Artificial General Intelligence (AGI) can understand, learn, and apply knowledge across a range of tasks, in a similar way to humans. It is not limited to specific tasks. In e-commerce, AGI is revolutionizing the industry by enabling highly personalized shopping experiences, intelligent customer service, and efficient supply chain management. However, it also raises concerns about job losses and invasion of privacy (Cheng et al. 2025). Autonomous decision-making also has ethical implications.
Figure 1. Future trends in AGI research
In supply chain and operations management, AGI-driven tools like Deepseek can forecast demand better than before, coordinate logistics, and solve problems independently in real time. When organizations learn how to use these tools, they can adopt them faster and improve the overall supply chain performance. Alibaba’s smart warehouse demonstrates how AGI uses data, algorithms, and robots to automate inventory management, reduce errors, and enhance labour productivity through the collaboration between AI and human expertise. In marketing, AGI enables highly personalized strategies by creating content that resonates emotionally with customers. AI-generated virtual influencers with emotional expressions, such as happiness or surprise, can significantly increase user engagement, especially when combined with visually appealing content. AGI also excels at creating ads with agentic appeals, like messages focusing on efficiency. Consumers prefer these ads because they enhance the sense of self-efficacy in completing tasks. However, for ads that require emotional storytelling, human-AI collaboration remains crucial. Generative AI can also deliver highly personalized marketing content, outperforming traditional digital tools in terms of relevance and efficiency. In customer service, AGI-powered digital assistants build trust and encourage purchase intent by using anthropomorphic features. Computers become social actors. By improving response speed and problem-solving accuracy, AI chatbots can improve customer satisfaction and loyalty. Their effectiveness depends on balancing technical capabilities with human-like interactions. In industries such as hospitality and tourism, AGI tools like Deepseek can personalize recommendations and simplify backend operations, showing their adaptability across different sectors. A bibliometric review indicates that e-commerce AI research has long focused on recommendation systems, sentiment analysis, and personalization. Now, AGI is expected to integrate these areas into a cohesive, autonomous ecosystem. Collaborative AI frameworks emphasize combining AGI’s mechanical and thinking intelligence with human marketers’ intelligence. This allows humans to focus on strategic and emotional tasks while AGI automates routine processes.
Reference Cheng, X., Mou, J., Wang, Y., & Zarifis, A. (2025) ‘Development of AGI in e-commerce’, Journal of Electronic Commerce Research, vol.26, no.3, pp.163-169. http://www.jecr.org/node/737
When we think of great leaders, we turn to famous leaders from history for inspiration, but they did not have to deal with unpredictable disruption AI is causing. The modern leader must not only lead humans, but also autonomous AI agents. They must also guide the organization through the process of adapting to fully utilize AI across all the operations. There is no simple answer to this challenge, but there is a structured approach with six steps that will increase the chances of success.
Figure 1. The steps to being a great leader in the age of AI
Step 1: Learn the three most effective leadership styles
The first step is to learn the three most effective leadership styles and understand the benefits of combining them in various ways. These are servant, transactional and transformational.
Step 2: Learn the typical stages of a project
The modern leader must constantly integrate the latest versions of AI so their role becomes similar to that of a project manager implementing a series of digital transformation projects. Typically, a project has six stages that are forming, storming, norming, performing, adjourning and post-project collaboration.
Step 3: Evaluate the context
While we are fascinated by the capabilities of AI, the role of the context the leader finds themselves in must not be underestimated. The leadership approach must consider the influence of the context on the people and the technology.
Step 4: Choose a business model and a leadership style
The leader needs to think about whether to focus on one of the three leadership styles or combine two of them to get the best out of the situation they are in.
Choosing a proven AI centred business model will offer clarity. There are six proven AI focused business models: first: incumbent focusing on one part of the value chain and disaggregating, second: incumbent absorbing AI into existing model, third: incumbent expanding beyond current model to fully utilise the opportunities of AI and access new data, fourth: startup disruptor focused on one sector, built from the start to be highly automated, fifth: disruptor focused on tech adding a new service such as insurance, and lastly the sixth model is a disruptor that is not tech-focused but has an extensive userbase.
Step 5: Build trust with a clear vision of what the role of AI is
To lead autonomous AI agents, the leader must build trust in them among the team. The leader must be clear on their use and build a consensus around this. The team must be put on a sustainable trajectory for change.
Step 6: Decide what to do at each stage of the project
Effective leadership today must involve leading on technology as well as people, building trust in the technology, and finding the best combination of leadership styles to get the most out of humans and automated AI agents. These many tasks cannot all be done at once so the leader must have a plan of what they will focus on at each stage.
The steps touched on here are covered more thoroughly in the book. If you want to learn more about Leadership in AI with trust you can buy my book from all good bookshops.
Fintech companies face the challenge of trying to lead in AI adoption while navigating potential pitfalls. The board of directors plays a critical role in demonstrating leadership and building trust with key stakeholders during the implementation of AI.
This research interviewed board members from Fintech companies to identify the most effective strategies for fostering trust among shareholders, staff, and customers. These three groups have different concerns and face different risks from AI. The findings reveal that the most effective methods for building trust differ among these three groups of stakeholders. Leaders should build trust for these three stakeholders in two ways: First, through the effective and trustworthy implementation of AI, and second, by transparently communicating how AI is used in a manner that addresses stakeholders concerns. The practical ways to build trust with the implementation and the communication for these three groups, shareholders, staff, and consumers, are presented in tables 1-3.
The findings show significant overlap between the effective overall implementation and governance of AI. However, several issues are identified that relate specifically to how AI innovations should be communicated to build trust. The findings also indicate that certain applications of Generative AI are more conducive to building trust in AI, even if they are more restrained and limited in scope, and some of Generative AI’s performance may be sacrificed as a result. Thus, there are trade-offs between unleashing Generative AI in all its capacity and a more constrained, transparent, and predictable application that builds trust in customers, staff, and shareholders. This balancing act, between a fast adoption of Generative AI and a more cautious, controlled approach is at the heart of the challenge the board faces.
Leaders and corporate boards must build trust by providing a suitable strategy and an effective implementation, while maintaining a healthy level of scepticism based on an understanding of AI’s limitations. This balance will lead to more stable and sustainable trust.
Table 1. How leaders can build trust in AI with shareholders
Implementation: 1) Use AI in a way that does not increase financial or other risks. 2) Build in-house expertise, don’t rely on one consultant or technology provider. 3) Make new committee focused on the governance of AI and data. Accurately evaluate new risks (compliance etc.). 4) Develop a framework of AI risk that board will use to evaluate and communicate risks from AI implementations. Management should regularly update the framework. 5) Renew board and bring in more technical knowledge and have sufficient competence in AI. Keep up with developments in technology. Ensure all board members understand how Generative AI and traditional AI work. 6) Make the right strategic decisions, and collaboration, for the necessary technology and data (e.g. through APIs etc.).
Communication: 1) Clear vision on AI use. Illustrate sound business judgement. Showcase the organization’s AI talent. 2) Clear boundaries on what AI does and does not do. Show willingness to enforce these. 3) Illustrate an ability to follow developments: Show similar cases of AI use from competitors, or companies in other areas. 4) If trust is concentrated on specific leaders that will have a smaller influence with the increased use of AI, the trust lost must be re-built. 5) Be transparent about AI risks so shareholders can also evaluate them as accurately as possible.
Table 2. How leaders can build trust in AI with staff
Implementation: 1) Show long term financial commitment to AI initiatives. 2) Encourage mindset of experimentation but with an awareness of the risks such as privacy, data protection laws and ethical behaviour. 3) Involve staff in process of digital transformation. Share new progress and new insights gained to illuminate the way forward. 4) Make AI ethics committee with staff from a variety of seniorities. 5) Give existing staff the necessary skills to effectively utilize Generative AI, rather than hiring new people with technological knowledge that do not know the business. Educate staff on when to not follow, and when to challenge the findings of AI. 6) Key performance indicators (KPIs) need to be adjusted. Some tasks become easier with AI, but the process of digital transformation is time consuming.
Communication: 1) Communicate a clear coherent, long-term vision, with a clear role for staff. The steps towards that vision should reflect the technological changes, business model changes, and the changes in their roles. 2) Be open and supportive to staff reporting problems, so whistleblowing is avoided.
Table 3. How leaders can build trust in AI with customers
Implementation: 1) Avoid using unsupervised Generative AI to complete tasks on its own. 2) Only use AI with clear transparent processes, and predictable outcomes, to complete tasks on its own. 3) Have clear guidelines on how staff can utilize Generative AI, covering what manual checks they should make. 4) Monitor competition and don’t fall behind in how trust in AI is built.
Communication: 1) Explain where Generative AI and other AI are used and how. 2) Emphasise the values and ethics of the organization and how they still apply when Generative AI, or other AI, is used.
The authors thank the Institute of Corporate Directors Malaysia for their support, and for featuring this research: https://pulse.icdm.com.my/article/how-leadership-in-financial-organisations-build-trust-in-ai-lessons-from-boards-of-directors-in-fintech-in-malaysia/
References
Zarifis A. & Yarovaya L. (2025) ‘Building Trust in AI: Leadership Insights from Malaysian Fintech Boards’ In Zarifis A. & Cheng X. (eds.) Fintech and the Emerging Ecosystems – Exploring Centralised and Decentralised Financial Technologies, Springer: Cham. https://doi.org/10.1007/978-3-031-83402-8_15 (open access)
Decentralized finance (DeFi) is becoming more and more popular. Decentralized exchanges (DEX) are a type of DeFi that allow users to trade freely and anonymously on a blockchain. There are two platforms for cryptocurrency trading, centralized exchange (CEX) and DEX. Most of the exchange volume is happening at CEX because they are easier to use. However, DEXs volume is catching up. The reasons for the popularity of DEXs are linked to the unique advantages of DeFi. First, CEX is a business that requires Know Your Customer (KYC). It requires customers’ identification for registration to comply with anti-money laundering regulations and any other laws from the countries its customers come from. Because of this, users are still concerned about several issues such as privacy and the risk of their wealth being confiscated. This study focuses on how external factors affect the adoption of DEXs and provides suggestions for the development of the entire DEX industry. The study finds that the expansion of the DeFi industry has a significant impact on the adoption of DEXs, so DEXs should cooperate to develop the industry instead of focusing on competing for a larger share of the existing market. Some other external factors also have an impact, such as technological innovation, partner integration and community support. As DeFi is closely related to the adoption of DEX, the study also discusses the external factors that may affect the adoption of DeFi, namely trust, infrastructure, and regulation.
Figure 1. The factors that affect the adoption of Decentralised Exchanges DEX
Related research is mainly focused on the difference between DEXs, and tries to find how a DEX can gain an advantage over other DEXs, but this research concentrates on the external factors to show how to encourage DEX adoption. While market fluctuations may attract short-term attention and reactions, the long-term development of DEX relies on a stable user base in DeFi and continued expansion of the industry. This research enriches the theoretical framework of DeFi and sheds light on the relationship between DeFi and DEX. A DEX relies on the liquidity of DeFi, with higher liquidity on the blockchain, the DEX liquidity is expected to be higher, leading to slippage, which means the loss that users suffer when trading on DEX, is reduced. This research provides new insights into understanding the liquidity dynamics of DEXs, extending existing theory on the interplay between liquidity and transaction costs.
References Zarifis A. & Yao Y. (2025) ‘External factors that affect the adoption of decentralized exchanges (DEX) for cryptocurrencies: The case of Curve DEX’ In Zarifis A. & Cheng X. (eds.) Fintech and the Emerging Ecosystems – Exploring Centralised and Decentralised Financial Technologies, Springer: Cham. https://doi.org/10.1007/978-3-031-83402-8_7
(chapter 4 in book) Central bank digital currencies (CBDC) have been implemented by some countries and trialled by many more. As the name suggests, the fundamental characteristics are that this is money that is digital, without a physical note or coin, and issued by a central bank. The consumer has an increasing range of financial services to choose from including decentralised blockchain based cryptocurrencies. A CBDC may use blockchain technology, but it is centralized, so the institutions that support it play an important role. While being centralised may reduce some risks, it may inadvertently increase others. Despite the centralised top-down nature of this financial technology, it still needs to be adopted so the consumer’s perspective, particularly their trust in it, is very important. Each CBDC implementation can be different, and each country’s context can be different, therefore it is important to understand each case separately. This research models the Brazilian consumer’s trust in their two-tier CBDC, where the central bank and the retail banks retain their current role (Zarifis and Cheng, 2025). This implementation is not a one tier solution where retail banks are bypassed in some ways, and the citizen interacts mostly with the central bank. Existing research that identified six ways to build trust in a different CBDC (Zarifis and Cheng, 2024) was used as a basis. This research tested a model with one additional way to build trust, but this additional way to build trust was not supported. The seventh hypothesized way that is not supported is that the implementation process, including pilot implementations, would build trust. Therefore, despite the differences in the Brazilian CBDC, the original model applies here also which suggests the model applies for both two-tier solutions, and mixed one and two-tier solutions.
Figure 1. Model of consumer trust in Brazil’s two-tier CBDC, adapted from (Zarifis and Cheng 2024)
Three institutional, and three technological factors, are found to play a role. The six ways to build trust that are supported are: (a) Trust in government and central bank offering the CBDC, (b) expressed guarantees for those using it, (c) the favourable reputation of other active CBDCs, (d) the CBDC technology, the automation and limited human involvement necessary, (e) the trust building features of the CBDC wallet app, and (f) the privacy features of the CBDC wallet app and back-end processes. It is important to develop user centered services in Brazil so that trust is built in the services themselves, and the government institutions that deliver them, sufficiently for broad adoption.
References Zarifis A. & Cheng X. (2024) ‘The six ways to build trust and reduce privacy concern in a Central Bank Digital Currency (CBDC)’. In Zarifis A., Ktoridou D., Efthymiou L. & Cheng X. (ed.) Business digital transformation: Selected cases from industry leaders, London: Palgrave Macmillan, pp.115-138. https://doi.org/10.1007/978-3-031-33665-2_6 (open access)
Zarifis A. & Cheng X. (2025) ‘A model of trust in Central Bank Digital Currency (CBDC) in Brazil: How trust in a two-tier CBDC with both the central and retail banks involved changes consumer trust’ In Zarifis A. & Cheng X. (eds.) Fintech and the Emerging Ecosystems – Exploring Centralised and Decentralised Financial Technologies, Springer: Cham. https://doi.org/10.1007/978-3-031-83402-8_4 (open access)
Different organizations see this period of transition and adjustment as either an opportunity or a threat. Some from outside the financial sector such as bigtech see it as an opportunity to take market share. Others have managed to limit competition and have regulatory ‘moats’ around the financial services they provide, that they would prefer to maintain. Whether an existing financial organization decides to keep their existing model, disrupt themselves in a drastic way, or evolve gradually into a new business model, it is important that they understand this multifaceted transformation. While startups are like agile speedboats that can change direction easily, large financial organizations more closely resemble large cruise ships that need to know where they will be in five years’ time, before setting a course to get there.
This research finds support for six Fintech business models that are optimised for AI and blockchain. These are (1) focus on less financial services and disaggregate, (2) absorb AI into existing financial model, (3) incumbent in finance expanding beyond current model, (4) new dedicated startup in finance disrupting the established ways of operating, (5) tech company disrupting finance, (6) disruptor not focused on technology with extensive user-base. The first three models involve organizations that are already active in the finance sector. The last three models are new organizations using AI to start offering financial services.
While the sixth model has similarities to the fifth, it also has some distinct features. The key characteristics are that it has an existing user base, with which trust has already been built, and unlike the fifth model, it uses advanced but commoditised financial technology. Despite the ongoing innovation, some Fintech transitioning into a commoditised service, that is easily deployed, is a sign of a maturing sector.
Figure 1. Six Fintech business models that are optimised for AI and blockchain
It is important to appreciate that existing trust with customers or fans, or the ability to build trust, are important parts of the value chain. Having the capability to build trust can be the starting point, with financial services added to it. Is the technology still the most critical factor in a Fintech at this stage, or the ability to build trust in it? Words such as modularity and ecosystem are often used, but it is important to understand how a new Fintech can be created, its journey and how it can build momentum and carve out a niche with technology and trust.
Reference: Zarifis A. & Cheng X. (2025) ‘The new centralised and decentralised Fintech technologies, and business models, transforming finance’ In Zarifis A. & Cheng X. (eds.) Fintech and the Emerging Ecosystems – Exploring Centralised and Decentralised Financial Technologies, Springer: Cham. https://doi.org/10.1007/978-3-031-83402-8_1
E-government can utilise the many new technologies to offer better services. Given the potential benefits of e-government, it is crucial to understand how to successfully achieve agile responses with e-government systems. An agile response in e-government, is when government employees use technology and are very effective in their role. The transformation of technology and collaboration methods, driven by the e-government systems, forces government employees to reconsider their daily workflow and collaboration with colleagues.
Despite the extensive existing knowledge of technology usage and collaboration, there are limitations in explaining the synergy between technology usage and group collaboration in achieving agile responses, from the perspective of government employees.
To address these challenges, this study provides a holistic understanding of the successful pathway to an agile response in e-governance, from the perspective of government employees. Two parallel paths are needed to achieve an agile response in e-governance. This study identifies five layers of mechanisms that lead to an agile response in e-governance, considering both the government-employee technology usage path, and the group collaboration path.
Figure 1. Model of how to achieve an agile response in e-governance
The dual pathways are as follows: Level 5 is positioned at the bottom of the model. It includes the fundamental factors that contribute to an agile response in e-governance, including ease of use, usefulness, and being traceable. Traceable in this context is more related to government employees’ work flow.
Levels 2, 3, and 4, are the intermediate factors, which play a bridging role, and are mainly composed of system quality, technology mindfulness, software reliance, communication transparency, trust, and collaboration efficiency. Specifically, system quality, technology mindfulness, and software reliance belong to the government employee technology usage pathway, while communication transparency, trust, and collaboration efficiency belong to the government employee collaboration pathway.
Level 1, at the top of the model is the ultimate goal, an agile response in e-governance. This research shows that to achieve an agile response in e-government, both the perspective of government employee technology usage, and the perspective of group collaboration efficiency must be taken into account.
Reference Bao Y., Cheng X., Su L. & Zarifis A. (2024) ‘Achieving employees’ agile response in e-governance: Exploring the synergy of technology and group collaboration’, Group Decision and Negotiation. https://doi.org/10.1007/s10726-024-09911-y (open access)
While ride-hailing platforms such as Didi, Uber, and Lyft have been with us for some years, it is an innovation that is still evolving, and customers beliefs on it, are still evolving also. Some are happy to use it, while others have some reservations.
Promoting the passengers’ trust in platform and customer citizenship behaviour (CCB) is both challenging and important. It refers to voluntary and discretionary behaviours that are not required for the successful production or delivery of the service, but that help the organization offering the service overall. In ride-hailing services, customer citizenship behaviour (CCB) is the voluntary behaviour of passengers, which is not necessary for the process of ride-hailing services.
This study looks at three aspects of the relationship of passengers’ trust in platform and customer citizenship behaviour (CCB): (1) What are the signals sent by the ride-hailing platforms that impact passengers’ trust in platform? (2) What are the dimensions of customer citizenship behaviour in the context of ride-hailing? (3) How does passengers’ trust in ride-hailing platforms influence their customer citizenship behaviour towards the platforms? The outcome of this research is the trust-customer citizenship behaviour (CCB) model in the ride-hailing context shown in figure 1.
The findings reveal that platforms can foster passengers’ trust by sending service-related signals (i.e., service quality and structure assurance) and a firm-related signal (i.e., platform reputation). Customer-company identification (CCI) mediates the relationship between passengers’ trust and customer citizenship behaviour (CCB), where passengers engage in CCB by providing recommendations, exhibiting forgiving behaviour, and providing feedback. Customer-company identification (CCI), is related to social identity theory, and refers to the positive and emotional attachment that passengers feel towards the values and concepts of a ride-hailing platform.
Additionally, firm-related signals, including platform size and reputation, enhance the positive relationship between trust and customer-company identification (CCI). These findings contribute to the body of knowledge on trust, customer citizenship behaviour (CCB), and signalling theory, and offer practical guidance to ride-hailing platforms.
Understanding how to build trust, and the specific benefits of a trusting relationship, encourages ride-hailing companies to work harder to build trust better. It also shows customers of these services the power they have, and how important they are to the success of these companies.
Reference Su L., Cheng X. & Zarifis A. (2025) ‘Passengers as defenders: Unveiling the role of customer-company identification in the trust-customer citizenship behaviour relationship within ride-hailing context’, Tourism Management, vol.107, 105086. https://doi.org/10.1016/j.tourman.2024.105086 (open access)
Generative AI (GenAI) has seen explosive growth in adoption. However, the consumer’s perspective in its use for financial advice is unclear. As with other technologies that are used in processes that involve risk, trust is one of the challenges that need to be overcome. There are personal information privacy concerns as more information is shared, and the ability to process personal information increases.
While the technology has made a breakthrough in its ability to offer financial insight, there are still challenges from the users’ perspective. Firstly there is a wide variety of different financial questions that are asked by the user. A user’s financial questions may be specific such as ‘does stock X usually give a higher dividend than stock Y’, or vague, such as ‘how can my investments make me happier’. Financial decisions often have far reaching, long term implications.
Figure 1. Model of building trust in advise given by Generative AI, when answering financial questions
This research identified four methods to build trust in Generative AI in both of the scenarios, specific and vague financial questions, and one method that only works for vague questions. Humanness has a different effect on trust in the two scenarios. When a question is specific, humanness does not increase trust, while (1) when a question is vague, human-like Generative AI increases trust. The four ways to build trust in both scenarios are: (2) Human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness, and finally (5) ease of use and support. For the best results all the methods identified should be used together to build trust. These variables can provide the basis for guidelines to organizations in finance utilizing Generative AI.
A business providing Generative AI for financial decisions must be clear what it is being used for. For example analysing past financial performance to attempt to predict future performance is very different to analysing social media activity. The advise of Generative AI needs to feel like a fully integrated part of the financial community, not just a system. Trust must be built sufficiently to overcome the perceived risk. The findings suggest that the consumer will not follow the ‘pied piper’ blindly, however alluring ‘their song’ of automation and efficiency is.
Reference Zarifis A. & Cheng X. (2024) ‘How to build trust in answers given by Generative AI for specific, and vague, financial questions’, Journal of Electronic Business & Digital Economics, pp.1-15. https://doi.org/10.1108/JEBDE-11-2023-0028 (open access)
Cryptocurrencies’ popularity is growing despite short-term fluctuations. Peer-reviewed research into trust in cryptocurrency payments started in 2014 (Zarifis et al., 2014, 2015). While the model created then is based on proven theories from psychology, and supported by empirical research, a-lot has changed in the past 10 years. This research re-evaluates and extends the first model of trust in cryptocurrencies and delivers the second extended model of consumer trust in cryptocurrencies CRYPTOTRUST 2 (Zarifis & Fu, 2024) as seen in figure 1.
Figure 1: The second extended model of consumer trust in cryptocurrencies (CRYPTOTRUST 2)
Trust in a cryptocurrency is a multifaceted issue. While some believe that the consumer does not need to trust cryptocurrencies because they utilize blockchain, most people appreciate that you must trust cryptocurrencies, just as you must trust any other technology you use that involves some risk.
The first three variables of the model come from the individual’s psychology: Personal innovativeness is divided into (1) personal innovativeness in technology and (2) personal innovativeness in finance. These two influence (3) personal disposition to trust.
There are then six variables that come from the specific context, and not the person’s psychology: The first three are related to the cryptocurrency itself. These are (4) the stability in the cryptocurrency value, (5) the transaction fees and (6) reputation. Institutional trust is shaped by (7) regulation and (8) payment intermediaries that may be involved in fulfilling the transaction. The last contextual factor is (9) trust in the retailer. The six variables from the context influence (10) trust in the cryptocurrency payment which then, finally, influences (11) the likelihood of making the cryptocurrency payment.
Separating personal innovativeness to personal innovativeness in (1) technology and (2) finance, is a useful distinction as some consumers may have different levels of personal innovativeness for technology and finance. The analysis here supports that these are separate constructs.
This research shows that trust in cryptocurrencies has not changed fundamentally, but it has evolved. All the main actors in the value chain still play a role in building trust. There is more emphasis from the consumer on having a stable value and low transaction fees. This may be because consumers now have more experience with cryptocurrencies, and they are better informed. It may also be because there are more cryptocurrencies available, and other alternatives such as Central Bank Digital Currencies (CBDC), so consumers can review the many alternatives and try to identify the best one.
References
Zarifis A., Cheng X., Dimitriou S. & Efthymiou L. (2015) ‘Trust in digital currency enabled transactions model’, Proceedings of the Mediterranean Conference on Information Systems (MCIS), pp.1-8. https://aisel.aisnet.org/mcis2015/3/
Zarifis A., Efthymiou L., Cheng X. & Demetriou S. (2014) ‘Consumer trust in digital currency enabled transactions’, Lecture Notes in Business Information Processing-Springer, vol.183, pp.241-254. https://doi.org/10.1007/978-3-319-11460-6
Zarifis A. & Fu S. (2024) ‘The second extended model of consumer trust in cryptocurrency payments, CRYPTOTRUST 2’, Frontiers in Blockchain, vol.7, pp.1-11. https://doi.org/10.3389/fbloc.2024.1220031 (open access)
By Dr Alex Zarifis, originally published in The Conversation
I have a confession to make. Despite being an academic, I do not actually read many books. In truth, I don’t often find books about what I like. My interest is on how the latest technologies affect business, and this topic is usually covered better by research articles and the press.
In the days when I was a student and money was tight, I was even less likely to buy books. But one exception was Wikinomics: How Mass Collaboration Changes Everything (2006). Written by Canadian tech thinkers Dan Tapscott and Anthony D. Williams, it captured my interest in how new innovations can change our personal and professional lives – and how exciting this change can be. Clearly I was by no means the only one that felt this way, as the book became a tremendous success.
The title, a compound of Wikipedia pages and economics, followed in the style of the equally successful Freakonomics (2005), but Wikinomics is very much a landmark in its own right. What it conveyed powerfully was that the level of mass collaboration and sharing online was about to move way beyond what we had seen in the first 15 years of the internet, transforming how people did business.
These were much more than mere technological advances, the book argued, and would require a completely different business mindset and philosophy. This was all about openness, sharing, freedom to innovate, and acting globally.
Wikinomics highlights seven new models of collaboration:
1. Open-source software: Software whose source code is made available for everyone to use and build on, which provides a way for firms and coders to coalesce around the same standard. One of the key early driving forces was the Linux operating system, while the book also points to Wikipedia as the archetypal example of the collaborative mindset.
Today, we see many examples of standalone coders coming together from around the world to build applications that are decentralised, meaning they’re not owned by anyone or based anywhere. Decentralised finance (defi), for instance, is offering a new way for people to do everything from trading financial assets to taking out mortgages.
2. Crowdsourcing innovative talent: This allows organisations to solve problems with ideas from outside, typically from other parts of the world. The example given in the book is InnoCentive (now Wazoku Crowd), a site where organisations post scientific challenges and offer rewards for their solution.
3. Prosumers: These are forward-thinking consumers who co-create products and services. In 2006, for instance, users of the virtual world, Second Life, were creating virtual buildings then renting them to other users. Today, fans of computer games such as Total War: Warhammer III are creating new characters and environments in a similar way.
4. Innovators sharing information: Making data widely available for others to use has particular importance in helping solve humanity’s greatest challenges, such as climate change. The increasing popularity of open-access publishing of academic research has been an important step in this direction.
5. Open platforms: Software that allows largely unrestricted access to its content and data gives businesses and individuals more room to collaborate and create new products. One example in the book is Google Maps, which was used by US entrepreneur Paul Rademacher to create a service called HousingMaps. It took data from Craigslist about homes for sale in a given area and pinned them on a map so that anyone searching for a home could see all the available locations at the same time.
Combining capabilities in this way became known as mash-ups, and can be seen today in a service like online bank Revolut. Revolut brings together services and information from a broad variety of organisations and offers them in an integrated way that is easy to use.
6. Mass collaboration in manufacturing: The book noted how a manufacturer like Boeing had shifted from designing everything in-house and sourcing specified parts from individual suppliers to instead having suppliers working together to design parts themselves and then assemble them in teams in Boeing factories.
This switch in emphasis from supply chains to ecosystems has more recently been typified by Shenzhen in China, where collaborative manufacturing in everything from circuits to touchscreens blurs the boundaries between the companies involved.
7. Modern workplaces that avoid hierarchies and silos: Instead of rigid structures, the driving force is social connectivity and fun.
Pros and cons
As is often the case with hugely successful books, Wikinomics was a combination of a great title, good writing and timing. By 2006 many of these trends were well underway. For instance, it was already common for coders to use open-source software like Linux for mass global collaboration. But if the book had been more original, it would not have been so well timed for mass market appeal.
A common criticism of Wikinomics is that it created many obscure terms that will only be familiar to those who have read the book. For example, its fourth collaborative model is called “ideagoras”, which hasn’t exactly caught on. No doubt the authors could have used simpler existing terms, but this is not the main weakness of the book.
With the benefit of hindsight, Wikinomics emphasised the positives of mass collaboration but did a poor job of foreseeing the challenges. Openness has made the world much more vulnerable to cybersecurity hacks, frauds and privacy breaches. Our behaviour online is now endlessly recorded and analysed, making people feel both distrustful and powerless.
What the book did do very well was to frame the issues around the new collaborative economy and explain them clearly. It helped readers to organise the new and old information in their minds, making it easier for them to analyse developments and be part of the revolution.
As a lecturer that teaches business on an executive MBA course to experienced managers, this is something I can appreciate. I cannot always tell them something they have not heard before, but if I can frame the issues well and communicate them clearly, it’s still useful to them. This is ultimately what Wikinomics did: it helped clarify the issues readers already had some understanding of, helping to shape the business zeitgeist for web 2.0.
There are many benefits for researchers that take part in a project but there are also several challenges that can create a cumulative, negative, effect on their mental health. This research identifies the challenges researchers face in projects, so that the leader of the project can reduce them as far as possible.
Existing research focuses on four stages of a project: Forming, Storming, Norming and Adjourning. This research adds a fifth stage, Post-Project Collaboration, as this stage is implicitly or explicitly a part of most research projects. For example, a post-doctoral researcher expects to be credited for their work even if it is published after the end of the project. The specific challenges for each of the five stages are identified. This enables the leader to focus on a manageable number of challenges at each stage.
Some challenges are in only in one stage of the process, while other challenges are across several stages. It is notable that there is no conflict at the start, but trust is a challenge at the start. This suggests that low trust at the start causes problems later. Therefore, there is a delayed reaction, and once the conflict happens it might be too late, as the trust should have been built earlier.
Figure 1: A model for reducing the challenges for researchers in projects across five stages
Trust is important in several collaboration settings, particularly at the start, until participants familiarise themselves with each other and the project team matures. In research teams, due to the long period of time until the research is published, often over five years, there is an additional, long-term cause for risk and distrust that is only resolved once the research is published.
Trust should be built during the first stage to cover four specific topics: Trust in the leader, process, evaluation method, and trust in being credited in published work.
In the final two stages of the project, adjourning and post-project collaboration, a new vision needs to be communicated effectively as the original vision stops resonating after the norming stage.
For those challenges that cannot be solved outright, the leader of the research project must show an awareness. The leader should be ambidextrous, in the sense of focusing on the project deliverables and the socio-psychological aspects of the teamwork.
Reference
Zarifis A. & Cheng X. (2024) ‘A model reducing researchers’ challenges in projects: build trust first for better mental health’, Cogent Business & Management, vol.11., no.1, pp.1-13. https://doi.org/10.1080/23311975.2024.2350786 (open access)
Financial technology often referred to as Fintech, and sustainability are two of the biggest influences transforming many organizations. However, not all organizations move forward on both with the same enthusiasm. Leaders in Fintech do not always prioritize operating in a sustainable way. It is, therefore, important to find the synergies between Fintech and sustainability.
One important aspect of this transformation many organizations are going through is the consumersʹ perspective, particularly the trust they have, their personal information privacy concerns, and the vulnerability they feel. It is important to clarify whether leadership in Fintech, with leadership in sustainability, is more beneficial than leadership in Fintech on its own.
This research evaluates consumers’ trust, privacy concerns, and vulnerability in the two scenarios separately and then compares them. Firstly, this research seeks to validate whether leadership in Fintech influences trust in Fintech, concerns about the privacy of personal information when using Fintech, and the feeling of vulnerability when using Fintech. It then compares trust, privacy concerns and vulnerability in two scenarios, one with leadership in both Fintech and sustainability, and one with leadership just in Fintech without sustainability.
Figure 1. Leadership in Fintech, trust, privacy and vulnerability, with and without sustainability
The findings show that, as expected, leadership in both Fintech and sustainability builds trust more, which in turn reduces vulnerability more. Privacy concerns are lower when sustainability leadership and Fintech leadership come together; however, their combined impact was not found to be sufficiently statistically significant. So contrary to what was expected, privacy concerns are not reduced more effectively when there is leadership in both together.
The findings support the link between sustainability in the processes of a Fintech and being successful. While the limited research looking at Fintech and sustainability find support for the link between them by taking a ‘top‐down’ approach and evaluating Fintech companies against benchmarks such as economic value, this research takes a ‘bottom‐up’ approach by looking at how Fintech services are received by consumers.
An important practical implication of this research is that even when there is sufficient trust to adopt and use Fintech, the consumer often still feels a sense of vulnerability. This means the leaders in Fintech must not just think about how to do enough for the consumer to adopt their service, but they should go beyond that and try to build trust and reduce privacy concerns to the degree that the consumer’s belief that they are vulnerable is also reduced.
These findings can inform a Fintech’s business model and the services it offers consumers.
Reference
Zarifis A. (2024) ‘Leadership in Fintech builds trust and reduces vulnerability more when combined with leadership in sustainability’, Sustainability, 16, 5757, pp.1-13. https://doi.org/10.3390/su16135757 (open access)
This research is on the state of central bank digital currencies (CBDC) in Latin America. This is the sixth chapter in my report with the University of Cambridge (Proskalovich et al. 2023). I have given a general overview of this report already, so I am just focusing on the chapter on CBDC adoption here.
A CBDC is essentially digital money, issued by a central bank. Unlike most cryptocurrencies that are decentralised, this currency is centralised. This is an important characteristic of the technology that has many implications. For example the central bank may be able to see all the user transactions.
CBDCs can be either wholesale and retail. The general public can use the retail version, while the wholesale version can move large amounts of money between banks. Our research findings suggest that Latin American central banks are focusing mainly on the retail version.
Retail CBDCs can operate with one tier or two tiers. A central bank can issue a one-tier retail digital currency directly to individuals. For the two-tier form, it issues the digital currency to a commercial bank who then offers them to individuals. Most existing implementations in Latin America are hybrid, offering both the one-tier and two-tier forms in parallel. In the hybrid scenario, the user has both a central bank digital wallet, and a retail bank digital wallet.
Figure 1: The motivations behind CBDC adoption in Latin America
These initiatives in Latin America are not completely new. There has been effort to develop and implement them for some time. The first initiative to explore CBDCs was actually back in 2014 in Ecuador. Most countries in Latin America have expressed interest in CBDCs, however, the extent of the engagement varies greatly from (1) exploring the opportunity, to (2) having concluded a pilot project, or (3) launched and available to the public.
There are several motivation behind creating this form of currency. The two main drivers are usually (1) financial inclusion, and (2) encouraging innovation in finance and improving the efficiency of payments. Other popular reasons are encouraging cross-border payments, monetary policy efficiency, reducing cash use, improving financial sector competition, de-dollarisation and reducing crime.
Challenges include (1) a large informal economy and the popularity of cash, (2) limited financial and digital knowledge, (3) lack of identity documents, (4) limited accessibility, (5) power outages and natural disasters, and (6) currency substitution and capital flight. Capital flight happens for several reasons including high inflation and unfavourable economic conditions.
If you want to learn more about this important part of the cryptoasset ecosystem, you can read the third chapter of the report.
This research is on the regulation of cryptoassets, such as Bitcoin, in Latin America. This is the fifth chapter of my report with the University of Cambridge, (Proskalovich et al. 2023). I have given a general overview of this report already, so I am just focusing on the chapter on regulation here.
Unlike a few years ago, most regulators in Latin America are now favourable towards cryptoassets. The prevailing belief is that cryptoassets, such as Bitcoin, are a valuable alternative to traditional finance, as they have different characteristics such as being decentralised. It is expected that cryptoassets can provide growth and a more inclusive financial landscape. They can provide easier cross-border payment, investments and loans. Cryptoassets can enable open-source collaboration reducing the barriers to entry for Fintech startups. Cryptoassets like NFTs can tokenise assets such as art, so that they can be offered to investors that may not be able to purchase the whole asset. In some scenarios, the way cryptoassets use blockchain can offer transparency in terms of what transactions have happened.
The main risks are believed to be misinformation, scams, and money laundering. Key challenges for regulators include (1) not enough staff with cryptoassets knowledge, (2) insufficient coordination between countries, and (3) lack of cooperation between the public and private sector.
Figure 1. Some countries in Latin America want to lead on crypto regulation, while others want to follow
There is an increase in regulators’ attention in the last few years with most countries either having or developing specialised rules. Despite most regulators agreeing that progress must be made, there is a large difference in the pace of progress. Some countries strategy is to lead with fast and comprehensive regulation to control the risk, and maximise the benefits. At the other end of the spectrum, some countries prefer to move more cautiously, keeping the uncertainty and risk low, and accepting that the benefit will also be lower. An example of a country leading is Mexico that is the first in Latin America to regulate cryptoasset trading platforms. Most local cryptoasset companies believe regulatory uncertainty is the biggest challenge preventing their growth.
Whether the regulators strategy is to lead, follow or something in-between, they need to follow the developments in the cryptoassets ecosystem both globally, and in Latin America, so that the right decisions are made at the right time, and friction between countries is limited.
If you want to learn more about regulation of cryptoassets like Bitcoin in Latin America, you can read the fifth chapter of the report.
This research is about the business models of Decentralised Finance (DeFi) in Latin America. This is the fourth chapter of my report with the University of Cambridge (Proskalovich et al. 2023). I have given a general overview of this report already, so I am just focusing on the chapter on DeFi here.
DeFi refers to several software solutions that operate on a blockchain. These decentralised systems, supported by blockchain technologies, enable various forms of financial services. DeFi currently operates alongside the traditional financial system, thriving where the traditional system is either inefficient, or expensive, for users. It is unclear whether DeFi and traditional finance will continue in parallel in the future, or merge.
Figure 1. The Decentralised Finance (DeFi) services becoming popular in Latin America
DeFi services
DeFi is constantly evolving, and new use cases will emerge in the coming years. Some use cases of DeFi already identified are decentralised stablecoins, exchanges, lending, derivatives, and asset management.
Payments are an important part of DeFi. Payment can be just with a cryptoassets such as Bitcoin, or they can change bitcoin to a traditional currency and vice versa. Some key areas of the decentralised payments ecosystem are (1) traditional fiat currency-to-crypto services on exchanges, (2) cryptoasset ATMs that exchange traditional currency for cryptoassets and vice versa, (3) cards allowing users to buy and spend cryptoassets, as well as receive them as rewards in loyalty schemes, (4) digital wallets that allow users to send, receive and store cryptoassets, enabling cheaper cross border payments, and (5) both e-commerce and physical shops are increasingly accepting cryptoassets.
DeFi adoption in Latin America
The use DeFi is increasing dramatically, but despite this growth, the activity is very small relative to the use of commercial banks. Digital solutions are vital to overcoming the challenges associated with financial inclusion in Latin America. For example, mobile money use has grown significantly. As cryptoasset adoption in Latin America increases and users become more familiar with the decentralised ecosystem, activity will likely increase. There is a-lot of decentralised financial innovation in the region, such as cryptocurrencies, crypto mining, blockchain and NFTs, with consumers eager to learn more about this ecosystem. So far, Brazil, Argentina and Mexico have the highest adoption of DeFi among Latin American countries.
If you want to learn more about this important part of the cryptoasset ecosystem, you can read the fourth chapter of the report.
I am going to talk to you about the business models, and ecosystems, of cryptomining in Latin America. This is the third chapter in my report with the University of Cambridge, Judge Business School (Proskalovich et al. 2023). I have given a general overview of this report already, so I am just focusing on the chapter on cryptomining here.
The blockchain consensus mechanism used in Bitcoin, and some other cryptocurrencies, requires mining for the proof-of-work process. Mining, helps verify transactions and create new cryptoasset tokens. Activity from companies and individuals in this area can positively impact the cryptoasset ecosystem, by encouraging cryptoasset adoption, and providing an income stream.
Figure 1: The factors making Latin America popular for crypto mining
Cryptomining in Latin America happens in registered mining companies, mining pools, and so called ‘ant farms’. Mining pools are a form of cooperation in which people share the risks and returns from mining. ‘Ant farms’ are created by hobbyist that install mining equipment in a residential area.
Latin America has some characteristics that support cryptomining and allow miners to be competitive internationally. These features include relatively cheap electricity and renewable power resources, such as solar, hydro and geothermal. The electricity price is one of the most significant factors determining the profitability of cryptomining, and whether a country will become a cryptomining hub. Despite this, Bitcoin mining in this part of the world is still only a small part of the global mining volume.
The popularity of cryptomining varies across Latin American countries. Some of the leading bitcoin mining countries in this part of the world are Brazil, Paraguay, Venezuela, Mexico and Argentina. In addition to electricity prices, other determining factors are regulation, subsidies, climate, the level cryptoasset adoption, and the general state of the economy. Mining is not widespread in the countries of this region where cryptocurrencies are partially, or entirely, banned.
The crypto mining industry seems to be very sensitive to regulation and electricity prices, and does not appear to be as ‘sticky’ to a geographic location as other parts of the crypto ecosystem. Some miners even have their IT hardware permanently in shipping containers when they are operating, so they can transport it to another country relatively easily. Changes in how countries regulate crypto mining often have a knock-on effect. For example, when Venezuela made regulation stricter, some mining activity moved from there, to Brazil.
If you want to learn more about this part of the cryptoasset ecosystem, you can read the third chapter of the report.
A Non-Fungible Token, usually referred to by its acronym NFT, uses technology that involves data on a blockchain that cannot be changed after they have been added. Therefore, while they share similar blockchain technology with cryptocurrencies, the functionality is different. NFT’s functionality enables them to be used to prove ownership of an intangible-digital, or tangible-physical, asset, and the associated rights the owner has. The most popular practical application of NFTs for digital assets is proving ownership of digital art, virtual items in computer games, and music. The unique features of NFTs are becoming increasingly appealing as we spend more of our time online. Despite this increased popularity there is a lack of clarity over the final form this digital asset will take. The purchasing process in particular needs to be clarified. This research developed a model of the purchasing process of NFTs and the role of trust in this process. The model identified that the purchasing process of NFTs has four stages and each stage requires trust. You see here in the figure, the four stages in the purchasing process on the left, and the trust required in each of these stages along the center. Finally, on the right you see that trust in all four stages leads to trust in an NFT purchase.
Figure 1. Model of consumer trust at each stage of the NFT purchasing process
The four stages of the purchase are: First, set up a cryptocurrency wallet to pay for the NFT, and to be able to receive it. Second purchase cryptocurrency with the cryptocurrency wallet, third use the cryptocurrency wallet to pay for an NFT on an NFT marketplace and finally, there is the fourth, after sales service that may involve returns, or some other form of support. The model that is supported by our analysis identified four stages to trust: First trust in the cryptocurrency wallet, second trust in the cryptocurrency purchase, third trust in the NFT marketplace, and fourth trust in after-sales services and resolving disputes.
Reference: Zarifis, A. & Castro, L.A. (2022) ‘The NFT purchasing process and the challenges to trust at each stage’, Sustainability, vol.14, no.24:16482, pp.1-13. https://doi.org/10.3390/su142416482 (open access)
This research explores the beliefs of a consumer from outside China, that is buying Chinese products. The findings highlight the importance of the cues of the country-of-origin, on foreign consumers’ intention to purchase Chinese products. The results also enhance our understanding of consumers’ beliefs on purchasing foreign products in general (Bao et al., 2022).
There are three practical implications:
Firstly, this study highlights several significant factors that may enable e-commerce managers, to strengthen consumers’ intention to purchase Chinese products. Cross-border e-commerce platforms should improve product quality, brand image, control cost and follow international business norms, as these act as a guarantee for foreign consumers.
Figure 1. Impacts of country-of-origin image on product evaluation and purchase intention
Secondly, cross-border e-commerce platforms should provide different advertising or marketing strategies for products with different levels of involvement. For example, for low-involvement products, managers should put more advertising effort into the product itself, and emphasize the quality of the product, and the high speed of delivery. For high-involvement products, marketing should emphasize the image of the product’s country of origin.
Thirdly, consumers’ nowadays may perceive more external-product cues, instead of the product itself. Cross-border e-commerce platform managers should be aware that the image of the product’s country of origin, heavily influences the consumers’ perceived product value. Nevertheless, they should also pay attention to the cross-border e-commerce company’s social norms, and develop effective policies to maintain consumers’ interests and rights.
Reference
Bao Y., Cheng X. & Zarifis A. (2022) ‘Exploring the impact of country-of-origin image and purchase intention in cross-border e-commerce’, Journal of Global Information Management, vol.30, iss.2, pp.1-20. https://doi.org/10.4018/JGIM.20220301.oa7 (open access)
Dr Alex Zarifis There has been rapid development in Cross-Border E-Commerce (CBEC) in recent years, and it is strengthening the global economy. However, compared to other trade mechanisms, the mechanism of CBEC is more complex. It appears that staff with special talents are needed to start a business trading across borders. Research has presented some solutions for the challenges of cross-border e-commerce from the perspective of the enterprise. However, not enough is known about the cross-border e-commerce talents requirements, and how to train people to develop them.
Requirements of CBEC talents We found four core the requirements of Cross-Border E-Commerce talents (Cheng et al., 2019): (1) Firstly, having business and marketing knowledge is critical to Cross-Border E-Commerce talents. Both theoretical and practical knowledge are important components in business and marketing knowledge. (2) Secondly, technical skills for trading online is necessary. For example, dealing with all the problems in the process of online trading is important. (3) Thirdly, it’s common to handle difficult situations when business across borders, hence analytical ability is needed for them to solve these problems. (4) Last but not least, all these skills we mentioned are not independent of having practical ability in business, which is the core the requirement. In summary, business and marketing knowledge, technical skills, analytical ability and practical ability in business were found to be the four core requirements of CBEC talents.
Figure 1. Training model for people working in cross-border e-commerce
Training model for CBEC talents After finding the four core requirements, we analyzed several teaching methods. We then chose Problem-Based Learning (PBL), which typically involves learning based on solving real problems. We applied WeChat in the training model, so the real work environment is replicated more closely. Writing a business plan was also applied in the training model as the way of practicing practical skills. The problem oriented training model encourages discussions and learning new knowledge. By successfully applying the model in teaching, this research provides a new direction to cultivate the CBEC talents.
Reference Cheng X., Su L. & Zarifis A. (2019) ‘Designing a talents training model for cross-border e-commerce: A mixed approach of problem-based learning with social media’, Electronic Commerce Research, vol.19, iss.4, pp.801-822. https://doi.org/10.1007/s10660-019-09341-y
