
+H6 Willingness to Buy
-H3 Perceived Risk
Information Search Stage
Alternative Evaluation Stage
Purchase Stage
Fig. 2. Hypothesized model of core consumer decision process in the digital marketplace.
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Table 1 De3nition of constructs Construct
De3nition
Source
Bene3ts of search
Bene3ts of search refer to perceived bene3ts that are expected to result from the external search, including price reductions, obtaining the most desired model and satisfaction with the decision-making process External search e8ort is the degree of attention, perception and e8ort directed toward obtaining environmental data or information related to the speci3c purchase under consideration Perceived risk is de3ned as the probability of any loss that can occur Overall deal evaluation is de3ned as the perceived net gains associated with the products or services acquired Willingness to buy is de3ned as the likelihood that the buyer intends to purchase the product. All things being equal, buyers’ willingness to buy is positively linked to their overall evaluation of the deal
Moorthy et al. [18].
External search e8ort
Risk Deal evaluation Willingness to buy
separate variables that may of course, a8ect external search e8ort. While it is conceptually useful to di8erentiate ongoing search from pre-purchase search, the true concepts are diKcult to separate in practice. The problem lies with precisely specifying when a purchase problem has been recognized and when the decision process started. Here, no attempt is made to distinguish between the two since the border between the two processes are further obscured by the possibility of impulse purchasing [40]. 4.2. Perceived bene7ts of search Buyers generally are uncertain which website to purchase from because of variations in products o8ered online. To reduce this uncertainty, buyers must seek information. Willingness to search for information is contingent on buyers’ trading o8 perceived bene3ts (e.g. money saved) relative to costs of the search (e.g. time, money, e8ort spent in conducting the search) [41]. Bene3ts of search refer to perceived bene3ts that are expected to result from the external search, including price reductions, obtaining the most desired model and satisfaction with the decision-making process. The bene3ts of search are driven by how a consumer perceives the uncertainty in his choice environment (problem framing), the importance he gives to the product category (involvement) and his risk aversion [18]. If an individual believes that greater bene3ts will accrue from search, he will be more inclined to search because the perceived bene3ts will outweigh the perceived
Beatty and Smith [16].
Grewal et al. [31]; Rice [36]. Dodds et al. [28]; Zeithaml [37]. Della Bitta et al . [38]; Grewal et al. [39].
costs. It is proposed that perceived bene3ts are the trigger of external search behavior. H1 : Perceived bene7ts of search is positively associated with external search e4ort. 4.3. Perceived risk One factor that research has identi3ed as a critical determinant of consumers’ willingness to buy online is the perceived risk associated with the purchase. Individuals, both experts and non-experts di8er in their perceptions of risks depending on the nature of the online product. Risk is personal and related to consumer’s perception of what they consider to be risky [31,36]. Perceived risk is de3ned as the probability of any loss that can occur. A possible type of risk related to adoption of non-store retailers may be source credibility [42]. Internal search involves a scan of memory for knowledge or experience of past events [43] while external information search is an examination of sources outside the individual’s personal knowledge. This may include the media, retailers, and word-of-mouth communication with other consumers, or neutral sources. The consumer is thus faced with a problem of requiring information to reduce perceived risk of purchase. Information search for tangible-dominant products may include pre-purchase trial or observation of others when they purchase products online. Search may continue when products are unsatisfactory. Search may also continue with the aim of providing reassurance that a good purchase has
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been made particularly in ambiguous situations when the consumer is highly involved but lacks con3dence. The increased consumer uncertainty leads to a consequent need for greater reassurance [44] and the consumer is thus faced with a problem of requiring information to reduce perceived risk of purchase. It follows that: H2 : External search e4ort is positively associated with perceived risk. 4.4. Overall deal evaluation Overall deal evaluation is de3ned as the perceived net gains associated with the products or services acquired [28,37]. Hence, deal evaluation refers to the assessment of value of online purchase. In assessing the value of online purchase, various potential bene3ts could be examined such as perceived quality [25], product features [45] and desirability [46]. All of these bene3ts are part of the overall deal evaluation the consumer makes. Here, an evaluation of the alternatives found during the search is undertaken which takes into account our attitudes and beliefs. Perceived risks have been negatively related to overall deal evaluation by previous research on catalog and online shopping [47]. The work on perceived risk in marketing [48,31] suggests that a consumer forms perceptions regarding the intangible costs such as “psychic costs” in the form of anxiety, frustration, downtime as well as the performance and 3nancial risk associated with purchasing online. Risk represents an uncertain, probabilistic potential future 3nancial outlay. Here, it is therefore proposed that perceived risk be negatively related to consumers’ evaluation of the deal. H3 : Perceived risk is negatively associated with overall evaluation of the deal. Consumers exert varying degrees of energy to seek out and process information as they learn about available products. One of the key bene3ts of searching for more information is to reduce uncertainty and facilitate consumers’ overall evaluation of the deal. However, the level of information necessary to make an informed choice, will obviously depend upon the monetary size of the purchase. Other inLuences include the existence of customers’ previous experiences, and patterns of learned behavior involving ways in which attitudes have been formed and other social factors like reference group inLuence and personal contacts [36]. Sellers can increase consumers’ overall evaluation of the deal by enhancing consumers’ perceptions of the product’s quality or bene3ts relative to the selling price. It is proposed that the perceived bene3ts of search and the amount of external search e8ort are linked positively to their overall evaluation of the deal. H4 : The perceived bene7ts of search is positively associated with the consumers’ overall evaluation of the deal. H5 : The amount of external search e4ort is positively associated with the consumers’ overall evaluation of the deal.
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4.5. Willingness to buy Since online shopping is still in the early stage, the purchase decision component of the model is adapted to examine willingness to purchase instead of actual purchase. Furthermore, early preference patterns of innovators were shown to be predictive of actual decision of innovators [49]. Willingness to buy is de3ned as the likelihood that the buyer intends to purchase the product. All things being equal, buyers’ willingness to buy is positively linked to their overall evaluation of the deal [38,39]. Past acquisition value-based models (e.g. [28,37]) have de3ned overall deal evaluation as the perceived net gains associated with the products or services acquired. That is, the perceived acquisition value of the product will be positively inLuenced by the bene3ts buyers believe they are getting by acquiring and using the product and negatively inLuenced by the money given up to acquire the product i.e., the selling price. The perception of value in turn directly inLuences willingness to buy. It follows that H6 : Consumers’ overall evaluation of the deal is positively associated with their willingness to buy online. 5. Method 5.1. Sample and procedures The Internet is used as the data collection tool as our topic of interest, consumer decision-making in e-commerce is of interest to general Internet users. To stimulate response, 100 sets of S$2 (US$1.15) Singapore phone cards were given to respondents selected at random. Recipients of the phone cards were contacted by e-mails for their mailing addresses and the phone cards were posted accordingly. A preliminary survey was created using HyperText Markup Language. The survey pages were tested thoroughly to ensure that they appear properly with di8erent Web browsers. To ensure that respondents answer all questions, or at least those that are absolutely necessary, JavaScript programming was added to the electronic survey to verify and perform all necessary checking of a user’s input before the survey is submitted. For example, if the respondent omits answering certain questions, a dialog box would direct users to backtrack, correct the problem and resubmit the data. The 3rst round of pretesting was done on a working adult (male) and an undergraduate (female). Feedback was obtained regarding the wording of the questions asked and the layout of the survey. Modi3cations were made and a next round of pretest was conducted on six students and six working adults. There were no adverse comments and the survey was deemed ready for data collection after minor modi3cations based on feedback. The survey site was resided at a Web page within the Faculty of Business Administration server. Messages announcing the survey were
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posted in various local discussion newsgroups to reach out to Internet users. Repeated postings of the advertisement message were also subsequently made, from three times a week initially and gradually to once a week to encourage more responses. Other than newsgroup advertisements, personalized e-mails were also sent to Internet users. These e-mail addresses were randomly solicited from Internet Service Providers (Singnet, Paci3c Internet, Cyberway) and the National University of Singapore Web pages. In total, 3606 e-mails were sent to invite participation in the online survey. However, 148 e-mails were returned due to expired e-mail addresses or fault in the mail delivery system, resulting in e8ectively 3458 e-mails sent. 5.2. Instrument The measures for various stages of the consumer decision-making process (external information search, alternative evaluation and purchase) were adapted from existing literature. The measures comprise Likert-type statements, measured on seven-point scales ranging from (1)“strongly disagree” to (7)“strongly agree” (see Table 2). Where appropriate, the wordings of the items were adapted to the context of the Internet. 6. Results 6.1. Demographic pro7le of respondents A total of 1133 responses were collected. For the personalized e-mail category, a total of 3458 e-mails were sent but only 887 replied, thus yielding a survey response rate of 25.7%. The newgroups category comprises 246 responses. Before the analysis, a source bias test using the chi-square (2 ) statistic was performed to determine if there is a di8erence between respondents from e-mail and those from newsgroups. The results of the chi-square test on demographic pro3le of respondents indicate that there is no signi3cant source bias in the response sample (Table 3). The demographic pro3le in Table 3 indicates that respondents were predominantly males (64.5%) and single (90.5%). Females made up 35.5%, which was slightly higher than the 26% reported in www.research [51]. Ethnic Chinese made up the majority (93.0%) of the respondents. The respondents were also relatively young, with 89.8% of them in the age group of 15 –29 years and the majority in their early twenties. Most of the respondents are highly educated with 78.7% of them attaining at least a diploma or other higher quali3cations. Further, 67.8% of respondents are currently pursuing their education. The demographic pro3le is consistent with the study on e-commerce behavior of Singaporeans by Roy and See [52], the study on cyberbuying by Wee and Ramesh [53] as well as the www.research online survey [51] which reported that
the local Internet user community is predominately male, Chinese, tertiary educated and aged 36 years and below. Similarly, the demographic pro3le of our respondents is also in line with previous research on Internet adoption by Singapore Press Holdings’ (SPH) [54] and research by Teo, Lim and Lai [55] who reported that the typical Singapore Internet users are Chinese, male, young and highly educated. As the survey was carried out online, all respondents were Internet users. About 90% of respondents were Internet users for more than a year while 21% had bought online. 6.2. Structural equation modeling In this study, structural equation modeling (SEM) with AMOS is used to test and analyze hypotheses. In building structural equation models, the measurement model must 3rst be speci3ed. Test of the measurement model involves specifying which observed variables de3ne a construct and ascertaining the extent to which the indicator items are actually measuring the latent construct proposed in the research model [56]. 6.2.1. Validity and reliability analysis Construct validity refers to the extent to which the indicators “accurately” measure what they are supposed to measure. In this analysis, we examine the individual indicators’ standardized loading (i.e. standardized estimates) and test it for statistical signi3cance. From an analysis of the individual indicators, we conclude that the measure was valid as all the indicators were statistically signi3cant. Those indicators with R2 below 0.4 were dropped because of low loading and explanatory power [57]. In addition, we examined the correlations among various construct and noted that there is high correlation between overall deal evaluation and willingness to buy (r = 0:68). Subsequently, we carried out factor analyses to determine whether they are distinct constructs. The results show that d04 and d05 loaded on both factors. These two items were dropped and the correlation between overall deal evaluation and willingness to buy is reduced to 0.52. Table 4 presents the means, standard deviations and correlations among the model components. The results show that perceived risk is negatively correlated with bene3ts of search (r = −0:037; p ¡ 0:05), overall deal evaluation (r = −0:429; p ¡ 0:05) and willingness to buy (r = −0:350; p ¡ 0:05). For the SEM analysis, we use the composite reliability to assess the reliability of the construct indicators. Bagozzi and Yi [58] suggested the composite reliability should be greater than or equal to 0.60. The composite reliabilities for various constructs were found to be satisfactory as shown in Table 5. 6.2.2. Estimation and 7t criteria For SEM, it is a common practice to evaluate the model using a few goodness-of-3t measures to assess the model
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Table 2 List of construct indicators Construct
Source
Total amount of external search e4ort e01 9. I spent a lot of time sur3ng before I decide upon online purchase e02 10. I spent a lot of time sur3ng the Website for information about online products e03 11. I made a lot of visits to sites before the purchase of products online Perceived bene7ts of search b01 1. It pays to surf around before purchasing online b02 2. By searching for more information, I am certain of making the best buy b03 3. I learned which products are suitable for me by sur3ng around b04 4. Sur3ng around various sites helped me to 3nd the lowest price when I purchase online b05 5. I got exactly what I wanted by searching enough before I purchase online b06 6. There is too much to lose by being ignorant about products when I have to purchase online b07 7. By rushing into an online purchase, one is bound to miss a good deal Overall deal evaluation d01 1. Purchasing online is de3nitely worth the money d02 d03 d04 d05 d06 d07 d08 d09
2. Considering the price, products purchased online are of excellent quality for the price 3. If I buy online, I will be saving a signi3cant amount of money 4. I am con3dent that buying online is a good decision 5. My attitude about purchasing online is favorable 6. The prices of products online are very acceptable 7. Considering everything, I think purchasing online is an excellent deal 8. Purchasing online is desirable 9. The Web’s product features are very attractive
Perceived risk r01 1. Considering the amount I would have to pay for online purchase, purchasing online would be risky r02 2. I think that the purchase of product online would lead to 3nancial loss for me because of the possibility of such things as uncertainty in the quality of item purchased r03 3. Given the potential 3nancial expenses associated with purchasing online, the overall 3nancial risk associated with purchasing online is high r04 4. As far as I am concerned, this 3nancial loss would be important r05 5. I am not con3dent that the product purchased online will perform the functions as described r06 6. I have serious doubts that the product purchased online will work satisfactorily r07 7. I am not certain whether the product purchased online will perform the functions that were described in the Website Willingness to buy p01 1. If I were going to buy a product, the probability of buying the product online is p02 p03
2. The probability that I would consider buying online is 3. The likelihood that I would purchase online is
in terms of model 3t and model parsimony. Chi-Square 3t index is not used here because of our large sample size (n=1133), which increases the likelihood of rejection of the model as not being a good 3t with the data and the likelihood of a Type II error.
Srinivasan and Ratchford [17]
Srinivasan and Ratchford [17]
Urbany et al. [27]; Dodds et al. [28]; Lichtenstein et al. [50]; Wood and Scheer [29]
Srinivasan and Ratchford [17]; Grewal et al. [31]; Wood and Scheer [29]
Dodds, Monroe and Grewal [28]; Grewal, Monroe and Krishnan [39]
To assess the overall 3t of the hypothesized model, the research model is evaluated according to the goodness-of-3t index (GFI) and the adjusted goodness-of-3t index (AGFI). The comparative 3t index (CFI) and the root-meansquare error of approximation (RMSEA) which attempt to
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Table 3 Demographic pro3le of respondents Demographic pro3le
E-mail
Newsgroups
Gender Male Female
568 319
163 83
Marital status Single Married
805 82
Ethnic group Chinese Malay Indian Eurasian Others Age Under 15 15 –19 20 –24 25 –29 30 –34 35 –39 40 – 44 Missing (99) Education Primary ITE certi3cate Secondary/ GCE ‘O’ level Pre-university/ GCE ‘A’ level Polytechnic diploma University degree Postgraduate diploma Masters degree Employment category Full-time Part-time Self-employed Student Not in the labor force Unemployed National serviceman Missing (99)
Percent
Chi-square
731 402
64.5 35.5
df = 1 chi-sq = 0:416 p = 0:519
220 26
1025 108
90.5 9.5
df = 1 chi-sq = 0:392 p = 0:531
820 31 21 13 2
234 5 5 — 2
1054 36 26 13 4
93.0 3.2 2.3 1.1 0.4
df = 4 chi-sq = 7:027 p = 0:134
1 245 387 160 56 24 7 7
— 70 110 45 14 4 2 1
1 315 497 205 70 28 9 8
0.1 27.8 43.9 18.1 6.2 2.5 0.8 0.7
df = 6 chi-sq = 1:409 p = 0:965
8 5 28 147 294 309 32 64
2 1 9 41 79 91 10 13
10 6 37 188 373 400 42 77
0.9 0.5 3.3 16.6 32.9 35.3 3.7 6.8
df = 7 chi-sq = 1:737 p = 0:973
218 16 26 598 7 4 10 8
59 2 9 170 2 1 3 —
277 18 35 768 9 5 13 8
24.4 1.6 3.1 67.8 0.8 0.4 1.1 0.7
df = 6 chi-sq = 1:629 p = 0:950
minimize the impact of sample size, and shift the research focus from exact 3t to approximate 3t are used as well [59]. The GFI is based on a ratio of the sum of the squared di8erences between the observed and reproduced matrices to the observed variances, thus allowing for scale. To have a good model 3t therefore, GFI of close to 0.90 reLects a good 3t. In addition, the AGFI, which is adjusted for the degrees of freedom of a model relative to the number of variables
Total
should be over 0.80, the CFI over 0.9 and RMSEA less than 0.10 [56]. 6.2.3. Test of structural equation model The total sample (N = 1133) in this study was split into two sub-groups (odd and even respondents) for cross validation purposes [60]. The calibration sample consisted of the odd respondents (n1 = 567) and the even respondents (n2 = 566) served as the validation sample. The results for
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363 Table 4 Composite reliability analysis Constructs
Reliability
Items eliminated
E8ort ( 1 ) Risk ( 2 ) Deal ( 3 ) Purchase ( 4 ) Bene3ts ( 1 )
0.735 0.841 0.854 0.900 0.796
— — d04, d05, d06, d08, d09 — b06, b07
the calibration sample show that the model exhibit reasonable 3t (GFI = 0:862; AGFI = 0:823 and CFI = 0:914 and RMSEA = 0:088). To avoid post-hoc 3tting of mis-speci3ed models, the validation sample (n2 = 566) was used to cross validate the causal model before its acceptance. In this study, a “tight” cross validation approach was used where all the parameters obtained from the calibration sample were 3xed for the validation sample. The model yields high values for GFI (0.876), AGFI (0.844), CFI (0.926) and RMSEA (0.082) and provides favorable evidence in support of replication and generalizability. Fig. 3b depicts a graphical summary of the model based on the validation sample. Further, both Figs. 3a and b indicate that the model for both the calibration and validation samples were very similar to each other. This provides further empirical evidence supporting the generalizability of the model. 6.2.4. Structural model (based on total sample) Subsequently, the model was tested on the total sample (N = 1133) because a larger sample size enhances the accuracy and stability of coeKcient estimates [56]. A review of the overall measures of 3t reveals a consistent pattern of good support for the model (GFI = 0:879; AGFI = 0:846; CFI = 0:919 and RMSEA = 0:086). The diagrammatic summary of the model is presented in Fig. 4. Since the overall 3t of the model is acceptable, we now move on and focus on the speci3c elements of 3t. As a measure of the entire structural equation, an overall coeKcient of determination (R2 ) is calculated. Structural equation 3t of the endogenous constructs is desirable for the model. In terms of the individual constructs: • R2 of external search e4ort construct ( 1 = 0:372) shows that 37.2% of the variance in E4ort was accounted for by perceived bene3ts of search. • R2 of perceived risk construct ( 2 ) remains low at 0.003. Only 0.3% of the variance in perceived risk was accounted for by external search e8ort. The relatively low coeKcient of determination for risk suggests that there may be other variables that determine risk. For example, other variables that may determine risk might be the number of alternative channels from which products can be purchased and the perceived di8erences between these channels.
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• R2 of deal construct ( 3 ) indicates that 38.4% of the variance in the overall deal evaluation was accounted for by external search e8ort and perceived risk. • R2 of purchase ( 4 ) has a value of 0.343 which implies that 34.3% of the variance in willingness to purchase was accounted for by overall deal evaluation and perceived risk. 6.3. Hypotheses testing The standardized coeKcients for each path closely approximate the e8ect sizes usually shown by beta weights in regression. Thus, low coeKcients have limited substantive e8ect, whereas increases in values signify their increasing importance in the causal relationships [61]. In the statistical analysis, supportive 3ndings for H1 ( = 0:60; p ¡ 0:05) suggest an association of perceived bene3ts of search with external search e8ort. Hence, the greater the perceived bene3ts of search, the more likely will a user conduct more external search. In addition, external search e8ort is found to have no relationship with perceived risk (H2 ) ( = −0:05; p ¿ 0:05). Supportive 3ndings for H3 ( = −0:49; p ¡ 0:05) suggest that there is a negative relationship between consumers’ perceived risk and their overall evaluation of the deal. In addition, the relationship between perceived bene3ts of search and consumers’ overall evaluation of the deal (H4 ) ( = 0:60; p ¡ 0:05) was statistically signi3cant. However, the relationship between the amount of external search e8ort and consumers’ overall evaluation of the deal (H5 ) ( = −0:00; p ¿ 0:05) was statistically insigni3cant. For the 3nal hypothesized relationship between consumers’ overall evaluation of the deal and their willingness to purchase online (H6 ), signi3cant support was also found for the hypothesized relationship ( = 0:81; p ¡ 0:05). 7. Discussion 7.1. External search e4ort It was hypothesized in our research model that the total amount of external search e8ort is inLuenced by perceived bene3ts of search (H1 ). Results from SEM showed a significant positive relationship between the two variables. This result is consistent with Srinivasan and Ratchford [17] who found a strong positive relationship between bene3ts and the amount of external search. The vast information on the Web equips people with the latest and best o8ers. Store navigation features include product search functions, site maps, product indices and the overall site design and organization. Potentially, the Web makes the information search portion of the decision-making process much easier. In contrast to the time and e8ort required to go to many physically located stores or phone around, visiting multiple sites on the Web requires minimal e8ort.
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Table 5 Means, standard deviations (SD) and correlations Constructs
Mean
SD
1
2
3
4
1. 2. 3. 4. 5.
5.201 4.970 4.525 3.824 3.706
1.435 1.670 1.471 1.210 1.503
0.610∗ −0:037∗ 0.363∗ 0.077∗
−0:061 0.193∗ 0.127∗
−0:429∗ −0:350∗
0.521∗
Bene3ts E8ort Risk Deal Purchase ∗ Correlation
is signi3cant at p ¡ 0:05.
Information Search
Purchase
Alternative Evaluation
.35* .53* Benefits
-.01
.83* Deal
Effort -.06
-.47* Risk
(a)
Purchase GFI = 0.862 AGFI = 0.823 CFI = 0.914 RMSEA = 0.088 n1= 567 chi-square = 1075.1 df = 198
Alternative Evaluation
Information Search
Purchase
.31* .68* -.00 Benefits
Effort
Deal
-.51*
-.03 Risk
(b)
.78* Purchase GFI = 0.876 AGFI = 0.844 CFI = 0.926 RMSEA = 0.082 n2 = 566 chi-square = 945.8
df = 198
Fig. 3. (a) Structural model (based on calibration sample); (b) structural model (based on validation sample). Note—Bene7ts = perceived bene3ts of search; Risk = perceived risk; Deal = overall deal evaluation; E4ort = external search e8ort; Purchase = willingness to buy ∗ p ¡ 0:05.
Directories and search engines allow users to browse through comprehensive lists of vendors arranged by product and service, or to search for a vendor by name or page content all from the convenience of a home computer [62].
When the online retail website made quality information easier to search and compare, consumers may become less price sensitive and purchase higher quality and more expensive products. Clearly, di8erent ways of presenting
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
359 Purchase
Alternative Evaluation
Information Search .60*
eb01 eb02 eb03 eb04 eb05
ed01
ed02
ed03
ed07
d01
d02
d03
d07
b01 b02 b03
Benefits
ee04
ee01
ee02
ee03
e01
e02
e03
ed10
b04 b05
-.00
.60*
Deal .81*
Effort -49*
.05
Purchase
Risk
r01
r02
er01
er02
r03
-.49*
er03
r04
r05
r06
r07
er04
er05
er06
er07
p01
p02
p03
ep01
ep02
ep03
ep04
er08
GFI = 0.879 AGFI = 0.846 CFI = 0.919 RMSEA = 0.086 N = 1133 Chi-square = 1852.7 Df =198
Fig. 4. Structural model (based on total sample). Note—Bene3ts: perceived bene3ts of search; E8ort: external search e8ort; Deal: overall deal evaluation; Risk: perceived risk; Purchase: willingness to buy ∗ p ¡ 0:05.
information online alters the information search costs. For example, if the intent is to provide customers with the ability to search out detailed technical information, then a website with deep structured information and search facilities must be designed. Also, general “help” functions can assist users in error recovery or include information about the store’s navigation on the use of ordering features like a shopping cart function. The basic idea is that decision-makers trade o8 some accuracy in representing their preferences for a saving in a cognitive e8ort. More generally, the observed importance of attributes seems to be a8ected by the cost of processing those attributes. Researchers had shown that generally consumers are not more price sensitive when shopping online. In fact, consumers conduct less price comparison online when the store provides information in relevant non-price attributes and when quality information is made easier to process [63]. Consumers may also learn how to search e8ectively through experience and hence, there is a negative relationship between amount of experience and search e8ort [17]. This experience increases the likelihood that a consumer will perceive lower bene3ts of search and consequently to search less. Therefore, those who had the least need conducted the least amount of search. 7.2. Perceived risk In our hypothesized model, the total amount of external search e8ort is positively associated with perceived risk (H2 ). This relationship was not supported. One possible
explanation is that not all websites are equally credible. Consumers infer information about quantity, quality and a variety of products from the brand names or reputation of the store. A website’s credibility is determined by the source’s trustworthiness. Attribution theory [64] suggests that consumers who are exposed to a website often attempt to assess whether the website provides an accurate representation or whether the website lacks credibility. When source credibility is low, attribution theory suggests that consumers will discount the information in the website. As a result, the product attribute claims made by a low credibility website source are perceived as less useful in judging risk. Price however, is a particularly unambiguous cue that may be useful for making judgements about a product’s performance or quality when other cues are unavailable or unreliable [28]. The price cue is expected to have a greater e8ect on perceived risk when source credibility is low. In this case, consumers are more likely to use attribution information to judge perceived risk. Another possible explanation is that online consumers may be more likely than their non-wired counterparts to spend more to get the best and to make an e8ort to use new devices and methods [65]. Therefore, they may value the time saving provided by the Web rather than its cost savings. Online shopping saves time and hassle. Hence, they may view convenience, rather than cost savings as a key bene3t o8ered by good online stores [66] which is something not captured in our construct measurements. This may account for the insigni3cant relationship between total amount of external search e8ort and perceived risk.
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7.3. Overall deal evaluation The negative relationship between perceived risk and consumers’ overall evaluation of the deal (H3 ) is supported. This is consistent with previous research by Wood and Scheer [29] which showed that perceived risk has a powerful e8ect on the overall evaluation of the deal. This study o8ers insights into the importance of communicating information to consumer that allays their concerns about performance risk and 3nancial risk such as information about product quality, warranties and money-back guarantees. Customers are likely to try the online product when they come across a purchase trigger like coupon or free trial. Trial is an important part of most evaluation processes and reduces the consumer’s risk [62]. For instance, bookstores can o8er samples of speci3c o8erings, music stores can provide audio clips of their discs and software companies can let users download versions of their software for a limited trial period. The responsiveness of sta8 to the customer service link is critical. Types of service include sales clerk service for merchandise selection, answers to frequently asked question (FAQ) and credit, return and payment policies. Spiller and Lohse [67] surveyed 137 Internet retail stores and found that most stores provided a pittance of service information. Almost one-third did not provide any information on the company’s history policies or background and 80% had less than 10 lines of information. This is a surprising number since customers want to know who they are dealing with when making online purchase. This is especially important for new “virtual companies” solely operating on the Internet. The positive relationship between the perceived bene3ts of search and consumers’ overall deal evaluation (H4 ) was supported. This result is expected as one of the key bene3ts of search is to facilitate deal evaluation through providing more information. However, the relationship between the amount of external search e8ort and consumers’ overall evaluation of the deal (H5 ) was not supported. One possible explanation for this 3nding is that consumers’ search for information can be divided into two general categories: internal search and external search [68]. Information search initially covers internally stored information and experience. Personal selling will inLuence their choice as well. This level of search is quick and largely unconscious and if there are fairly strong beliefs and attitudes, automatic or routine problem solving follows. Where enough information is available in memory to make a decision, then an internal search is all that is required. If such information is scarce, external search for information is then undertaken [36]. Perhaps, the information environment for e-commerce is so rich that even unknowledgeable consumers can acquire enough relevant information, to feel they can make a purchase decision without seeking large amounts of externally obtained information from the Web prior to beginning the buying decision process. Consumers are able to acquire relevant product and purchase knowledge without having detailed website information. This might well explain the
relatively weak relationship between total amount of external search e8ort and consumers’ overall evaluation of the deal. Another possible explanation is that users invest their own time and energy in the interaction with a store’s digital marketing application, therefore creating an important disincentive for them to repeat that investment with another application. For instance, they might have spent time revealing their preferences in music or movies and the store is now able to o8er them suggestions on other things they might enjoy based on the information gained in previous purchases. This may account for the minimal amount of external search e8ort among shoppers. 7.4. Willingness to buy The results indicate a signi3cant positive relationship between consumers’ overall evaluation of the deal and their willingness to purchase (H6 is supported). This is similar to past research by Wood and Scheer [29], which found that purchase is apparently driven by the overall cost or bene3t tradeo8 represented by overall deal evaluation. In addition, Dodds et al. [28] also found a signi3cant positive relationship between overall evaluation of the deal and willingness to buy. It must be noted that consumers balance the bene3ts of purchase against the costs. Our research supports the idea that overall evaluation of the deal has a positive relationship with consumers’ willingness to buy online. Bene3ts can be functional, operational in terms of durability and reliability or personal. Costs include both 3nancial and non-3nancial aspects such as time and e8ort [37]. Today’s information technology enables consumers to compare bene3ts and prices with unprecedented ease and accuracy. Managers must understand the variables a8ecting consumers’ evaluation of the deal. Some consumers are sensitive towards price, whereas others are more bene3t oriented. Hence, the consumers’ overall evaluation of the deal will vary across segments. Managers should therefore determine which value strategy is appropriate for their target segments and develop their positioning strategies appropriately.
8. Limitations Although this study provides several interesting insights into consumers’ decision process in the Singapore context, there are however, some limitations that restrict the extent to which 3ndings can be generalized. First, the Internet and online shopping patterns used in this study were largely self-reported as opposed to objectively measured. Self-reported data are subject to the fallibility of people’s memories or even deliberate alteration through social desirability biases [66].
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Second, as the data was collected using Internet survey, the problems associated with the medium would also a8ect the quality of our data collected. Although precautions had been made to minimize these problems, such as using JavaScript programming to ensure necessary data are submitted, there might still be other inherent problems such as self-selection that could a8ect the quality and representativeness of our data collected. Further, as the online population tends to comprise people with certain characteristics (e.g., age, Internet experience), generalizability to the general population may be limited. In addition, although responses were screened for duplicated responses from the same email address, it is possible that some respondents might use another email address to answer the survey. However, given the length of the survey, this appears unlikely and is not a serious limitation. Third, the measures used in this study were obtained at one point in time. Hence, one has to be cautious about interpreting our results as indicative of casual relationships. Strictly speaking, they indicate only correlational relationships between the various constructs. Fourth, as the sample was collected in Singapore, generalizability to other countries might be limited due to di8erences in buying behavior across di8erent cultures. However, as the results appear consistent with previous research done in other countries, this limitation is not serious. Fifth, we only examined a sub-set of variables a8ecting consumer’s decision process to buy online. Future research can include other variables such as nature of products (physical or digital), delivery mechanisms, prices, brand name and speci3c attributes of the web sites through which selling is done. 9. Implications Market researchers have been trying for decades to understand consumer behavior. Our study provides further evidence on the consumer decision model for the understanding of consumer behavior in the digital marketplace. The results con3rm the argument by O’Keefee and McEachern [69], who proposed a framework called consumer decision support systems (CDSS) that the consumer decision process is applicable in the context of the virtual shopping environment. According to their framework, both CDSS facilities and Generic Internet and Web facilities can support each of the stages of the consumer decision process model. Our study has important implications for the understanding of the consumer decision process in e-commerce. In this research, attempt is made to develop and empirically test the consumer decision process model with speci3c attention to the applicability of online consumer behavior. The results provide more substantive understanding of the factors a8ecting consumers’ willingness to purchase online. Given the relatively good overall goodness of 3t of the model, we believe that this study could be a valuable addition to re-
361
searchers in their e8orts to understand consumer behavior in the digital marketplace. However, further development of the consumer decision process model could be done so as to improve the ability of the model in predicting online sales. In particular, it is noted here that the R2 for the perceived risk structural equation is relatively weak. At 0.3%, the ability of the model in explaining risk is on the low end. This could be because external search e8ort, which is hypothesized to a8ect perceived risk only explained 3% of the variance. Hence, this may indicate that there could be other factors a8ecting perceived risk, which were not examined here. It is also possible that the performance risk and 3nancial risk constructs impact this framework at di8erent points. For example, perceived performance risk may be a determinant of product desirability as well as potential costs, while perceived 3nancial risk may play a more important role in whether or not positive evaluation of the deal is translated into willingness to purchase. Future research could perhaps separate perceived risk into these two di8erent constructs. In addition, the results indicate positive relationships between perceived bene3ts of search and consumers’ overall deal evaluation. This suggests that perceived bene3ts of search could have a signi3cant inLuence on consumers’ overall deal evaluation and subsequently on their willingness to purchase online. Further, it was found from our results that external search e8ort is not statistically signi3cant with consumers’ overall deal evaluation. This may signify that other factors, such as internal search may inLuence consumers’ overall deal evaluation. Thus, this 3nding could be a new research topic area for researchers to explore. Further examinations of search cost models can be conducted to determine the distribution of prior beliefs among consumers and their e8ect on the total amount of external search. 10. Future research directions There are several avenues where future research can be conducted. First, the study involved asking general Internet users about their perceptions of purchasing online. With the rapid growth of e-commerce, future research can replicate our study solely on e-commerce users, measuring actual purchase behavior instead of intentions. This is to determine if there are any signi3cant di8erences in the core consumer decision process of general Internet users versus e-commerce users. Second, the investigation of the consumer decision process model can also be extended to cover the same issue from the point of view of business consumers, in their procurement of goods so as to have a more holistic perspective on this phenomenon. Third, a comprehensive comparative analysis can be carried out between the di8erent consumer behavior models in the investigation of consumer behavior
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in the digital marketplace. This will increase our understanding of the relative strengths and weaknesses of applying the various consumer behavior models in the context of the virtual shopping environment. Fourth, detailed studies may be conducted to reveal key push and pull factors surrounding the use of the Internet for search activities. For example, research can investigate the e8ect of di8erent website attributes on consumer’s search e8ort and their ultimate decision to purchase online. Fifth, future research may also use di8erent methodologies such as longitudinal studies, focus groups and interviews to examine consumer decision processes in online buying. Such studies would enhance the richness of the 3ndings. Sixth, the study can be replicated in a di8erent culture for cross-cultural comparisons. As Singapore has a strong pro-IT culture, it would be interesting to compare the results with studies in other cultures where IT culture is less dominant or developed. Last but not least, e-commerce is still currently in its infancy stage here. It would be interesting to replicate this study in the near future, where e-commerce would have possibly become part of the lifestyle of local residents and compare whether there are any signi3cant di8erences in the results obtained.
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