TY - JOUR
AU - Bellucci,, Emilia
AB - Abstract The need for an appropriate jurisdiction for electronic commerce disputes has led to the well-established mechanism for solving disputes through the internet known as the Online Dispute Resolution (ODR). Currently, there is no universal agreement about the concept of trust in ODR systems, although this issue has been widely discussed in the field of Alternative Dispute Resolution (ADR). The current study aimed to develop a set of standards to enhance trust and confidence in using ODR systems. In this study, we have adopted a new approach in the ODR field, and no similar research has been conducted. This study used a quantitative (online survey) and mainly qualitative approach (interview) for gathering data. After analysing data, this research identified three elements as standards to measure trust in ODR systems including knowledge, expectations of fairness and code of ethics. Finally, our findings provide several practical and methodological implications. INTRODUCTION Electronic commerce (e-commerce) has provided an opportunity to conduct transactions not only for high value but also for low-value transactions which were previously rare and complicated.1 There are many different classifications for e-commerce types. This research mainly focuses on Business-to-Consumer (B2C) e-commerce which is defined as ‘activities of businesses serving end consumers with products and/or services’.2 In e-commerce, as in offline commerce, disputes arise. For resolving e-disputes traditional mechanisms, such as courts and Alternative Dispute Resolution (ADR), are time consuming and expensive.3 This has led to the development of appropriate dispute resolution systems for online environments, known as Online Dispute Resolution (ODR). Compared with traditional ADR, ODR has several advantages such as time and cost resources savings, the flexibility of the process, more speed, transparency and traceability.4 Advances in Dispute Resolution and Information Technology over the past forty years have led to the current evolution of ODR: The 1970s—the rise of the ADR movement—resulting from the Pound Conference5 and the publishing of Fisher and Ury’s ‘Getting to Yes’ book.6 The 1980s—the development (and hype about) futuristic expert systems to model legalistic decision making—McCarty (TAXMAN)7 and Susskind and Capper (Latent Damage Adviser).8 The 1990s—the development of the Internet and initial proposals for ODR. The 2000s—the development of ODR for Ecommerce—in particular, its use by EBay and PayPal.9 The 2010s—The development of practical usable systems—Rechtwijzer (Netherlands)10 and Civil Resolution Tribunal (British Columbia).11 Recently, ODR has moved beyond Ecommerce—ODR is being used for non-financial disputes (see for instance, the work of Katsh, Ethan and Orna Rabinovich-Einy 12and the access to justice work at Kent Law School.13 The Civil Resolution Tribunal System, used in British Columbia is currently a very significant real world use of ODR. It provides online dispute resolution in a number of domains—strata titles, small claims and motor accidents.14 Trust building is an important concern in ADR, but trust-related issues could pose greater challenges for potential users in ODR systems. In ODR, because of the lack of face-to-face interaction, users cannot benefit from different forms of incoming non-verbal information such as face-to-face communication, handshakes and eye contact.15 Indeed, trust plays a very significant role in ODR systems, and if there is not any level of trust for the ODR process, consumers and businesses would not submit their dispute. So, companies should create a level of trust for consumers to resolve their dispute through ODR and providers of this process need to gain trust through being honest, forthright and reliable.16 Currently, there are no identified elements to measure trust in ODR systems and each ODR systems has its own guidelines. This study investigates the factors that contribute to measuring and defining trust in ODR systems that can be applied universally to enhance fair practice and maximize consistency of ODR systems. Moreover, an international framework for ODR could create more certainty and growth for industries and businesses in the context of e-commerce. Also, consumers would better understand how their dispute could be solved. It would moderate the inequality of bargaining power between consumers and businesses in online transactions and create more confidence for online practitioners in international trade. Recently, ODR has been used for civil, family and international disputes17. While our framework has been developed for e-commerce disputes, it can be adapted for civil, family and international disputes across jurisdictions. In addition, most researchers in the ODR field have conducted descriptive work, such as Chang, Hussain and Dillon,18 Del Duca, Rule and Loebl,19 Ebner,20 Hörnle,21 Pecnard22 and Ong.23 Although researchers have noted that there is a need to resolve current issues of ODR to increase the quality of ODR systems, their work focuses more on explaining what ODR is, and its advantages and disadvantages, rather than on how to define or measure trust. This study makes a significant and original contribution to understanding the concept of trust in ODR systems by providing empirical evidence. As the emergence and discovery of ODR is based on many years of work in the field of ADR, we apply relevant theoretical understandings from ADR literature to develop recommendations for ODR systems. Hence, the research question in this research is: What is an appropriate concept of trust in ODR and how can it be measured? How is it different from relevant notions of trust in traditional ADR? This study adopts an exploratory sequential mixed methods approach, using quantitative and mainly qualitative research to answer the research question. We first reviewed literature. Next, we conducted interviews with six ODR providers which were thematically analysed using NVIVO qualitative analysis software. After analysing qualitative data, in the next stage we verified our findings by conducting surveys of 108 consumers with online purchasing experience. Finally, an interpretation of qualitative and quantitative findings was conducted.24 The organization of this article is as follows. ‘Literature review’ section examines issues of trust discussed in the ADR and ODR literature. ‘Research methodology’ section outlines our research methodology. ‘Findings and discussion’ section discusses and interprets the extensive knowledge gained from our investigations. Finally, the implications and the limitations of this study are presented in ‘Research implications and limitations’ section. Literature review Trust: some definitions and concepts The significance of trust in interpersonal relationships has been stressed by Golembiewski and McConkie25 who pointed out that ‘perhaps there is no single variable which so thoroughly influences interpersonal and group behaviour as does trust…’. Trust has been extensively studied. Definitions differ widely, as scholars may not accept or understand the definition of trust in other disciplines.26 Mayer, Davis and Schoorman27 suggest there are several reasons for such disagreements in definitions of trust: ‘problems with the definition of trust itself; lack of clarity to find the relationship between trust and risk; confusion between trust and it’s antecedents and outcomes; and failing to consider both the trusting party and the party to be trusted’. Trust has been studied in both the sociological and psychological literature. From a sociological point of view, trust should be accepted as a social concept, not something isolated within individuals.28 Trust is defined in psychology literature as one person having faith in another person.29 Trust is a psychological state or position of an individual (the truster) in regard to a particular partner (the trustee), meaning the truster needs to attract the trustee’s cooperation to obtain valued results or resources30 However, this view of trust has been rejected by David Lewis and Andrew Weigert31 who argue that trust cannot be defined as a personal characteristic. Regardless of the discipline of authors, the most common definition of trust is ‘the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustors, irrespective of the trustors’ ability to monitor or control that other party’.32 This definition includes the real relationship with another identifiable party who is perceived to act and react with volition towards the trustor.33 According to Sidney Landau,34 the most important factors behind trust are: (i) ‘a confident reliance on the integrity, honesty, or justice of another; faith’; (ii) ‘a confidence in the reliability of persons or things without careful investigation’; and (iii) ‘confident expectation; belief; hope’.35Denise Rousseau, Sim Sitkin, Ronadl Burt, Colin Camerer36 argued that a widely held definition of trust, after considering contemporary and cross-disciplinary scholarly literature, is: ‘Trust is a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behaviour of another’.37 By comparing these different ideas about the concept of trust, a consensus definition will emerge and help practitioners and researchers eliminate confusion and lead to a shared meaning of trust. Trust in information technology and e-commerce: definitions and concepts It is important to understand the notion of trust and its implications in information technology. The first implication is in the use or adoption of a technology. The second implication of trust in IT is its influence on other IT perceptions such as efficiency of the technology.38 In addition, the absence of quality control and standard procedures and behavioural and environmental cues, affects the establishment and growth of trust and results in difficulties in building trust in an online environment.39 Trust in IT relies on infrastructure systems such as the web or on specific information systems like Microsoft Excel.40 Formally, the concept of trust is ‘a secure willingness to depend on a trustee because of that trustee’s perceived characteristics’.41 A lack of trust continues to be an obstacle to adopting different kinds of e-commerce. One of the main factors for the success of e-commerce is the high level of service quality, which means the judgments and evaluations of the quality of online delivery by customers.42 The three dimensions of service quality—responsiveness, trust and empathy—are critical to the achievements of e-commerce.43 Therefore, in the online environment trust is defined as a customer’s readiness and consent to face vulnerability in online transactions in their positive assumptions according to future online retailer manners and actions44, since customers depend on trust as an initial mechanism to reduce transaction uncertainty.45 According to Sirkka Jarvenpaa, Noam Tractinsky and Lauri Saarinen46 trust in e-commerce is affected by the customer’s attitude to an online store’s size and the reputation of the business. Reputation means the degree to which a consumer believes a trader is professionally competent or benevolent or honest47. Also, reputation is an intangible asset; it is harder to establish it than to lose it, and it is created by a long-term investment of resources and attention to customer needs. The subject’s level of knowledge influences trust in online shopping. This means knowledge or familiarity decreases social uncertainty and promoting awareness about what is likely to happen lessens uncertainty and leads to increasing trust.48 There are several elements that affect a consumer’s trust in e-commerce including: knowledge, trust propensity, perceived integrity, online payment security concerns and online shopping activities. Wang et al49 discussed the relationship among knowledge, trust in online shopping, and the intention to go shopping online. The results revealed that knowledge is positively associated with trust and online shopping activities. Moreover, a consumer’s perceived integrity of an e-commerce website is positively related to trust in online shopping. As a result, the integrity of the online business is a significant moderator which influences the individual’s motivation to buy online. However, another study found trust propensity is not associated with trust in e-commerce; that is, when a consumer has an online shopping experience the propensity to trust is not as significant a factor as before.50 In addition, a significant way of building trust for online shoppers is through reading and posting product reviews and review forums.51 A significant way of building trust is by protecting consumer private information. The 2015 Certificate Authority Security Council’s (CASC) Consumer Trust Survey in the USA indicated that e-companies should always remember that without protecting private information consumers will not trust them. Moreover, consumers seek the highest degree of protection available and identified the padlock and green bar as providing a trusted connection. E-commerce and regulated industries need to have high validation to provide greater trust and assurance to consumers and to safeguard against fraud. Using certificates creates the most reliable indicator of the trustworthiness of the site and provides a high degree of accountability to consumers.52 Therefore, the absence of trust in e-commerce has been identified as a significant barrier for successful business transitions53 In e-commerce, this obstacle is more challenging than compared to face-to-face interaction and causes a weakened consumer trust due to a lack of contiguity in time and space, issues of privacy and confidentiality of personal and credit card information54 and an absence of physical interaction.55 Trust in alternative dispute resolution Trust is important in alternative dispute resolution systems as it enhances the likelihood that individuals will resolve their conflict56. In designing legal systems, it is important to gain the trust of the society; otherwise violence and crime can ensue.57 A legal system resolves disputes and should maintain confidence in the system of government. Dispute resolution is hindered by failures in communication between the parties because of the distrust each other. ADR is built upon the hypothesis that if parties can trust each other, they can more readily resolve the dispute and reach an agreement which is generally similar to the result that a court might impose, while the adjudicatory system is based on the theory of fundamental distrust and assumes disputants ‘means never put faith in the adversary’.58 In contrast, litigation is formal, time consuming, divisive, tricky and distorting. So, in designing ADR processes, building trust is fundamental.59 In addition, for the public to engage in ADR requires neutrals to be trustworthy. There needs to be a sensitive and special relationship between the disputants and neutrals, similar to a relationship between a lawyer and a client or a patient and a doctor. To establish trust in ADR, there needs to be in existence either a code of ethics or private ADR professional standards.60 While such codes are not easily enforceable, they can create public trust and lead to universal standards and rules for ADR.61 For example, in mediation one of the most significant roles of a mediator is to create trust between the disputants.62 In litigation and attorney to attorney negotiation, most communication between the disputants is prevented. Many mediators make the effort to engage in enhanced communications with clients so that the disputants will understand and trust each other.63 Mediator neutrality is central to western concepts of mediation. Its existence ensures a degree of trust that encourages a party to express his or her preferences and consider trade-offs suggested by a mediator.64 When the parties place their trust in a mediator they are more inclined to engage in a cooperative manner.65 Mistrust hinders successful mediation outcomes.66 As with mediation, some level of trust between the disputants is necessary in other forms of negotiation. According to Todd H. Chiles and John F. McMackin67 trust is important because ‘if we are vulnerable to another or are considering an option that makes us vulnerable to another, then if we can trust the other, we do not need to worry about exploitation by the other’. ADR procedures are designed to create and restore trust and can overcome the suspicion and mutual hostility fostered by the adversarial system and can lead the parties to resolve their differences. Comparing the outcomes and costs of both litigation and ADR, all parties benefit more from ADR.68 Trust in online dispute resolution Noam Ebner and John Zeleznikow investigate the issues of trust as well as fairness and security in ODR.69 ODR should help to create an environment of trust in ecommerce, thus enhancing consumer perception of, and belief in, the trustworthiness of a given service or site.70 Trust building is an important concern in ADR, but trust related issues can create major challenges for potential users of ODR systems as they mostly have online communication rather than face-to-face interaction.71 In ODR, disputants might be unsure of how to reach an agreement as they have little faith that the other party would abide by the mediated agreement. The absence of trust can hinder resolution, even when it is obvious that disputants would be better off resolving their dispute by ODR.72 ODR service providers rely on disputants and third parties to respect confidentiality, refrain from being partial or judgmental, and not to design rules that disadvantage one side. Online opponents negotiate often without knowing each other, a potential obstacle to building informed trust.73 For ODR systems to be efficient, it is imperative to build some trust and confidence in them.74 Noam Ebner75 acknowledges three aspects of trust in ODR: ODR as a facilitator: growing consumer confidence in e-commerce systems may be demonstrated by the degree of incorporating ODR into their financial dealings. Ongoing use of the internet depends on successful e-commerce, which in turn relies on trust more than on anything else. Users’ faith in ODR as a functional way of solving disputes: technology should be marketed and constructed to create public trust that ODR is an effective way of solving their dispute. In fact, with a low level of trust in ADR, what ODR providers have not heard claims that the public would not buy into ODR in general as it is a foreign concept. Dispute resolution needs warmth and human interaction while the internet is cold and distant. Interpersonal trust: Users of ODR not only have inherent distrust in conflict situations but they are also challenged by the online environment. While these two aspects may have much in common, there are also conceptual differences and each includes fundamentally different players. People contemplating the use of ODR need structured information to make informed choices about whether or not to institute the process and which provider to choose. Lack of knowledge, often in the form of transparency is considered a major concern for the use of ODR, and can lead to a lack of trust.76 Users are able to trust the outcomes of an ODR if they have sufficient knowledge and understanding of the process by which the resolution is achieved. In addition, there is likely to be little confidence if users do not trust online neutral mediators who work with the ODR with distrust impacting their acceptance of dispute resolution. Mediators need to build positive relationships between parties, because those involved in the dispute may need to continue to interact.77 Three important components for growth of ODR are trust, control and government.78 Trust is a perennial problem in virtually all online activities, though is of greatest importance for activities where participants are the most invested and impacted. One such online activity is ODR, where financial, emotional and structural aspects impact the trust we place in ODR. While ODR developers can try to design platforms for trustworthiness, it is the end users who have control over how it is utilized and therefore the trustworthiness of the system. Ultimately, ODR providers have a responsibility to enhance user’s trust in the system. ODR service providers can exert control and enhance confidence in different ways, such as reputable institution furnishing which requires reliable information to be provided. Users are more likely to choose recommended organizations in which they have more confidence. For example, if the ODR provider does not comply with the indicated standard of delivery, the institution will stop using it.79 In addition, by issuing press releases providing relevant information such as telephone numbers, email and physical addresses and ensuring the use of data protection rules; and by explaining the process and their use of third party neutrals, trustmarks and feedback mechanisms, ODR providers can create a climate of confidence. They can also provide instant feedback which is a considerable advantage over the use of ADR. In addition, parties should be allowed to provide feedback regardless of their success or otherwise.80 Governments are most likely to be trusted by consumers to provide appropriate information about ODR, given their status in law and responsibility to keep society functioning under socially acceptable norms, such as trust and control.81 Accreditation is a typical form of structured information. Relevant certification may be displayed through Trustmarks.82 There are two basic types of trust—identification-based trust (IBT) and calculus-based trust (CBT)—the former depends on the degree to which parties care about each other. ODR practitioners may encourage parties to investigate each other’s reputation, similar to viewing the feedback rating of an eBay seller.83 Online disputes often increase scepticism between opposing parties and the mediator. If developing relationships is neither feasible nor desired, the mediator may want to focus on CBT,84 which can be described as the reception of a certain level of exposure based on the calculated costs of upholding or dissolving a relationship. With CBT, which features self-enforcing, binding agreements, individuals deliver what is expected of them in order to avoid penalties.85 Settlements reached through ODR are generally legally enforceable (under contract law) as are private mediation agreements.86 According to empirical studies, a well-designed ODR platform creates a sense of justice and fairness in the marketplace for users which in turn improves the trust and loyalty of those who seek advantage from redress systems.87 Research on trust in ODR confirms that the most active buyers on eBay are those with experience of resolving their dispute via eBay’s ODR software. These users increased their commercial activity more than users who had not experienced disputes in e-commerce.88 Colin Rule89 noted that: ‘The explanation for this phenomenon is that trust in your fellow users to do the right thing in good faith is more powerful than the belief that a marketplace administrator will intervene and use their power to decide disputes between users who disagree’. This suggests that successful use of ODR as an effective redress mechanism in e-commerce90 installs confidence in trusted users.91 Moreover, consumers accept the fact that mistakes can occur online but this does not prevent them from purchasing online if the trader responds appropriately. Chin E Ong and Caroline Chan’s92 research on understanding redress procedures in B2C ecommerce found that consumers claimed: ‘If you have shown your attitude and responsiveness to fix this problem, it doesn’t only gain my trust and confidence, but this is a very trustworthy company. It might make mistakes but it can also improve them and do better and why couldn’t I trust them and use their services more … as long as you have shown your attitude, especially the way you deal with people and cope with the situation’. Trust is a major factor in the growing use of online services and relates to how a business behaves and treats a buyer when a dispute occurs. A dispute provides a good opportunity for that marketplace to resolve the dispute and to make a positive and lasting impression on the user.93 In addition, consumers indicated that a simple and accessible redress procedure increased their confidence and trust in online shopping.94 Practitioners in ODR reported that jointly creating ground rules, building positive relationships, inviting disputants to value other’s reputation or using a brief biography and photo to introduce themselves will maximize trust.95 We have now discussed how trust is an important legal issue related to ODR which, among other roles, facilitates access to justice by promoting fairness. We believe ODR as a facilitator for e-commerce can also enhance trust in the e-commerce space. Research methodology By conducting a literature review regarding trust in ODR, we revealed gaps in definitions and standards for measuring trust in ODR systems and in empirical evidence about developers’ and users’ views about trust related issues. Therefore to fill the gaps uncovered in the literature review and to find answers to the research question, a mixed-methods approach using both qualitative and quantitative research was employed. Conducting mixed methods research has several advantages. Use of both qualitative and quantitative research methods at different stages of this research allowed the researcher to access different sources of data about the research topic and research aims, which is analysing measurements for the concept of trust in ODR. Gathering various data enabled a greater depth of understanding about the research issue compared to using one single research method and lessens limitations associated with applying only one method.96 Therefore, adopting mixed methods of qualitative and quantitative approaches provides greater and deeper insight in understanding and analysing the research topic.97 Researchers can begin with qualitative data to explore an in-depth phenomenon before proceeding to the quantitative phase.98 The last step will be interpretation of the findings.99 There are various reasons for selecting this research design such as when the variables are unknown and there is no guiding framework or theory. This design starts with a qualitative method that is best suited for exploring the phenomenon of the research.100 Therefore, in this research data were collected and analysed in three separate phrases. In the first phase of collecting qualitative data, face-to-face semi-structured interviews with ODR providers were undertaken. In second phase, a survey was used in order to collect quantitative data. Finally, phase three included interpretation of both qualitative and quantitative data to obtain results and answer the research questions. Initially, we also intended to conduct a fourth phase, between steps one and two. This would have involved interviewing Chief Information Officers at large multi-national firms (e.g. QANTAS, Westfield, Broken Hill Proprietary) about how their firms used ODR Systems. Because of the difficulty in obtaining respondents, we did not go ahead with this section of the project. The research design in the completed research is illustrated in Figure 1. In the next section, a summary of the how the qualitative and quantitative phases of the research were conducted is presented. Figure 1: Open in new tabDownload slide Overview of the research design. Figure 1: Open in new tabDownload slide Overview of the research design. Qualitative phase A qualitative approach is used when the topic of the research is limited or inadequate, research has complicated constructs and the desire is to build a theory based on the participants’ life experiences.101 The nature of this study lends itself to qualitative research, using a transcendental phenomenological design to discover participant’s lived experience of using ODR processes and the meaning they make of lived experience.102 This qualitative phenomenological research explored the lived experiences of six ODR providers and experts. We chose the developers of ODR systems, because we wished to interview those people at the coalface—those who had designed and developed currently utilized ODR systems. By interviewing them at the annual ODR Conference, we guaranteed we had a cross-section of ODR providers based upon both location type of service provided. Developers of ODR systems were chosen as they were closest to the system and must have considered trust aspects to assess the commercial viability of their systems. The first author interviewed developers attending the annual ODR Conference, which provided us with access to a cross-section of International ODR providers. Semi-structured interviews with ODR providers were conducted to explore how they define, measure and apply trust in their ODR systems, as they were closest to the system and must have considered trust aspects to assess the commercial viability of their systems. The rest of this section details the data collection process from interviews and presents the major themes identified. Qualitative data collection procedures In qualitative research, there is a purposeful selection of participations and sites in order to help the researcher to better explore and understand the research problems and research questions.103 In this study, the participants in the phenomenological qualitative research phase were experts and providers of ODR. They were chosen based on purposive sampling.104 Moreover, in a qualitative study, there is a need for in-depth interviews; analysing data from large numbers of participants would be difficult to manage.105 Therefore, the number of participants and sample size was small compared to that of a quantitative method. In phenomenological research, a small number of participants is acceptable, while a larger number of participants better illuminates the multiple facets of the phenomenon.106 In this research, the number of participants in the sample was six. In addition, John Creswell107 asserts that in phenomenology research between three and ten participants is adequate. Purposive sampling does not have a fixed number of participants as it intends to interview until redundant themes appear instead of applying a definitive sample size formula used in quantitative studies.108 Prior to the interview participants signed an informed consent form as required by the ethics approval granted by Victoria University. Participants were asked about their experiences and perceptions of the trust concepts in ODR systems. They described the meaning of these concepts and the processes of their resolution systems in online B2C disputes. The questions were mainly open-ended which the researcher allowed the participants to answer questions freely in their own words; this is a great advantage of using semi-structured interviews. All interviews were audio recorded with the consent of participants and backup notes were made of their answers. After finishing the interview, each interview was transcribed and analysed thoroughly. The interviews took place at the Pace Law School Campus, the location of the ODR Conference in Manhattan, New York City in 2015. The participants were from four different continents - North America (USA), South America (Argentina), Europe (Czech Republic and the Netherlands) and Asia (China and Japan). The interviews took place over three days of the conference from 3rd June to 5th June 2015. To ensure the accuracy of the interview transcriptions, the transcriptions were returned to each participant for review and revision. After approval was received from each participant, we commenced the data analysis process. Qualitative data analysis In qualitative research, data analysis begins with an inductive function with a large amount of information and progressively reducing the information into smaller, more distinct bracketed sets of key data and themes.109 The data analysis method for this phenomenological research study used Clark Moustakas’110 adaptation of the Paul F. Colaizzi-Keen111 method. Therefore, the seven-step method was applied in this research to analyse interview data, as follows: The first step involved transcribing all audio recorded interviews immediately after interview. Each transcript was read several times to understand the holistic sense of the content; In the second step, significant statements were manually extracted from each transcript; In the third stage, we formulated meanings from the significant statements. Each statement was coded. From this step, we extracted and identified sixteen meanings and codes from the significant statements of the six interview transcripts. In the fourth step, an inductive approach was used to identify key ideas from the primary codes and to categorize and collapse them into four clusters. In the fifth stage, clusters were sorted into a further, rich, thick, exhaustive description of the phenomenon as emergent themes which were the overarching goal of this qualitative data analysis. Three Emergent Themes were created from clusters. In the sixth stage, the explanation of the emergent themes and their relationship to clusters was applied. The final stage included returning the descriptive result (from stage vi) to participants for validation. This stage was conducted to make ensure analysis accurately reflected participant’s experiences. The identified themes, clusters and primary codes after analysing interview transcripts are shown in Table 1. These themes will be further discussed in relation to the literature review and quantitative data (the survey) in ‘Findings and discussion’ section (findings and discussion). Table 1 : Themes, Clusters and Codes Identified after Analysing Interview Data Emergent themes Clusters Primary codes 1) Reputation 1. Relationship between trust and reputation 2. Reputation creates trust 3. Feedback system and review forums affect reputation 4. Reputation by government bodies builds trust in ODR 5. Endorsed by the recognized law firms 1) Knowledge 6. Presenting official logo on ODR website 7. Transparency of the procedure 2) Transparency 8. Providing information to users of ODR process 9. Providing templates makes transparency 10. Anonymous cases for transparency of process 11. Mixing cases and creating data set for transparency 12. ODR information is trustable 2) Expectations of Fairness 3) Expectations of fairness 13. Reflection of disputant’s expectations 14. Informing users about their rights 3) Code of Ethics 4) Code of ethics 15. Neutrality of neutrals makes trustable outcome 16. Certification of neutrals by government agencies Emergent themes Clusters Primary codes 1) Reputation 1. Relationship between trust and reputation 2. Reputation creates trust 3. Feedback system and review forums affect reputation 4. Reputation by government bodies builds trust in ODR 5. Endorsed by the recognized law firms 1) Knowledge 6. Presenting official logo on ODR website 7. Transparency of the procedure 2) Transparency 8. Providing information to users of ODR process 9. Providing templates makes transparency 10. Anonymous cases for transparency of process 11. Mixing cases and creating data set for transparency 12. ODR information is trustable 2) Expectations of Fairness 3) Expectations of fairness 13. Reflection of disputant’s expectations 14. Informing users about their rights 3) Code of Ethics 4) Code of ethics 15. Neutrality of neutrals makes trustable outcome 16. Certification of neutrals by government agencies Open in new tab Table 1 : Themes, Clusters and Codes Identified after Analysing Interview Data Emergent themes Clusters Primary codes 1) Reputation 1. Relationship between trust and reputation 2. Reputation creates trust 3. Feedback system and review forums affect reputation 4. Reputation by government bodies builds trust in ODR 5. Endorsed by the recognized law firms 1) Knowledge 6. Presenting official logo on ODR website 7. Transparency of the procedure 2) Transparency 8. Providing information to users of ODR process 9. Providing templates makes transparency 10. Anonymous cases for transparency of process 11. Mixing cases and creating data set for transparency 12. ODR information is trustable 2) Expectations of Fairness 3) Expectations of fairness 13. Reflection of disputant’s expectations 14. Informing users about their rights 3) Code of Ethics 4) Code of ethics 15. Neutrality of neutrals makes trustable outcome 16. Certification of neutrals by government agencies Emergent themes Clusters Primary codes 1) Reputation 1. Relationship between trust and reputation 2. Reputation creates trust 3. Feedback system and review forums affect reputation 4. Reputation by government bodies builds trust in ODR 5. Endorsed by the recognized law firms 1) Knowledge 6. Presenting official logo on ODR website 7. Transparency of the procedure 2) Transparency 8. Providing information to users of ODR process 9. Providing templates makes transparency 10. Anonymous cases for transparency of process 11. Mixing cases and creating data set for transparency 12. ODR information is trustable 2) Expectations of Fairness 3) Expectations of fairness 13. Reflection of disputant’s expectations 14. Informing users about their rights 3) Code of Ethics 4) Code of ethics 15. Neutrality of neutrals makes trustable outcome 16. Certification of neutrals by government agencies Open in new tab Textual description of emerging themes In this section, the emerging themes from analysing participant’s responses to the interview questions are explained. These themes are as follows: Theme 1: Knowledge Theme 2: Expectation of Fairness Theme 3: Code of Ethics An analysis of these themes will be presented in the findings and discussion section. Theme 1: knowledge Knowledge was a strong theme identified from analysing interview transcripts. We noted that users should have knowledge about ODR systems; with this knowledge gained through reputation as participant 2 explained: ‘I build my structure of trust through reputation. Today in X region if you speak about ODR, you talk about me’. Participant 5 also mentioned the importance of knowledge gained through reputation: ‘One of the very strong assets I am going to use for the platform is the X legal aid board which is a public body; it is an official provider to the general public. It also has the support of the ministry of justice. So, there is a logo of the ministry as well. These are obviously two websites very important for the trust of people’. Participant 1 added: ‘Reputation is related to dispute resolution. Because if you resolve a dispute then you will affect reputation and if you don’t, you don’t have that reputation’. Knowledge for users can also be provided by transparency of the procedure in ODR systems. As Participant 3 explained: ‘We use transparency very expansively like procedural transparency, the participant’s transparency, information about how we resolve the dispute … Also the public should be able to access the information about the case if they want. There is no personal information of the participants. We don’t care about parties’ real name until they sign the resolution and they sign that in front of the notary, the notary knows their real name and personal details … in any case, dispute, evidence from parties should be fully transparent to everybody. We only ask about the dispute not the personal information such as age, marital status, etc.’ Participant 1 also emphasized the importance of transparency: ‘It’s very important for the users to explain for them how it works, it should be totally transparent, if they get into the processes they don’t understand then it’s a black box. it’s very important before they get in you give them whole of the map and you say this is how long it will be take, there are the different steps of the process, these are the possible outcomes and then the consumer will be aware.’ Participant 3 argued similarly that: ‘Transparency is an important issue; the consumer has the right to know things. In ODR, we use transparency very expansively like procedural transparency, the participant’s transparency, information about how we resolve the dispute’. One of the ODR providers, Participant 1, mentioned that they ensure transparency of their systems by guaranteeing the cases will be anonymous. He explained: ‘Take fact patterns from the case, change all the information about the cases, so there is no way to track back to the original party. The other way is aggregate the cases, you can put 20, 50 cases together and create a data set, say we have looked at one hundred cases and we learned these and other things, these are the facts, So there are ways to get the data from the cases’. Another participant reported that they mix cases and create templates and data sets for users to provide information to them about how their systems work. Figure 2 explains the primary codes and cluster themes that resulted in the knowledge theme. Figure 2: Open in new tabDownload slide Theme 1: Knowledge. Figure 2: Open in new tabDownload slide Theme 1: Knowledge. Theme 2: Expectation of fairness The Expectation of Fairness was the second theme that emerged from the participant’s responses. Parties in ODR systems expect some level of fairness, such as informing them about their rights and that the information provided by the ODR system is correct and trustable. Participant 1 explained how their ODR system sets expectations for the parties: We do something called problem diagnosis. This is when people come to an intake session. They are generally not communicating with the other side. In this session, you need to set expectations, you need to walk them through the process and you need to say ‘well these are your rights if you start this process’. Participant 4 added the expectation of fairness is related to trusting the information clients have access to through the ODR: ‘Trust has several elements; that people want to know whether the information they find is correct.’ It is important to consider parties’ expectations in order to create efficient ODR systems. The diagram below describes the expectation of fairness theme with the cluster theme and primary codes that guided the researcher to identify this theme (Figure 3). Figure 3: Open in new tabDownload slide Theme 2: Exceptions of fairness. Figure 3: Open in new tabDownload slide Theme 2: Exceptions of fairness. Theme 3: Code of ethics The need for a code of ethics theme was a theme identified through analysing the transcripts. In any ODR systems, there is a need for a code of ethics for neutrals which brings trust and fairness into their systems. Not all respondents mentioned this issue. One example was Participant 5 who stated: ‘they know that neutrals have been carefully certified and selected under the responsibility of the legal aid board and the ministry and we have a complaint procedure where parties can complain about neutrals’. The Figure 4 illustrates the code of ethics theme with its cluster theme and primary codes. Figure 4: Open in new tabDownload slide Theme 3: Code of ethics. Figure 4: Open in new tabDownload slide Theme 3: Code of ethics. Quantitative phase In this section of the research, we surveyed the attitudes of e-commerce users in relation to their experiences of online disputes and the online dispute resolution process. This phase was conducted to inform the qualitative data results with the perspective of ODR users in e-commerce. Quantitative data collection procedures The survey data in this section of the research was used to collect data about how consumers have experienced problems when shopping for goods and services online. The reason for using Qualtrics,112 an online survey software system, was economy of the design and the rapid distribution of the surveys. The purpose of data collection through conducting a survey is to better understand the qualitative data results, not the generalizability of the results. A pilot study was conducted prior to collecting the survey data, to identify any problems in the instructions or design. A pilot study can indicate whether or not the respondents understand the questions or whether or not there are ambiguous or biased.113 The pilot study was conducted to establish how long it took for participants to complete the survey, whether or not the questions were clear, and if the data collection procedure was correct. The pilot questionnaire was sent to 20 participants by email and 15 were returned. During the pilot study for this phase, we noted that survey participants did not understand the term ‘Online Dispute Resolution’, partly due to its prevalence in academia and uncommon in practice. Since the disputes being considered were ones related to e-commerce, we decided to replace the words ‘Online Dispute Resolution’ with the words ‘Online Complaint Management Systems’. In contrast, pilot study survey participants had no difficulty understanding the term ‘Online Complaint Management Systems’. While complaint management is only a small part of Online Dispute Resolution it is perhaps the major avenue of disputation in the e-commerce area. We hence used the term ‘Online Complaint Management Systems’ in the quantitative survey instrument and attached the following definition to the survey introduction: ‘When consumers buy goods and services online, disputes can arise, and as it is difficult to take the case to court, there are online ways of resolving disputes; for example, consumers and businesses send emails to resolve disputes (online negotiation) or they may agree to have an expert as a neutral to help them resolve the dispute (by video conferencing, emailing, etc). These ways of resolving disputes online are called Online Complaint Management Systems (OCMS)’. Other changes made arising from conducting the pilots study included altering the font size and correcting grammatical mistakes. This pilot study was necessary to improve the questionnaire and confirm its reliability and validity. For ease of use, the participants were selected from business and law students at Victoria University, Melbourne, Australia. Their names and email addresses were collected and surveys were sent to them by Qualtrics.114 The participants received the online survey through an email which included a brief summary about the research project and the importance of their participation. Participants were also informed that participation was voluntary and anonymous. A minimum of 200 individuals were invited to participate in this survey and from which 108 completed surveys were returned. Participants responded to the questions with yes and no answers, multiple-choice responses and a six-point Likert scale (respondents expressed how much they agreed or disagreed by rating a particular statement). After the survey was collected from respondents, the data was coded and statistically analysed using the SPSS software program. The security and privacy of the surveys were assured using digital encryption. Quantitative data analysis Demographic data The survey respondents’ demographic information covers gender, age and educational background. Among the 108 respondents there were 40 (37 per cent) male respondents and 67 (62 per cent) were female respondents, while one of the respondents preferred not to disclose their gender. The ages of the consumers ranged from 18 to more than 65 years. None of the respondents were under 18 years. Most of the participants (37 or 34.4 per cent) had completed an undergraduate degree. A further 18 (16.7 per cent) identified themselves as students at university who had not completed their study. The demographic information of respondents is presented in Table 2. Table 2: Participants’ Demographics All individuals Items Numbers Percentage Gender Male 40 37.0 Female 67 62.0 Prefer not to say 1 0.9 Total number of participants 108 100.0 Age range (years) 18–24 48 44.4 25–34 42 38.9 35–44 12 11.1 45–54 3 2.8 55-64 1 0. 9 65+ 1 0.9 Prefer not to say 1 0.9 Total number of participants 108 100.0 Educational level High school 22 20.4 TAFE or Diploma 11 10.2 Started university student but did not completed 18 16.7 Undergraduate 37 34.3 Post graduate 24 13.0 Other 6 5.6 Total number of participants 108 100.0 All individuals Items Numbers Percentage Gender Male 40 37.0 Female 67 62.0 Prefer not to say 1 0.9 Total number of participants 108 100.0 Age range (years) 18–24 48 44.4 25–34 42 38.9 35–44 12 11.1 45–54 3 2.8 55-64 1 0. 9 65+ 1 0.9 Prefer not to say 1 0.9 Total number of participants 108 100.0 Educational level High school 22 20.4 TAFE or Diploma 11 10.2 Started university student but did not completed 18 16.7 Undergraduate 37 34.3 Post graduate 24 13.0 Other 6 5.6 Total number of participants 108 100.0 Open in new tab Table 2: Participants’ Demographics All individuals Items Numbers Percentage Gender Male 40 37.0 Female 67 62.0 Prefer not to say 1 0.9 Total number of participants 108 100.0 Age range (years) 18–24 48 44.4 25–34 42 38.9 35–44 12 11.1 45–54 3 2.8 55-64 1 0. 9 65+ 1 0.9 Prefer not to say 1 0.9 Total number of participants 108 100.0 Educational level High school 22 20.4 TAFE or Diploma 11 10.2 Started university student but did not completed 18 16.7 Undergraduate 37 34.3 Post graduate 24 13.0 Other 6 5.6 Total number of participants 108 100.0 All individuals Items Numbers Percentage Gender Male 40 37.0 Female 67 62.0 Prefer not to say 1 0.9 Total number of participants 108 100.0 Age range (years) 18–24 48 44.4 25–34 42 38.9 35–44 12 11.1 45–54 3 2.8 55-64 1 0. 9 65+ 1 0.9 Prefer not to say 1 0.9 Total number of participants 108 100.0 Educational level High school 22 20.4 TAFE or Diploma 11 10.2 Started university student but did not completed 18 16.7 Undergraduate 37 34.3 Post graduate 24 13.0 Other 6 5.6 Total number of participants 108 100.0 Open in new tab Descriptive statistics of the variables This part of the survey was critical in exploring consumer’s attitudes as well as their experience and knowledge of ODR and its attributes. In particular, this phase investigate consumer protection in e-commerce and examined the efficiency of ODR systems. As explained above, the pilot study revealed respondents had difficulty understanding the term ODR and therefore the term was replaced by ‘Online Complaint Management System’. The following is the definition we attached to the survey introduction: ‘When consumers buy goods and services online, disputes can arise, and as it is difficult to take the case to court, there are online ways of resolving disputes; for example, consumers and businesses send emails to resolve disputes (online negotiation) or they may agree to have an expert as a neutral to help them resolve the dispute (by video conferencing, emailing, etc). These ways of resolving disputes online are called Online Complaint Management Systems (OCMS)’. Therefore, in this survey OCMS is synonymous with ODR in that both terms have exactly the same meaning and impact in this research. Respondents were asked to rate the importance of OCMS services based on their experiences. The descriptive data statistics of the variables collected from analysing the survey data are presented in this section. Figure 5 represents data gained from respondent’s answers to the statement about trusting public authorities to protect consumer rights. More than half (65 or 60.2 per cent) said that they trust public authorizes and replied ‘Agree’ or ‘Strongly agree’. In contrast, a total of 11 respondents (10.2 per cent), ‘Disagree’ or ‘Strongly disagree’ that public authorizes protect their rights. But 33 (30.6 per cent) were unwilling or unable to answer the question and stated they ‘Neither agree nor disagree’. Figure 5: Open in new tabDownload slide Question of ‘you do trust public authorities to protect your rights as a consumer’. Figure 5: Open in new tabDownload slide Question of ‘you do trust public authorities to protect your rights as a consumer’. Figure 6 shows respondent’s attitudes to the statement that it is easier for them to settle their online purchasing disputes through out of court bodies such as arbitration, mediation or conciliation. A total of 44 respondents (40.7), ‘Agree’ or ‘Strongly agree’ that it would be easier for them to claim their dispute through out of court systems, while 39 (36.1 per cent) ‘Neither agree nor disagree’ and 25 (23.1 per cent) ‘Disagree’ or ‘Strongly disagree’. Figure 6: Open in new tabDownload slide Question of ‘It is easy to settle disputes with retailers/providers through an out of court bodies such as arbitration, mediation, or conciliation’. Figure 6: Open in new tabDownload slide Question of ‘It is easy to settle disputes with retailers/providers through an out of court bodies such as arbitration, mediation, or conciliation’. As described in Figure 7, respondents were asked to rate whether or not it was easy to trust the OCMS process. Half the respondents (50 per cent) ‘Neither agree nor disagree’, while total of 23 (29.4 per cent) ‘Agree’ or ‘Strongly agree’ that it was hard to trust the OCMS procedure and 15 (19.2 per cent) ‘Disagree’ or ‘Strongly disagree’ as they found it easy to trust OCMS process. Figure 7: Open in new tabDownload slide Question of ‘Rate the following statement: It is not easy to trust Online Complaint Management System (OCMS) process’. Figure 7: Open in new tabDownload slide Question of ‘Rate the following statement: It is not easy to trust Online Complaint Management System (OCMS) process’. Descriptive statistics were used to present the findings from the quantitative data analysis. All the graphs and tables showed there was minimal discontent with OCMS systems, less than 10 per cent for each question. The next section includes a full discussion and interpretation of both the qualitative and quantitative findings. Findings and discussion The interview and survey data presented in ‘Literature review’ section. (Phase 1: qualitative data analysis) and ‘Research methodology’ section. (Phase 2: quantitative data analysis) were used to answer the research question examined in this study. By referring to the qualitative research findings, the three themes of knowledge, expectations of fairness and availability of a code of ethics were deemed as significant in measuring trust in ODR systems. These elements are shown in Figure 8. The findings of the quantitative survey indicate that consumers trust public authorities to protect their rights. They also consider it is easier to resolve disputes through out of court systems. It also supported consumers are less likely to trust ODR processes because of lack of transparency and adequate information about the process. Figure 8: Open in new tabDownload slide Trust elements in ODR. Figure 8: Open in new tabDownload slide Trust elements in ODR. Each of these elements is fully discussed in the following sections. Knowledge One of the main elements that contribute to evaluation of trust in ODR systems is the existence of knowledge about the process. It is important that individuals have adequate information and knowledge about ODR systems, in order to trust them. Moreover, there is a strong relationship between reputation of government authorities and trust. Therefore, a well-designed ODR platform should provide knowledge for individuals. This could occur in two ways: Reputation and endorsement by official bodies: for example, ODR providers could create a strong reputation by using feedback systems and review forums, gain endorsement by recognized law firms or government bodies and by presenting official logos on their website. Transparency of the procedure: ODR providers should offer a full road map of the process for users, including how their system works, how long the process will take, what are the steps in the ODR process and what are the possible outcomes. This transparency could be achieved by a demonstration of virtual cases such as: anonymizing cases or mixing cases to find similar cases and taking the common procedure and creating data sets for transparency. Referring to the ADR literature, researchers such as Jethro K Lieberman and James F Henry115 have mentioned that in designing ADR systems trust is necessary; as the role of ADR is to enhance trust among individuals, which relates to information about how the ADR system works. This is similar with ODR, where transparency of the procedure for creating user trust in ODR is essential. Moreover, researchers in the e-commerce and ODR field confirm the effect of individual’s information about online systems on trust. There are different ways of transferring ODR knowledge to individuals, as discussed by Noam Ebner,116 Pablo Cortes,117 Gabrielle K Kohler and Thomas Schultz,118 Chin Eang Ong,119 Susan S Raines,120 Colin Rule,121 Colin Rule and Larry Friedberg122 and Ben Shneiderman.123 They mention, for example, reputation and accreditation by reliable institutions, feedback mechanisms and transparency of the procedure, and providing information about ODR which indicates ODR is the most effective way of resolving online disputes. In addition, a trustworthy company is one which is impartial with users, not a company which does not experience any dispute or problem with its users.124 Indeed, a pattern of predictable behaviour is part of trust. The important role of information gained about ODR systems in creating trust has been confirmed by different researchers. However, none of them has used the term knowledge and its components, including reputation and transparency. When parties have sufficient knowledge about how trustable and how convenient ODR systems are, then they will most likely enter into the process with a high level of confidence. Figure 9 highlights the relationship of knowledge elements. Figure 9: Open in new tabDownload slide Knowledge elements. Figure 9: Open in new tabDownload slide Knowledge elements. Expectations of fairness An expectation of fairness in ODR systems is another criterion identified in this research as creating trust in ODR mechanisms. This element means that individuals in any ODR process expect some level of fairness that makes them trust the system, including informing them about their rights, providing them with correct and trustable data about the ODR process and enhancing trust in decision makers. In an ODR mechanism, an expectation of fairness is obtained by: Confidentiality of personal data; Integrity and honesty of neutrals such as mediators; The existence of biographies and identifying images which establish parties' confidence and familiarity with each other and neutrals; Consistency of outcomes; and Simple and accessible redress procedures. The transparency of the ODR process will enhance individuals’ knowledge about the fairness of ODR systems. Disputants expect to receive correct information about the process. The literature, including Sidney I Landau125 mentions that individual expectations will shape trust in justice. Some researchers consider that what individuals expect from justice systems shapes their trust. It has been argued by Pablo Cortes126 that individuals who resolved their dispute through ODR services have more trust in these systems, as they expect that if a dispute happens again it will be resolved in a consistent way. Also, Chin Eang Ong127 asserts that parties expect to enter into a simple and accessible process. Moreover, Susan S Raines128 emphasizes the relationship that exists between parties’ perceptions about fairness depending on their high level of confidence and familiarity with each other and trust in ODR. Figure 10 highlights the relationship of expectations of the fairness elements. Figure 10: Open in new tabDownload slide Expectations of fairness element. Figure 10: Open in new tabDownload slide Expectations of fairness element. Consequently, an expectation of fairness is vital for an individual’s expectations in creating trust. However, none of these researchers have mentioned expectations of fairness directly in this element as a necessary component to measure trust in ODR systems. ODR providers need to fulfill parties’ expectations of fairness to have a trustable system. Code of ethics The third significant element recognized in this research to measure trust in ODR systems is the presence of a code of ethics. The reason for the importance of a code of ethics in ODR systems is that its existence will help individuals feel confident and trust that the neutrals and decision makers are working professionally without any biased behaviour. Moreover, the existence of such elements not only enhances trust but also increases fairness in ODR systems. Therefore, a code of ethics in ODR systems includes an official certification for neutrals and decision makers to ensure their impartiality and professional competence. Figure 11 highlights the relationship of the code of ethics elements. Figure 11: Open in new tabDownload slide Code of ethics element. Figure 11: Open in new tabDownload slide Code of ethics element. The existing literature in the ADR and ODR field, such as the work of Laurence Boulle et al,129 Sai On Cheung and Kenneth TW Yiu Yiu130, Todd H Chiles and John F McMackin,131 Robert F. Cochran,132 Ellen E. Deason,133 Tak Wing Yiu and Wai Ying Lai134 have considered significant aspects of a mediator’s role is establishing trust in the procedure between disputants and neutrals. Adam Furlan Gislason135 argues that the existence of a code of ethics for neutrals’ trustworthy behavior increases trust in ADR. This research has recognized that the existence of a code of ethics in ODR mechanisms as necessary to create some level of trust for users. Research implications and limitations This study has added empirical evidence to an important gap in the literature in the ODR field. The practical and methodological implications and limitations of this study are discussed in the following sub-sections. Practical implications The findings of this research have the following practical implications for: ODR providers, e-commerce companies and consumer organizations. ODR providers: Elements of trust can be implemented by ODR providers in a new dispute resolution framework which is internationally accepted. One of the advantages of this framework is that it encompasses the attitudes of both ODR providers and consumers and therefore meets as many of their needs and interests of as possible. The existence of such an ODR framework will invoke trust among users if all ODR providers are consistent in achieving a fair outcome because they all follow certain globally accepted conditions and rules. E-commerce companies: The practical implication of this research for E-commerce companies is that they should work closely with ODR providers to implement efficient online resolution systems that will promote the online market. When consumers consider they are protected in their online transactions their communication, whether it is negative or positive with e-commerce holders, will be enhanced. Consumer organizations: The concept of consumer protection online is very important. The implications of the findings of this research on consumer organizations are that consumers trust consumer organizations to protect their rights. It is their duty to provide consumers with adequate information and knowledge about their legal rights when purchasing products online and about the existence of online redress mechanisms’ namely ODR systems. Methodological implications This study adopted a mixed-methods exploratory approach in which quantitative findings from face-to-face interviews were supported by quantitative findings from the perspective of ODR users. This has provided for first time a new empirical approach in the ODR field. Although conducting qualitative face-to-face interviews regarding ODR systems can be difficult because ODR providers are located all over the world, a carefully well planned discussion though a face-to-face interview could help researchers to better understand how ODR systems work and how to increase ODR effectiveness. In this study, face-to-face interviews with ODR providers guided the researchers into significant themes and findings that were supported by survey data collected from consumers who experienced online purchasing disputes. Limitations This study included several limitations. The sample size and nature of the participants in the phase 2 of this study impacted on the generalizability of the quantitative findings. The total of the 108 respondents in this study do not represent the population of global consumers who purchase online. However, the purpose of the quantitative survey data collection was to better understand the qualitative data results, not the generalizability of the results. Whilst this study has been able to make significant findings by taking into account the perspectives of ODR developers and users, a major limitation for this study was the inability to fully explore the e-commerce companies’ attitudes about trust in ODR systems. This should be considered the focus of future studies Conclusion The growth of e-commerce has necessitated suitably developed ODR systems which can resolve online disputes cheaply and easily. In this article, we investigate how trust is embedded in ODR systems and in doing so, we develop a set of standards which may help to enhance trust and confidence in ODR. We conducted interviews with several ODR developers and supplemented these substantive findings with the perspectives of ODR users to reveal a new approach on how to embed trust in ODR. We have found knowledge by way of transparent ODR processes and the importance of ODR provider reputation as strong indicators of assurances of trust in the system’s outcomes. There is also an expectation of fairness which can be provided by the ODR system if users find ODR information is trustable, that ODR advice and decisions are reflective of a user’s expectation and that users are adequately informed of their rights. Lastly, a code of ethics to ensure the neutrality of the ODR is also an important consideration for trust in ODR. Our findings may inform the development of an ODR global standard for incorporating trust into ODR systems. If all ODR developers develop their systems according to a global standard, users will be more trusting of the system and its advice or decisions; ODR developers can be confident their products are providing a fair service and consumer organizations using ODR for their dispute resolution processes can be assured their customers will resolve disputes fairly and without additional and expensive recourse. As ODR systems will continue to evolve, it is our hope that the next iterations of ODR systems will consider embedding trust as a foundational tenant of ODR systems. Footnotes 1 Huong Ha and Sue LT McGregor, ‘Role of Consumer Associations in the Governance of E-commerce Consumer Protection’ (2013) 12(1) Journal of Internet Commerce 1. 2 Rania Nemat, ‘Taking a Look at Different Types of E-Commerce’ (2011) 1(2) World Applied Programming 100. 3 Del Duca, Colin Rule and Zbynek Loebl, ‘Facilitating Expansion of Cross-Border E-Commerce-Developing a Global Online Dispute Resolution System (Lessons derived from existing ODR systems-work of the United Nations Commission on International trade law)’ (2012) 1 Penn State Journal of Law and International Affairs 59. 4 Faye Fangfei Wang, Online Dispute Resolution: Technology, Management and Legal Practice from an International Perspective (Chandos Publishing 2009). 5 Rex E Lee, ‘The Profession Looks at Itself–The Pound Conference of 1976’ (1981) 1981 Brigham Young University Law Review 737. 6 Roger Fisher, L William Ury and Bruce Patton, ‘Getting to YES: Negotiating Agreement Without Giving In’ (2011) 185 Penguin 3. 7 L. Thorne McCarty, ‘Reflections on Taxman: An Experiment in Artificial Intelligence and Legal Reasoning’ (1976) 90 Harvard Law Review 837. 8 Phillip Capper and Richard E Susskind, Latent Damage Law: The Expert System (Butterworths1988). 9 Colin Rule and Chittu Nagarajan, ‘Leveraging the Wisdom of the Crowds: The Ebay Community Court and the Future of Online Dispute Resolution’ (2010) 2(2) ACResolution 7. 10 Michael Legg, ‘Online Alternative Dispute Resolution’ (2017) 141 Precedent 32. 11 Shannon Salter and Darin Thompson, ‘Public-Centered Civil Justice Redesign: A Case Study of the British Columbia Civil Resolution Tribunal’ (2016) 3 McGill Journal of Dispute Resolution 113. 12 Ethan Katsh and Orna Rabinovich-Einy, Digital Justice: Technology and the Internet of Disputes (Oxford University Press 2017). 13 Raymond H Brescia, Alexandria Decatur and Julia Kosineski, ‘Civil Society and Civil Justice: Teaching with Technology to Help Close the Justice Gap for Non-Profit Organizations’ (2019) 29 Albany Law Journal of Science and Technology 29. 14 Civil Resolution Tribunal <https://civilresolutionbc.ca/how-the-crt-works/getting-started/viewed> accessed 3 March 2019. 15 Elinor Ostrom and James Walker, Trust and Reciprocity: Interdisciplinary Lessons for Experimental Research (Russell Sage Foundation 2003). 16 Amy J Schmitz, ‘Organic Online Dispute Resolution: Resolving Cramming Claims as an Example’ (2013) 32(9) Banking & Financial Services Policy Report 1. 17 See (n 12). 18 Elizabeth Chang, Farookh Hussain and Tharam Dillon, Trust and Reputation for Service-Oriented Environments: Technologies for Building Business Intelligence and Consumer Confidence (John Wiley & Sons 2006). 19 See (n 3). 20 Noam Ebner, ‘ODR and Interpersonal Trust’ in Mohamed S Abdel Wahab, Ethan Katsh and Daniel Rainey (eds), ODR: Theory and Practice (Eleven International Publishing 2012). 21 Julia Hornle, ‘Encouraging Online Dispute Resolution in the EU and Beyond-Keeping Costs Low or Standards High?’ (2012) 122 Legal Studies Research Paper 1. 22 Camile Pecnard, ‘The Issue of Security in ODR’ (2004) 7(1) ADR Bulletin 1. 23 Chin Eang Ong, ‘The Types of Redress Procedures in Business-to-Consumer (B2C) E-Commerce’ (Proceedings of 21st Americas Conference on Information Systems, Puerto Rico, 2015). 24 John Creswell and Vicki L Plano Clark, Designing and Conducting Mixed Methods Research (Sage Publications 2007). 25 Robert T Golembiewski and Mark McConkie, ‘The Centrality of Interpersonal Trust in Group Processes’ (1975) 131 Theories of Group Processes 185. 26 Harrison McKnight, Vivek Choudhury and Charles Kacmar, ‘Developing and Validating Trust Measures for E-Commerce: An Integrative Typology’ (2002) 13(3) Information Systems Research 334. 27 Roger Mayer, James H Davis and David Schoorman, ‘An Integrative Model of Organizational Trust’ (1995) 20(3) Academy Management Review 709. 28 David Lewis and Andrew Weigert, ‘Trust as a Social Reality’ (1985) 63(4) Social Forces 967. 29 See (n 18). 30 Jeffry A Simpson, ‘Psychological Foundations of Trust’ (2007) 16(5) Current Directions in Psychological Science 264. 31 See (n 28). 32 See (n 27) 712. 33 See (n 27). 34 Sidney I Landau, Funk & Wagnalls Standard Desk Dictionary (Harper & Row Publishers 1984). 35 ibid 14. 36 Denise M Rousseau, Sim B Sitkin, Ronald S Burt and Colin Camerer, ‘Not So Different After All: A Cross-Discipline View of Trust’ (1998) 23(3) Academy of Management Review 393. 37 ibid. 38 Harrison D McKnight, ‘Trust in Information Technology’ (2005) 7 The Blackwell Encyclopedia of Management 329. 39 Rocco Elena, ‘Trust Breaks Down In Electronic Contexts But Can Be Repaired By Some Initial Face-To-Face Contact. (Proceedings of the SIGCHI Conference On Human Factors in Computing Systems 1998). 40 See (n 38). 41 See (n 36). 42 Jessica Santos, ‘E-Service Quality: A Model of Virtual Service Quality Dimensions’ (2003) 13(3) Managing Service Quality: An International Journal 233. 43 William H Delone and Ephraim R McLean, ‘The Delone and Mclean Model of Information Systems Success: A Ten-Year Update’ (2003) 19(4) Journal of Management Information Systems 9. 44 Kathryn M Kimery and Mary McCord, ‘Third-Party Assurances: Mapping the Road to Trust in E-Retailing’ (2002) 4(2) Journal of Information Technology Theory and Application 63. 45 Christian Gronroos, Fredrik Heinonen, Kristina Isoniemi and Michael Lindholm, ‘The Net Offer Model: A Case Example from the Virtual Marketspace’ (2000) 38(4) Management Decision 243. 46 Sirkka L. Jarvenpaa, Noam Tractinsky and Lauri Saarinen, ‘Consumer Trust in an Internet Store: A Cross‐Cultural Validation’ (1999) 5(2) Journal of Computer-Mediated Communication 1. 47 ibid. 48 Patricia M Doney, Joseph P Cannon and Michael R Mullen, ‘Understanding the Influence of National Culture on the Development of Trust’ (1998) 23(3) Academy of Management Review 601. 49 Chih-Chien Wang, Chun An Chen and Jui Chin Jiang, ‘The Impact of Knowledge and Trust on E-Consumers' Online Shopping Activities: An Empirical Study’ (2009) 4(1) Journal of Computers 11. 50 Jui C Jiang, Chun A. Chen and Chih C Wang, ‘Knowledge and Trust in E-Consumers Online Shopping Behavior’ (2008) Electronic Commerce and Security International Symposium 652. 51 Beverley Sparks and and Victoria Browning, ‘The Impact of Online Reviews on Hotel Booking Intentions and Perception of Trust’ (2011) 32(6) Tourism Management 1310. 52 Baljeet Kaur and Sushila Madan, ‘Factors Influencing Trust in Online Shopping: An Indian Consumer’s Perspective’ (2013) 5(29) European Journal of Business and Management 132. 53 Salam Al F, Lakshmi Iyer, Prashant Palvia and Rahul Singh, ‘Trust in E-commerce’ (2005) 48(2) Communications of the ACM 72. 54 Sonja Grabner-Kraeuter, ‘The Role of Consumers’ Trust in Online-Shopping’ (2002) 39(1) Journal of Business Ethics 43. 55 Charles E Naquin and Gaylen D Paulson, ‘Online Bargaining and Interpersonal Trust’ (2003) 88(1) Journal of Applied Psychology 113. 56 Colin Rule and Larry Friedberg, ‘The Appropriate Role of Dispute Resolution in Building Trust Online’ (2005) 13(2) Artificial Intelligence and Law 193. 57 Jean R Sternlight, ‘ADR is Here: Preliminary Reflections on Where it Fits in a System of Justice’ (2002) 3 Nevada Law Journal 289. 58 Jethro K Lieberman and James F Henry, ‘Lessons from the Alternative Dispute Resolution Movement’ (1986) 53(2) University of Chicago Law Review 424. 59 ibid. 60 Adam Furlan Gislason, ‘Demystifying ADR Neutral Regulation in Minnesota: The Need for Uniformity and Public Trust in the Twenty-First Century ADR System’ (1998) 83 Minnesota Law Review 1839. 61 Noam Ebner and John Zeleznikow, ‘No Sheriff in Town: Governance for Online Dispute Resolution’ (2016) 32(4) Negotiation Journal 297. 62 Ellen E Deason, ‘Need for Trust as a Justification for Confidentiality in Mediation: A Cross-Disciplinary Approach’ (2005) 54 University of Kansas Law Review 1387. 63 Robert F Cochran, ‘ADR, the ABA, and Client Control: A Proposal that the Model Rules Require Lawyers to Present ADR Options To Clients’ (1999) 41 South Texas Law Review 183. 64 Joseph B Stulberg, ‘Theory and Practice of Mediation: A Reply to Professor Susskind’ (1981) 6 Vermont Law Review 85. 65 Sai On Cheung and Kenneth TW Yiu, ‘A Study of Construction Mediator Tactics—Part I: Taxonomies Of Dispute Sources, Mediator Tactics And Mediation Outcomes’ (2007) 42(2) Building and Environment 752. 66 Laurence Boulle, Micheal T Colatrella and Anthony P Picchioni, Mediation: Skills and Techniques (LexisNexis, 2008). 67 Todd H Chiles and John F McMackin, ‘Integrating Variable Risk Preferences, Trust, and Transaction Cost Economics’ (1996) 21(1) Academy of Management Review 73. 68 See (n 58). 69 Noam Ebner and John Zeleznikow, ‘Fairness, Trust and Security in Online Dispute Resolution’ (2015) 36 Hamline Journal of Public Law and Policy vi. 70 See (n 22). 71 See (n 15). 72 Susan S Raines, ‘Mediating in Your Pajamas: the Benefits and Challenges for ODR Practitioners’ (2006) 23(3) Conflict Resolution Quarterly 359. 73 See (n 56). 74 See (n 72). 75 See (n 3). 76 John Zeleznikow and Emilia Bellucci, ‘Legal Fairness in ADR Processes Implications for Research and Teaching’ (2012) 23(4) Australasian Dispute Resolution Journal 256. 77 See (n 72). 78 Thomas Schulz, ‘Does Online Dispute Resolution Need Governmental Intervention-The Case for Architectures of Control and Trust’ (2004) 6 NCJL & Tech 71. 79 Gabrielle Kaufmann-Kohler and Thomas Schultz, Online Dispute Resolution: Challenges for Contemporary Justice (Kluwer Law International 2004). 80 Pablo Cortes, Online Dispute Resolution for Consumers in the European Union (Routledge 2011). 81 See (n 78). 82 See (n 79). 83 Ben Shneiderman, ‘Designing Trust into Online Experiences’ (2000) 43(12) Communications of the ACM 57. 84 See (n 59). 85 Roy J. Lewicki and Carolyn Wiethoff, ‘Trust, Trust Development, and Trust Repair’ in Morton Deutsch, Peter Coleman and Eric Marcus (eds), The Handbook of Conflict Resolution: Theory and Practice (Wiley Publishing 2006). 86 See (n 72). 87 Nancy H Rogers and Robert C Bordone, Designing Systems and Processes for Managing Disputes (Wolters Kluwer Law & Business 2014). 88 Pablo Cortes, ‘Online Dispute Resolution Services: A Selected Number of Case Studies’ (2014) 20(6) Computer Tele communion Law Rev 172. 89 Colin Rule, ‘Quantifying the Economic Benefits of Effective Redress: Large E-Commerce Data Sets and the Costbenefit Case for Investing in Dispute Resolution’ (2012) 34(4) University of Arkansas at Little Rock Law Review 767. 90 Chin Eang Ong and Caroline Chan, ‘How Complaint Handling Procedures Influence Consumer Decisions to Shop Online?’ (Proceedings of the 27th Bled eConference eEcosystems,, Slovenia 2014). 91 Pablo Cortes, ‘A New Regulatory Framework for Extra‐Judicial Consumer Redress: Where We Are and How to Move Forward’ (2015) 35(1) Legal Studies 114. 92 See (n 90). 93 See (n 89). 94 See (n 90). 95 Simon James Boehme, ‘Skeptics of The Screen: Irish Perceptions of Online Dispute Resolution’ (Master thesis, Maynooth University 2015). 96 Thomas V Bonoma, ‘Case Research in Marketing: Opportunities, Problems, and A Process’ (1985) 22 Journal of Marketing Research 199. 97 See (n 24). 98 Omar Gelo, Diana Braakmann and Gerhard Benetka, ‘Quantitative and Qualitative Research: Beyond The Debate’ (2008) 42(3) Integrative Psychological and Behavioral Science 266. 99 John Creswell, The Selection of a Research Design: Qualitative, Quantitative and Mixed Methods Approaches (Sage Publications 2009). 100 See (n 24). 101 Janice Morse and Lyn Richards, Read Me First For a User’s Guide to Qualitative Research (Sage Publications Thousand Oaks 2002). 102 ibid. 103 See (n 24). 104 Mariwilda Padilla-Díaz, ‘Phenomenology in Educational Qualitative Research: Philosophy as Science or Philosophical Science?’ 2015 (1)2 Journal of Educational Excellence 101. 105 Jane Ritchie, Jane Lewis, Carol McNaughton Nicholls and Rachel Ormston, Qualitative Research Practice: A Guide for Social Science Students and Researchers (Sage Publications 2013). 106 Magnus Englander, ‘The Interview: Data Collection in Descriptive Phenomenological Human Scientific Research’ (2012) 43(1) Journal of Phenomenological Psychology 13. 107 John W Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (Sage Publications 2013). 108 Sharan B Merriam and Elizabeth J Tisdell, Qualitative Research: A Guide to Design and Implementation (John Wiley & Sons 2015). 109 Lois Phillips-Pula, Julie Strunk and Rita H Pickler, ‘Understanding Phenomenological Approaches to Data Analysis’ (2011) 25(1) Journal of Pediatric Health Care 67. 110 Clark Moustakas, Phenomenological Research Methods (Sage Publications 1994). 111 Paul F Colaizzi, ‘Psychological Research as the Phenomenologist Views It’ in Ronald S Vaile and Mark King (eds), Existential Phenomenological Alternatives for Psychology (Oxford University Press 1978). 112 Jonathan Hill, Cross-border Consumer Contracts (OUP 2008). 113 Uma Sekaran and Bougie Roger, Research Methods for Business: A Skill Building Approach (John Wiley & Sons 2016). 114 ibid. 115 See (n 58). 116 See (n 20). 117 See (n 80). 118 See (n 79). 119 See (n 23). 120 See (n 72). 121 See (n 89). 122 See (n 56). 123 See (n 83). 124 Bruce Winick, ‘Therapeutic Jurisprudence and the Role of Counsel in Litigation’ (2000) 37 California Western Law Review 105. 125 See (n 34). 126 See (n 88). 127 See (n 23). 128 See (n 72). 129 See (n 66). 130 See (n 65). 131 See (n 67). 132 See (n 63). 133 See (n 62). 134 Tak Wing Yiu and Wai Ying Lai, ‘Efficacy of Trust-Building Tactics in Construction Mediation’ (2009) 135(8) Journal of Construction Engineering and Management 683. 135 See (n 48). © The Author(s) (2019). 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TI - Universal standards for the concept of trust in online dispute resolution systems in e-commerce disputes
JF - International Journal of Law and Information Technology
DO - 10.1093/ijlit/eaz005
DA - 2019-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/universal-standards-for-the-concept-of-trust-in-online-dispute-o26Lzf48sU
SP - 209
VL - 27
IS - 3
DP - DeepDyve
ER -