Int. J. Networking and Virtual Organisations, Vol. 5, Nos.

3/4, 2008

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A multitheoretical approach for solving trust problems in B2C e-commerce Siddhi Pittayachawan*, Mohini Singh and Brian Corbitt
School of Business Information Technology RMIT University G.P.O. Box 2476V, Melbourne 3001 Victoria, Australia E-mail: siddhi.pittayachawan@rmit.edu.au E-mail: mohini.singh@rmit.edu.au E-mail: brian.corbitt@rmit.edu.au *Corresponding author
Abstract: Trust has been identified as a major barrier in online shopping, especially in the B2C e-commerce model. It has been studied for several years, but there is no indicator that a satisfactory solution for trust in online shopping has been achieved. A trust model is proposed in this paper in order to address this issue. It is uniquely based on five current issues (cybercrime, security, control, web interface, and a trusted third party) that impact trust, guided by four supportive theories: Semiotics, Trust in Signs, Simmelian Model of Trust and Trustworthiness. The research was accomplished with an online survey to collect data from online shoppers around the world. Structural Equation Modelling (SEM) was used to validate the trust model revealing that privacy and security of information are most important factors affecting trust in B2C e-commerce followed by web interface and control. Keywords: B2C trust model; e-commerce trust issues; online shopping solution for e-vendors. Reference to this paper should be made as follows: Pittayachawan, S., Singh, M. and Corbitt, B. (2008) A multitheoretical approach for solving trust problems in B2C e-commerce, Int. J. Networking and Virtual Organisations, Vol. 5, Nos. 3/4, pp.369395. Biographical notes: Siddhi Pittayachawan is a lecturer in the School of Business Information Technology at RMIT University. He received his PhD degree addressing trust issues in B2C e-commerce. His research interests are online shopping, trust, security, and other consumer issues. Mohini Singh is a Professor of Information Technology and E-business in the School of Business Information Technology at RMIT University. She earned her PhD from Monash University, and has published widely in the areas of e-business and new technology and innovation management. Her publications comprise books, book chapters, journal and conference papers. She is the principal Editor of two highly regarded books on e-business and serves as a member on the editorial boards of several international journals.

Copyright 2008 Inderscience Enterprises Ltd.

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Professor Brian Corbitt is currently Professor of Management Information Systems and Head of the School of Business Information Technology at RMIT University, Australia. He has previously been Professor of Management Science at Shinawatra University in Thailand, Pro-Vice Chancellor (online services) at Deakin University, and JADE Professor of e-Commerce at Victoria University of Wellington in New Zealand. He specialises in IT policy development, in health information systems, analysis and implementation in business modelling and electronic commerce trade relationships, and knowledge management. He has published ten books including six on e-business, e-commerce and e-government and over 150 refereed scholarly papers.

Introduction

Trust is an important barrier discouraging consumers to transact online, especially in Business-to-Consumer (B2C) model, although e-commerce has expanded and grown in the last five years. The issue of trust in e-commerce is widely addressed, however, a satisfactory solution is yet to be achieved. On the contrary, it is reported that trust in online shopping is not improving, in fact, becoming a bigger problem for electronic vendors (e-vendors), leading to a decline in online shopping (NECTEC, 2006). The aim of this paper is to provide e-vendors a simple trust model to address trust problems in B2C e-commerce. Trust is known to be very complex and multidimensional (McKnight and Chervany, 2001), and therefore, it entails several variables which e-vendors should be aware of. This paper includes a discussion on current trends in online business on the issues of trust and a critical review of literature on trust models from previous research. From an extensive review of literature on trust issues (e.g., cybercrime, security and web interface), 21 propositions were developed to establish consumer issues in online trust. An online survey establishing consumer perception guided by Semiotics (Chandler, 2002), Trust in Signs (Bacharach and Gambetta, 2001), Simmelian Model of Trust (Mllering, 2001), and Trustworthiness (Hardin, 1996) theories was undertaken to develop a trust model for e-vendors. Data analysis based on SEM identifying privacy of information and secure transactions to be the most important components of trust form the gist of this paper.

B2C e-commerce

B2C e-commerce has become a popular method for sale of goods and services to consumers in the last ten years. It enables vendors to access customers globally (Miniwatts, 2006), to generate higher income (comScore, 2006), to gain a competitive advantage (Lederer et al., 2000), to enhance and integrate business processes (Piris et al., 2004), and to improve customer service (Lovett, 2003). McGann (2004) advocates that the benefits of B2C e-commerce to consumers include faster transaction (78%), cheaper prices (51%), more selection of similar products or services (43%), simpler shipping process (40%), more customisable products (28%), and more product information (20%). Due to these benefits, B2C e-commerce is a trend widely adopted and is constantly growing since 1995 (Netcraft, 2007). Despite its rapid growth and popularity, trust is a

A multitheoretical approach for solving trust problems in B2C e-commerce 371 critical problem discouraging the use of B2C e-commerce (CMO Council, 2006; Grazioli and Jarvenpaa, 2000; Hsiung et al., 2001; NECTEC, 2006; Tsiakis et al., 2004; Wang and Emurian, 2005).

2.1 The importance of trust in B2C e-commerce

According to McKnight and Chervany (2001), trust has several meanings including competence, predictability, benevolence and integrity, each meaning different things in different situations. In B2C e-commerce, competence may be used in a trust relationship where the ability of an e-vendor is key to foster consumer trust while benevolence may be applied in a trust relationship where care and responsiveness are important to consumers. This kind of trust relationship is three-part trust relation (Hardin, 2002), which is defined as A trusts B to do C. Trust has been identified to be an important factor affecting online consumers decisions to commit a purchase (McKnight and Chervany, 2001). An important function of trust in e-commerce is to help consumers confidently shop online even if e-vendors are not known or recognised (Akhter et al., 2004; Jutla et al., 2004). Winning consumer trust is especially important for new e-vendors who are using the internet as a platform for business. Although trust has been identified to be a barrier to e-commerce, it is also a success factor for e-vendors if satisfactorily implemented (Stockdale and Standing, 2003). Trust is also a central factor in e-transaction (Daughtrey, 2001), business collaborations (Bryant and Colledge, 2002), security systems (Ratnasingam and Pavlou, 2003) and organisations (Antoci et al., 2006). It supports B2C e-commerce as it simplifies transaction process (Riegelsberger et al., 2003) as well as maintains long-term relationships between e-vendors and customers (Keen, 1997), improves customer loyalty (Thatcher and George, 2004), improves price tolerance (Delgado-Ballester and Munuera-Alemn, 2001), and gains a competitive advantage (Wernerfelt, 1991). Trust is important to every e-vendor because it is a key factor for increased sales and improved market share in online shopping (McKnight and Chervany, 2001; Nielsen et al., 2001).

2.2 Barriers to online trust

There are several new developments that affect trust which impacts e-commerce. These include cybercrime, security, control, web interface, and third party intervention. Each of these issues is explained separately in the following section.

2.2.1 Cybercrime
Cybercrime is an activity that utilises computing and communication technologies as tools to carry out a crime (Smith, 2004). Identity theft and fraud are two main types of cybercrime that affect consumer decisions to shop online (Hoffman et al., 1999a; Taylor Nelson Sofres & TRUSTe, 2005). Identity theft (i.e., stealing information) leads to fraud (Foley et al., 2004), and this causes fear among consumers (Farrell et al., 2000), and discourage them from shopping online (Entrust, 2005). Cybercrime is perceived risk affecting consumer behaviour (Gefen et al., 2003a) on the internet. Since cybercrime affects trust (Farrell et al., 2000), it generates a negative impact on consumer trust and shopping behaviour in e-commerce (Krebsbach, 2006).

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2.2.2 Security
Security refers to data protection in e-transactions, and is recognised to be a fundamental component in e-commerce (Aldridge et al., 1997; IDC, 2003; Turban et al., 2006). It scrambles text (e.g., e-transactions or financial details) into something unreadable (i.e., an encrypted message) by intruders (e.g., opportunists and criminals) in order to establish secure information exchange. Since scrambled contents generated by security are extremely difficult to decipher, it can deter cybercrime activities (ITU Telecommunication Standardization Sector, 1997; Turban et al., 2006), and thus, foster consumer trust (Farrell et al., 2003; Kini and Choobineh, 1998). McKnight and Chervany (2001) clarify that security makes consumers perceive assurance, helping them to complete transactions without risks. In spite of this, the internet does not naturally support security in online shopping, and subsequently, e-vendors must recognise the importance of security in B2C e-commerce and properly deploy it in their online businesses (Bhimani, 1996). Specifically, four critical aspects of security must be met including authentication (i.e., identities of transacting parties are genuine), confidentiality (i.e., e-transactions are secret to outsiders), integrity (i.e., e-transactions are not compromised) and non-repudiation (i.e., e-transactions are undeniable by relevant parties) in order to establish secure e-transactions (ITU Telecommunication Standardization Sector, 1997). This clearly requires technical knowledge, and thus, it can be notably challenging for e-vendors to correctly utilise security in online shopping. Implementing it wrongly can result in security breaches, and consequently, destroy consumer trust in e-vendors and B2C e-commerce (BBC, 2007; CMO Council, 2006). Several researchers (Chan and Lee, 2003; Gefen et al., 2003b; Lee and Turban, 2001) are of the opinion that security improves trust and intention to shop online. It also helps consumer to trust e-vendors (McKnight and Chervany, 2005). Although Ratnasingam and Phan (2003) state that security is a success factor in e-commerce, Katsikas et al. (2005) argue that security issues need to be further explored in e-commerce.

2.2.3 Control
Control in this paper implies consumers abilities to control, influence, or monitor the online environment and information (Hoffman et al., 1999b; Robles et al., 2001). If control exists, then consumers tend to commit to transactions even if trust is not adequate (Tan and Thoen, 2002). Since a lack of trust and the rise of cybercrime cause consumers to fear e-commerce and to raise concern for the safety of their information, control is recognised as another factor contributing to trust (Yoon, 2002). A lack of control in the online environment create disputes between consumers and e-vendors (Olivero and Lunt, 2004). This is due to the fact that the nature of e-commerce is such that it does not automatically give power to consumers (Yoon, 2002). Therefore, e-vendors must understand what kinds of control should be given to the customers to win their confidence in this environment. Not allowing a consumer to unsubscribe from a mailing list, an e-mail sent to them without consent, inability to change information and inability to close an account (TRUSTe, 2006) are examples of control issues in B2C e-commerce. Silverpop (2006b) suggests that consumers should be able to control content and decide how often they want to receive e-mails about products. Continuously sending e-mails without allowing consumers to unsubscribe from a mailing list generates a

A multitheoretical approach for solving trust problems in B2C e-commerce 373 negative impact on brand reputation and trust (Lee and Turban, 2001; Silverpop, 2006a). Furthermore, spam e-mails are on the rise and more difficult to be detected by anti-spam software, which frequently carry malware, searching and capturing shopping information for cybercriminals (Anti-Phishing Working Group, 2007; Sophos, 2006). This further discourages consumers from shopping online (Taylor Nelson Sofres & TRUSTe, 2005). Hence, it is clear that cybercrime raises the need for more control over e-commerce (Gauzente, 2004).

2.2.4 Web interface

Web interface plays a significant role in e-commerce as it is the first link between a customer and an e-vendor (Fogg et al., 2001). If web interface does not win consumers to believe that e-vendors are trustworthy, then a transaction will not take place (Ang and Lee, 2000). This view is supported by Kini and Choobineh (1998) who recognise that display modes, information of e-vendors and visual effects on the screen are important components of trust. McKnight and Chervany (2001) advocate that site quality, a privacy statement, a trusted seal and links to other websites also support trust and intention to shop online; and Wang and Emurain (2005), are of the opinion that the four dimensions of web interface which enhance trust are graphics, structure, content and social cues. A security and privacy statement, which is categorised as a part of content dimension by Wang and Emurain (2005) is also important in e-commerce. Consumers are concerned about how e-vendors collect and use their information. It is reported that 68% of consumers want e-vendors to disclose where and how their information is being processed (Krebsbach, 2006), and 95% of consumers refuse to provide information if there is no privacy statement (Gauzente, 2004; Hoffman et al., 1999a). It is therefore clear that a security and privacy statement on the website is important information for consumers affecting their decision to shop online. In addition, a security and privacy statement should be written in a way that is easily understood by consumers (Rivest and Lampson, 1996). Therefore, web interface is an important factor affecting shopping intention, trust, security and control.

2.2.5 Third party intervention

A trusted seal is a part of content dimension described by Wang and Emurian (2005). It is a piece of graphics displayed on the front page of shopping websites. Its function is to tell consumers that this e-vendor has been verified, and therefore, it is safe to purchase goods or services from this particular website. A trusted seal has an impact on consumer trust and online shopping behaviour (McKnight and Chervany, 2001). However, a trusted seal is merely a piece of graphics, and it does not contain any trustworthy value. In fact, when consumers trust a seal that is displayed on websites, they trust an identity of the seal issuer and not the seal itself (Sztompka, 2000). An issuer of a trusted seal is commonly known as a Trusted Third Party (TTP). In e-commerce, examples of TTPs are Verisign,1 GeoTrust,2 and TRUSTe.3 Hu et al. (2004) suggest that a trusted seal has a positive impact on consumer trust, and encourages them to purchase products from unknown websites confirming that the e-vendor is genuine. Furthermore, a well-known TTP has been identified to have a greater impact on trust (Peszynski and Thanasankit, 2002).

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To further understand regarding trust factors in B2C e-commerce, a review of trust models addressing one or more aspects of B2C e-commerce are presented in the following section.

2.3 Previous research on trust models

An analysis of models explaining B2C e-commerce trust is listed in Table 1 from the years 2006 to 2000. Variables addressing trust, impact on B2C e-commerce and limitations of these models are listed in columns 24 of Table 1. Previous studies included in Table 1 addressed some important aspects of trust in B2C e-commerce; however, none of them completely recognised all trust issues (i.e., cybercrime, security, control, web interface and TTP) discussed in this paper. They also placed very little emphasis on e-vendor issues. Without a proper solution to improve trust, the viability of e-commerce is questionable.
Table 1 Study Cheung and Lee (2006) Summary of previous research Variables Legal framework Perceived competence Perceived integrity Perceived privacy control Perceived security control Propensity to trust Third party recognition Kim and Kim (2005) Consumer trust Disposition to trust General self-efficacy Online transaction self-efficacy Perceived risk Pavlou and Gefen (2005) Buyers past experience Perceived effectiveness of institutional structures Perceived risk from community of sellers Price premiums Psychological contract violation Sellers past performance Transaction intentions Trust in community of sellers Koufaris and Hampton-Sosa (2004) Perception about the company Perception about the website Initial trust in company Addressed too few issues Transaction behaviour Too heavily based on psychological issues Purchase intention Too heavily based on consumer skills Impact on B2C e-commerce Consumer trust in internet shopping Limitation(s) Addressed consumer issues, but failed to include web interface and cybercrime

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Table 1 Study Thatcher and George (2004) Summary of previous research (continued) Variables Effect Commitment Satisfaction Social involvement Switching costs Trust in internet Cheung and Liao (2003) Low consumer awareness Negative shopper characteristics Perceived privacy concerns Perceived transaction insecurity Poor perceived e-vendor characteristics Corbitt et al. (2003) Participation in e-commerce Perceived market orientation Perceived risk Perceived site quality Perceived technical trustworthiness Users web experience Corritore et al. (2003) Gefen et al. (2003b) Credibility Ease of use Risk Calculative-based beliefs Perceptions of situation normality Perceptions of structural assurances Familiarity Trust Perceived ease-of-use Perceived usefulness Ratnasingham and Pavlou (2003) Salam et al. (2003) Technology trust Perceived benefits E-commerce performance Economic incentive Institutional trust Consumer-perceived risk of transactions over the internet Technology centric and ignored e-vendors Failed to address trust in e-vendors Intended use of a B2C website Trust Addressed only a narrow perspectives of trust Based on TAM which is technologically oriented. Online trust is not only about technology but also e-vendors Perceived trust Only focused on consumer perception Intentions not to shop over internet Addressed a negative consequence and not useful for e-vendors Impact on B2C e-commerce Web consumer loyalty Limitation(s) Addressed only long-term trust relationship

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Table 1 Study

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Summary of previous research (continued) Variables Perceived business tie Perceived interaction Perceived similarity Trusting beliefs Impact on B2C e-commerce Intention to buy Limitation(s) Addressed trust transitivity without clarification

Stewart (2003)

Suh and Han (2003)

Attitude toward using Behavioural intention to use Perceived strength of control Trust

Actual use

Too heavily based on security, however, trust also involves non-technical variables which this study left out Partially addressed web interface and ignored cybercrime, control and TTP

Belanger et al. (2002)

Pleasure Privacy Security Trustworthiness Web features

Purchase intentions Willingness to give private information

Bhattacherjee (2002) Gefen (2002)

Familiarity Trust Cost to switch vendor Customer trust Perceived risk with vendor Service quality

Willingness to transact Customer loyalty

Not always applicable to B2C e-commerce Too heavily based on service quality and less emphasis on trust

Kimery and McCord (2002)

Attention to assurance seals Attitude toward e-retailer Disposition to trust Perceived risk Trust in e-retailer

Intention to purchase

Too focused on a trusted seal and failed to address trust issues in relation to it

Sultan et al. (2002)

Characteristics of consumer Characteristics of website Perceptions of website trust Navigation functionality Personal variables Transaction security Website awareness Website properties Website satisfaction Website trust

Consumer behavioural intent

Only focused on web interface

Yoon (2002)

On/offline purchase intention

Only focused on security and web interface and failed to address cybercrime, control and TTP

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Table 1 Study Lee and Turban (2001) Summary of previous research (continued) Variables Trustworthiness of internet merchant Trustworthiness of internet shopping medium Contextual factors Other factors McKnight and Chervany (2001) Disposition to trust Institution-based trust Trusting beliefs Trusting intention Web vendor interventions Chau et al. (2000) Attitude towards online purchasing Perceived website ease-of-use Perceived website usefulness Grazioli and Jarvenpaa (2000) Assurance mechanisms Attitude toward shopping at the store Deception Risk Trust Trust mechanisms Jarvenpaa et al. (2000) Attitude Perceived reputation Perceived size Risk perception Trust in store Willingness to buy Only addressed trust in websites and neglected trust in e-vendors Actual purchase and willingness to buy Partially addressed web interface but failed to address security, control and TTP Online purchase intention Too heavily based on consumer attitudes Trust-related internet behaviour Failed to address cybercrime Impact on B2C e-commerce Consumer trust in internet shopping Limitation(s) Too heavily based on consumer perception of trust

2.4 Supportive theories

Four theories, Semiotics (Chandler, 2002), Trust in Signs (Bacharach and Gambetta, 2001), Simmelian Model of Trust (Mllering, 2001) and Trustworthiness (Hardin, 1996) are used to guide the development of research model proposed in this paper. Each of these theories is briefly discussed in the following section. Semiotics (Chandler, 2002) or semiology is the study of signs. It describes how one (i.e., interpretant) perceives something (i.e., representamen), and then refers to it as something else (i.e., object) based on ones belief, as shown in Figure 1. Although its focus is not directly about trust but perception, it is heavily based on belief, which is a very important component of trust. Therefore, it is adopted to explain the perception process and how the perception links to what it is perceived and what it is meant

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to a person that perceives. For example, Alice (i.e., interpretant) perceives a lock sign (i.e., representamen) on her web browser while shopping online, and then refers to it as security (i.e., object).
Figure 1 A semiotic triangle

Representamen (Lock sign)

Object (Security)

Interpretant (Alice)

Sources: Adapted from Chandler (2002, p.32) and Echtner (1999, p.48)

Trust in Signs (Bacharach and Gambetta, 2001) is a theory that explains how one trusts others based on a hidden property (i.e., krypta), which is translated from the targets appearance (i.e., manifesta), as shown in Figure 2. For example, during shopping online, Alice knows that there is encryption (i.e., krypta) supporting e-commerce websites because she sees a lock sign (i.e., manifesta) on her web browser.
Figure 2 Trust in signs (see online version for colours)
Trustee (person or thing)

(Lock sign) Manifesta (observable property)

(Encryption) Krypta (unobservable property)

Trustor (Alice)

Source: Adapted from Bacharach and Gambetta (2001)

Simmelian Model of Trust (Mllering, 2001) is a theory explaining three-step process of trust. These steps are interpretation, suspension and expectation, as shown in Figure 3. Interpretation signifies the process when one perceives something and interprets it into something else based on incomplete knowledge. No one is able to know and understand everything (Weber, 1913), and subsequently, interpretation always contains

A multitheoretical approach for solving trust problems in B2C e-commerce 379 uncertainty. According to Mllerings (2001) discussion on the work of Simmel (1964) regarding the three-step process of trust, suspension signifies the process when one must ignore uncertainty in interpretation, because one does not have complete knowledge or information about what one perceives. By ignoring uncertainty, interpretation is temporarily valid. Expectation signifies the process when one expects that the interpretation is correct and suspension is the right thing to do. The outcome after these three steps is either trust or distrust. For example, Alice interprets a lock sign as security even though she does not understand anything at all about cryptography. The lack of knowledge about cryptography causes uncertainty in her interpretation. What if her interpretation is wrong? In order to trust or distrust, she must ignore this uncertainty by believing her interpretation to be correct or incorrect. For the outcome to be trust, she must believe that her interpretation is correct, and ignore any thought about her interpretation being wrong. She then expects that the interpretation and suspension are correct, and therefore, she trusts a lock sign as security.
Figure 3 Simmelian model of trust (see online version for colours)
Hm I might be wrong, but I dont care about cryptography or how secure it is. Well, I believe it is secure after all.

I believe a lock sign means security though I have no idea about cryptography at all.

Interpretation

Suspension

Expectation

I trust a lock sign is security that will protect my transaction.

Outcome Knowledge (Lock sign) (Security) Uncertainty (Cryptography)

Source: Adapted from Mllering (2001)

Trustworthiness is a theory that represents perceived value of someone or something about how trustworthy they are. According to Hardin (1996) trustworthiness is the main component in trust, because trustworthiness must be perceived before one can either trust or distrust, as shown in Figure 4. Others (Blois, 1999; Philips, 1996) also agree that unless one demonstrates ones trustworthiness to another party, it is impossible for one to establish trust (Jones, 2002; Lewis and Weigert, 1985). To explain Hardins theory; Alice is shopping from two websites. She notices that one website has a digital certificate signed by a known TTP, because a lock sign appears without any pop-up warning message. Another shopping website also has a digital certificate, but it is signed by an unknown TTP, and a lock sign appears with a pop-up warning message. Based on her knowledge, the second website might be fraudulent since it is verified by an unknown TTP. Accordingly, she decides to purchase from the first one. Alices decision may sound simple but is it logical?

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Figure 4

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Trustworthiness (see online version for colours)

A digital certificate is valid. Should I trust?

Trustee (E-vendor)

Trustor (Alice)

Trustworthiness (Digital certificate )

Source: Adapted from Hardin (1996)

Although each of the above theories provides useful insights into trust or perception, none of them completely address all issues. Semiotics does not explain trust at all. It only focuses on perception (i.e., interpretant, representamen, and object). Trust in Signs only explains how one perceives a physical sign and interprets it. It does not describe how trust is established. Simmelian Model of Trust explains the trust processes without adequate detail. Trustworthiness only explains two core components trustworthiness and trust. It does not clarify how trust is developed and what is involved. Therefore, to address trust issues from the consumer and the e-vendor points of view, the above four theories are integrated to guide the study discussed in this paper. Firstly, representamen from Semiotics and manifesta from Trust in Signs are merged because they share similar definitions. While the scope of representamen covers everything that can be perceived as a sign (i.e., physical or imaginable things), manifesta only focuses on physical characteristics. Hence, in Figure 5, representamen (i.e., a sign) leads to krypta (i.e., a hidden property).
Figure 5 An integrated supportive theory (with an example)
(Correct/Incorrect) (Cryptography) Krypta
Su sp en

Uncertainty

(Lock sign) Representamen Triad of Trust

Pe tio ep rc

In t

erp re ta

si on

tio n

Triad of Trustworthiness Object (Security) Interpretant Expectation (Trust/Distrust) (Alice)

A multitheoretical approach for solving trust problems in B2C e-commerce 381 Secondly, three-step process of trust (i.e., interpretation, suspension and expectation) from Simmelian Model of Trust is integrated to establish the relationships between representamen, krypta and object. As shown in Figure 5, interpretation points from interpretant to krypta indicating that a person interprets a representamen to be krypta. Since there is always uncertainty in interpretation, the arrow pointing from representamen to krypta is labelled as uncertainty, which signifies that the representamen may not have the krypta as it is interpreted by interpretant. Also, Suspension points to uncertainty because interpretant needs to ignore uncertainty for the relationship between representamen and krypta temporarily being certain. Expectation points from interpretant to object signifying that interpretant expects interpretation to be true and suspension to be correct. In other words, by perceiving representamen, interpreting it into krypta, and suspending uncertainty, interpretant expects that representamen and krypta lead to the correct object. Thirdly, the integration of three theories Semiotics, Trust in Signs and Simmelian Model of Trust forms two triads of trustworthiness and trust. With the theory of trustworthiness explained by Hardin (1996), these two triads can be seen as two concepts. The triad of trustworthiness clarifies how trustworthiness is established, and the triad of trust explains how trust is developed. According to Hardin (1996) since trust is more visible than trustworthiness, triad of trust is drawn with a continuous line while trustworthiness is drawn with a dashed line in Figure 5. These two triads are closely connected together illustrating the trustworthiness theory of Hardin (1996), which states that, in order to improve trust, one must improves trustworthiness. To illustrate Figure 5 as a representation of trust in B2C e-commerce, Alice (i.e., interpretant) looks for something that can be referred to as security (i.e., object) before making a transaction. She perceives that there is the lock sign (i.e., representamen) on her web browser as soon as the front page of a shopping website is loaded. Based on her knowledge (i.e., interpretant), she interprets that the lock sign means encryption (i.e., krypta), but also is aware that the lock sign on the screen might not be genuine due to fraudulent websites (i.e., uncertainty). However, in order to make a decision to shop, she must ignore (i.e., suspension) this uncertainty and believe that the lock sign is legitimate or fraudulent. Presumably, based on her personality (i.e., interpretant) she believes that the lock sign is legitimate, and thus believes that her perception, interpretation and suspension is correct (i.e., expectation). Therefore, she trusts that the lock sign leads to security. Conversely, if she interprets that the lock sign is not legitimate, then her interpretation leads to insecurity, and therefore, the result is that she distrusts the lock sign. To accomplish the research discussed in this paper, relevant variables (i.e., cybercrime, security, control, web interface, and TTP), which impact trust in online shopping were identified from the literature. However, addressing all the issues in Figure 5 was beyond the scope of this research. Therefore, in answering the research question which factor is perceived to be trustworthy by a consumer when shopping online only representamen is the focus of this study.

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Research propositions and model

From a review of relevant literature, 21 propositions (including subpropositions) were established to address representamen in relation to trust in online business: P1 P2 Trust will positively affect consumer intention to shop online. Cybercrime will negatively affect consumer trust. P2a P2b P3 P4 Cybercrime will negatively affect consumer trust in e-transactions. Cybercrime will negatively affect consumer trust in e-commerce websites.

Cybercrime will negatively affect consumer intention to shop online. Security will positively affect consumer trust. P4a P4b P4c Security will positively affect consumer trust in e-transactions. Security will positively affect consumer trust in e-vendors. Security will positively affect consumer trust in e-commerce websites.

P5 P6

Security will positively affect consumer intention to shop online. Cybercrime will positively affect the need for control. P6a P6b Cybercrime will positively affect the need for online environment control. Cybercrime will positively affect the need for information control.

P7 P8 P9

Control will positively affect consumer trust in e-vendors. Web interface will positively affect consumer intention to shop online. Web interface will positively affect consumer trust. P9a P9b Web interface will positively affect consumer trust in e-vendors. Web interface will positively affect consumer trust in e-commerce websites.

P10 Web interface will positively affect security. P11 Web interface will positively affect control. P12 A TTP will positively impact web interface.

A multitheoretical approach for solving trust problems in B2C e-commerce 383 Figure 6 shows the proposed research model including the 21 propositions. There are five representamens (i.e., cybercrime, security, control, web interface, and TTP) perceived by consumers according to the literature review. All these representamens directly affect trust either positively or negatively, and also impact one another.
Figure 6 The research model (see online version for colours)

Representamens P8(+) Web Interface y Graphics y Structure y Content y Social-cue P10(+) Security P9(+) P11(+) Control y Environment y Information P7(+) Trust y E-transaction y E-vendor y Website Intention to Shop Online P5(+) P4(+)

Trusted Third Party

P12(+)

P1(+)

P6(+) Cybercrime y Identity theft y Fraud P2() P3()

Research method

The study was undertaken via an online survey with consumers to establish their perceptions of trust in B2C e-commerce. The advantages of online survey are fast, convenient, access to online consumers worldwide, ability to obtain a response to every question and easy to transfer responses from a database to statistical software (Corbitt et al., 2003; Singh and Burgess, 2007; Singh and Byrne, 2005). The questionnaire was firstly developed with Microsoft Word to focus on creating each question carefully. The questionnaire was divided into two sections. The first section contains a question directed to online shopping experience on a categorical scale. Respondents were asked about their online experiences to ensure that they were real online shoppers. The second section consisted of 65 questions to establish trust issues in online shopping on a 5-point Likert scale, based on the research propositions and literature. The questionnaire was then rewritten in formats of Extensible Hypertext Markup Language (XHTML) and Cascading Style Sheet (CSS) to ensure compatibility with popular web browsers such as Microsoft Internet Explorer and Mozilla Firefox. It was developed with Notepad to minimise loading time of online questionnaire. XHTML and CSS code of questionnaire was validated with World Wide Web Consortium (W3C) online validators to ensure that it complied with the standards. The online questionnaire was presented in a single page to provide for simple navigation. The respondents provided answers by clicking on radio buttons and hitting a SUBMIT button upon completion. Questionnaire responses were transmitted to a database created in MySQL format, which was hosted by RMIT University. A server supported a secure connection to guarantee privacy of respondents. The online questionnaire was then pilot tested with academics and students at RMIT University.

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University students, graduates and faculties are likely to shop online more than other population (Canadian Internet Project, 2005; Cheeseman Day et al., 2003; Dessewffy et al., 2003; Findahl, 2003; Research Center for Social Development, 2005), and therefore, 22 universities across the globe were contacted to participate in this survey. In addition, since all these participants were educated, it was assumed that they were familiar with internet technology and e-commerce issues. Of these, 19 universities agreed to participate in the survey from which 540 valid responses were generated and stored in a MySQL database. The survey data were then imported and collated in SPSS.

4.1 Data analysis

SEM was used to analyse the survey data based on Byrne (2001) and Hoyles (1995) opinions that: It validates the relationship between two variables while taking effects of other variables into account. It is suitable for a causal relationship. It shows the accuracy of results by evaluating the measurement error.

Owing to the fact that some propositions did not have enough data to be analysed with SEM, five propositions were not tested. Dropping these did not affect the objective of this research, which is to establish a trust model, and not an intention model. For SEM, two-step approach analysis suggested by Anderson and Gerbing (1988) was performed. Firstly, an Exploratory Factor Analysis (EFA) was completed with the use of SPSS. Then, a Confirmatory Factor Analysis (CFA) was carried out with Analysis of Moment Structures (AMOS), which is a software developed especially for SEM (Byrne, 2001; Hair et al., 2006). Kaiser-Meyer-Olkin (KMO) and Bartletts test were used to determine collated data was adequate to be analysed with EFA. The value of KMO was .913 and Bartletts test was p = .000. According to Hair et al. (2006) and Brace et al. (2006), KMO > .8 means that the data is meritorious, and p < .05 indicates that the data is suitable for analysing with EFA. Based on EFA, the data was extracted with Maximum Likelihood (ML) and rotated with oblimin in order to examine the data dimensionality and to develop a measurement model. A combination of two tests Eigenvalues (>1) and scree plot generated by EFA were used to justify the number of extracted factors (i.e., a group of similar items) from the data. Subsequently, eight factors were extracted with 57% variance. These were Trust, Security, Control, Fraud, Privacy, Design-V, Design-W and Warranty, presented in Figure 7. Cronbachs was used to assess the reliability of each factor, results of which are presented in column 3 of Table 3. Nunnally (1978) recommends that the value of Cronbachs higher than .7 indicates a good reliability suggesting that all factors in this analysis are reliable. These eight factors were then put together according to the research model (Figure 6) in order to establish the measurement model (Figure 7), which was used for CFA analysis.

A multitheoretical approach for solving trust problems in B2C e-commerce 385

Figure 7 The measurement model generated by EFA (see online version for colours)
Design-V Security

Design-W

Warranty

Trust

Fraud

Privacy

Control

According to AMOS, the survey data is not multivariate normally distributed. Therefore, bootstrapping with Maximum Likelihood (ML) was used to assess the measurement model. Bootstrapping is a statistical technique that allows the model to be assessed under the condition multivariate normal distribution (Byrne, 2001). Row 2 of Table 2 indicates the goodness-of-fit values of measurement model. By comparing this result with the requirements of goodness-of-fit values (Row 4 of Table 2), the model is moderately supported.
Table 2 Model Original Modified Requirement The goodness-of-fit values

2 (bootstrap)
1455.808 307.733 = df
2

df 1156 253 >0

2/df
1.259 1.216 <2

p (bootstrap) .002 .002 > .05

RMSEA .073 .045 < .05

IFI .798 .957 > .9

CFI .797 .957 > .9

The measurement model (Figure 7) was then modified based on Modification Indices (MI) generated with AMOS that indicated which items can be dropped from the analysis. The model was carefully rectified by dropping one item at a time. The model modification was also thoroughly revised to ensure theoretical sense. The goodness-of-fit values were improved, as shown in Row 3 of Table 2. Although the requirement of p value is >.05, our model obtained p < .002. The p value can be inaccurate with a higher number of sample (Byrne, 2001), and therefore, other goodness-of-fit values must be used to justify validity. Based on the values of 2/df, RMSEA, IFI and CFI presented in Table 2, this research model is well supported by the SEM analysis. Column 4 of Table 3 shows that the reliability of each factor after dropping a few items still hold a high reliability factor (>.7).

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Table 3 Factor Trust Fraud Privacy Security Control

S. Pittayachawan, M. Singh and B. Corbitt

Factor reliabilities Reliability (before EFA) .841 .840 (Cybercrime) .886 .872 .954 (Web Interface) Reliability (after EFA) .841 .894 .798 .886 .872 .926 .887 .897 Reliability (after CFA) .841 .876 .802 .886 .807 .873 .741 .744

Design-W Design-V Warranty

Discussion

It is evident from column 2 of Table 3 that, by exploring the dimensionality in the survey data with EFA, the findings reveal that two factors (i.e., Cybercrime and Web Interface) are multidimensional: 1 Cybercrime, in the viewpoint of consumers, appears to contain two factors Fraud and Privacy. Furthermore, the correlation between these two factors is extremely low (.102), which indicates two different types of problems in cybercrime. This finding is different from the literature review (Anti-Phishing Working Group, 2007; Federal Trade Commission, 2007), which indicates three issues of cybercrime (identity theft, fraud and privacy). The implication from this research finding is that consumers perceive identity theft associated with fraud, and privacy to be a separate factor. Regarding Web Interface, consumers also see two different issues, which are design/functionality (Design-V and Design-W) and credibility/assurance (Warranty). The difference between Design-V and Design-W is that, the former factor contains items used to validate the relationship between web interface and trust in e-vendors while the latter factor contains items used to validate the relationship between web interface and trust in shopping websites. Warranty contains items related to a trusted seal, a security and privacy statement and information credibility. Regardless of their differences, the findings show moderate correlation between Design-V, Design-W and Warranty (>.4). A possible explanation for this is that even though design/functionality and credibility/assurance are different issues, consumers still observe these issues from the same source, which is the web interface.

To establish trust issues in a greater depth, propositions 2, 4 and 6 presented in Table 4 were tested in groups of two or three based on how closely they were related. Particularly, they are the subpropositions established to address the same issues. The findings of the study presented in Figure 8 indicate the following regarding trust in B2C e-commerce: Privacy and security are most important issues in consumer trust. The regression weight values of .225 (Privacy Trust) and .192 (Security Trust) indicate their impact on trust to be higher than the impact of other factors. The p values < .005

A multitheoretical approach for solving trust problems in B2C e-commerce 387 show that this is significant. This finding confirms the opinions of Belanger et al. (2002) that privacy and security are important factors for winning consumer trust in online business. Web design (Design-V Trust = .170) and control (Control Trust = .162) together impact trust. Data analysis also reveals that web design (Design-V Security = .161) impacts security which then affects trust. It is also evident that privacy (Privacy Control = .369) impacts control which affects trust. This finding supports the views of Wang and Emurian (2005) and Olivero and Lunt (2004) that web interface and control are important components fostering consumer trust. Warranty impacts security (Warranty Security = .202) and control (Warranty Control = .275). A trusted seal, a security and privacy statement and credible information help consumers perceive a greater security and control which lead to trust in the B2C e-commerce. Identity theft and fraud (Fraud Security = .172) have negative impact on security. This finding is similar to the view that customers perceive less security if their information has been compromised (CMO Council, 2006; Taylor Nelson Sofres & TRUSTe, 2005). A likely outcome of this is that the consumer will not commit to a purchase in such circumstances.
Summary of tested propositions Relationship Fraud Trust Privacy Trust 4a + 4b + 4c 6a + 6b Security Trust Fraud Control Privacy Control 7 9a Control Trust Design-V Trust Warranty Trust 9b Design-W Trust Warranty Trust 10 Design-V Security Design-W Security Warranty Security 11 Design-V Control Design-W Control Warranty Control Result Not supported Supported Supported Not supported Supported Supported Supported Not supported Not supported Not supported Supported Not supported Supported Not supported Not supported Supported

Table 4 Propositions 2a + 2b

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Figure 8

S. Pittayachawan, M. Singh and B. Corbitt

Standardised bootstrapping ML estimates (see online version for colours)

Design-V

.161 * .170 * n.s.

Security

.683 **** n.s. .540 **** .192 **** n.s. n.s. .630 **** .202 **

.289 ****

Design-W

Warranty
.275 *** .172 ****

n.s.

Trust

n.s. .192 ****

Fraud
n.s. .138 ** .225 **** .162 **

* p < .1 ** p < .05 *** p < .01 **** p < .005 n.s. p > .1

Privacy

.369 ****

Control

Applying the findings discussed above to a B2C e-consumer: Alice will look for privacy and security on the shopping website. Privacy will tell Alice how her information will be protected by an e-vendor, and security will ensure secure transaction. She will then explore web design and determine legitimacy of the e-vendor, and also look for controls that will protect her information from further use either for marketing or by other online sellers. Warranty such as a trusted seal will further enhance Alices trust on the e-vendor. However, before committing to a purchase, Alice will also explore the existence of identity theft and fraud problems with this e-vendor. Perhaps, she will ask her friends who are knowledgeable about security and risks in online shopping, or search e-commerce news on the internet. For instance, E-commerce Times4 that is an online e-commerce newspaper reports security breach issues in a daily basis. If Alice finds that the database containing millions of credit card numbers kept by this e-vendor has a history of getting hacked and there is no evidence of any security improvement, this will negatively impact security and then trust on this particular e-vendor.

Implication

This paper suggests that the four theories (i.e., Semiotics, Trust in Signs, Simmelian Model of Trust and Trustworthiness) are a good guide to address trust problems in B2C e-commerce. They helped answering the question what factors impact consumer trust in B2C e-commerce. The scope of this research allowed us to focus on trust from the e-vendors point of view. Based on the findings in this paper, it is suggested that an e-vendor should focus on addressing privacy (e.g., information and spam e-mails) and security (e.g., secure connection) since they have most impact on trust than other factors. After that, web design (e.g., functionality and ease of use) and control (e.g., consumer consents and monitor) should be considered to foster greater trust. Although warranty (e.g., a trusted

A multitheoretical approach for solving trust problems in B2C e-commerce 389 seal and a security and privacy statement) does not have any direct impact on trust, it improves consumers perceptions of security and control. Lastly, an e-vendor should avoid having any negative history regarding identity theft and fraud (e.g., credit card detail is stolen via database hacking), because these indicate to consumers that security used by an e-vendor is inadequate. Particularly, with the internet, consumers can easily search for news, consumer comments and feedback, and e-commerce review websites regarding e-vendors and their websites. In addition, they may ask their friends who are well-informed about security breaches in online shopping.

Limitations

The results presented in this paper are still in a preliminary stage of analysis, and the research is in progress. The goodness-of-fit values indicate that the model is well supported, and therefore, it indicates high construct validity of the research model. However, discriminant validity is still the problem of this model, because Design-V, Design-W and Warrantee are moderately correlated with one another at the values of higher than .540. In addition, the findings reveal that Design-W is statistically insignificant. In order to improve the model parsimony, this factor must be taken off and the model must be reanalysed (Hair et al., 2006). It is expected that the p values of each regression path may be changed due to the absence of this factor, and the conclusion for each relationship (i.e., regression weight values) may also be different from this paper. Therefore, the reader should use the findings with caution.

Conclusion

Trust is widely addressed as an important issue in B2C e-commerce. This research identified important trust issues that e-vendors can implement to enhance trust. The findings of this study include consumer issues analysed statistically to identify trust issues pertinent for all e-vendors. This paper reports that, by addressing trust issues from multiple angles including privacy, security, web design, control, warranty and fraud, e-vendors are able to win consumer confidence and enhance B2C e-commerce. Since this paper aims to propose a trust model, the relationships between the representamens (i.e., cybercrime, security, control, web interface, and TTP) and intention to shop online were not investigated. It is not known how these representamens affect intention to shop online, and therefore, further research is required to validate the proposed model with this factor. The in-depth study of how consumer personality and knowledge affect perception (i.e., exploration of interpretant) and what representamens are interpreted into (i.e., exploration of object) are also other possible new directions of the research.

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Notes
1 2 3 4 http://www.verisign.com http://www.geotrust.com http://www.truste.org http://www.ecommercetimes.com