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)
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.
Fintech is changing the services to consumers, and their relationship with the organizations that offer them. This change is neither top-down nor bottom-up, but is being driven by many different stakeholders in many different parts of the world, making it hard to predict its final form. This research identifies five business models of Fintech that are ideal for AI adoption, growth and building trust (Zarifis & Cheng, 2023).
The five models of Fintech are (a) an existing financial organization disaggregating and focusing on one part of the supply chain, (b) an existing financial organization utilizing AI in the current processes without changing the business model, (c) an existing financial organization, an incumbent, extending their model to utilize AI and access new customers and data, (d) a startup finance disruptor only getting involved in finance, and finally (e) a tech company disruptor adding finance to their portfolio of services.
Figure 1. The five Fintech business models that are optimised for AI
The five Fintech business models give an organization five proven routes to AI adoption and growth. Trust is not always built at the same point in the value chain, or by the same type of organization. The trust building should usually happen where the customers are attracted and on-boarded. This means that while a traditional financial organization must build trust in their financial services, a tech focused organization builds trust when the customers are attracted to other services.
This research also finds support that for all Fintech models the way trust is built, should be part of the business model. Trust is often not covered at the level of the business model and left to operation managers to handle, but for the complex ad-hoc relationships in Fintech ecosystems this should be resolved before Fintech companies start trying to interlink their processes.
Alex Zarifis
Reference
Zarifis A. & Cheng X. (2023) ‘The five emerging business models of Fintech for AI adoption, growth and building trust’. In Zarifis A., Ktoridou D., Efthymiou L. & Cheng X. (ed.) Business digital transformation: Selected cases from industry leaders, London: Palgrave Macmillan, pp.73-97. https://doi.org/10.1007/978-3-031-33665-2_4
Collaborative Consumption (CC) and the sharing economy, where consumers do not purchase a product or service, but share it, is growing in popularity. This is due to a trend away from ownership towards experiencing. The first two areas of the economy that this business model disrupted were fare sharing and renting rooms for short periods. Other areas are also influenced but it is unclear which sectors of the economy will be disrupted next. Smaller niches of the economy, or areas where more public-sector involvement is necessary, such as the elderly and the disabled may not be at the forefront and may be the laggards losing out on possible benefits for years.
This research evaluates the current CC business models and identifies 13 ways they add value from the consumer’s perspective. This research further explores whether CC business models fall into two categories in terms of what the consumer values. In the first category, they require a low level of trust while in the second category a higher level of trust is necessary. Our survey evaluates whether there was a difference between CC business models that require a low level of trust such as a taxi service and those that required a high level of trust such as supporting the elderly and disabled.
Figure 1. Comparative spider diagram of value added by collaborative consumption business models for low and high required trust
The analysis verified that the consumer requires 13 types of value added from the business model which can be separated into three categories which are personal interest, communal interest and trust building. It is important for organizations to acknowledge how they relate to these dimensions.
It was found that CC business models can be separated into those that require a relatively low level of trust such as fare sharing and those that require a high level of trust such as supporting the elderly and disabled, as we can see in the figure here. For the business models that only require low trust, the consumer considered the personal interest value added more important, while in the those requiring more trust the consumer rated the value added of trust building higher.
The findings suggest that changing CC business model from one that requires low trust to one that requires higher trust necessitates a significant improvement in how the organisation builds trust. This can be considered a ‘step’ change in trust-building which would have to be a consideration at business model level. Iterative improvements at operational level may not increase trust sufficiently.
Reference
Zarifis A., Cheng X. & Kroenung J. (2019). Collaborative consumption for low and high trust requiring business models: From fare sharing to supporting the elderly and disabled, International Journal of Electronic Business, vol.15, no.1, pp.1-20. https://www.inderscienceonline.com/doi/abs/10.1504/IJEB.2019.099059 (open access)
Artificial intelligence (AI) and related technologies are creating new opportunities and challenges for organizations across the insurance value chain. Incumbents are adopting AI-driven automation at different speeds, and new entrants are attempting to use AI to gain an advantage over the incumbents. This research explored four case studies of insurers’ digital transformation. The findings suggest that a technology focused perspective on insurance business models is necessary and that the transformation is at a stage where we can identify the prevailing approaches. The findings identify the prevailing five insurance business models that utilize AI for growth: (1) focus on a smaller part of the value chain and disaggregate, (2) absorb AI into the existing model without changing it, (3) incumbent expanding beyond existing model, (4) dedicated insurance disruptor, and (5) tech company disruptor adding insurance services to their existing portfolio of services (Zarifis & Cheng 2022).
Figure 1. Updated model of five business models in insurance with disruptors split into two types
In addition to the five business models illustrated in Figure 1, this research identified two useful avenues for further exploration: Firstly, many insurers combined the two first business models. For some products, often the simpler ones, such as car insurance, they focused and disaggregated. For other parts of their organization, they did not change their model, but they absorbed AI into their existing model. Secondly, new entrants can be separated into two distinct subgroups: (4) disruptor focused on insurance and (5) disruptor focused on tech but adding insurance.
The interest in Non-fungible Tokens (NFTs) has ‘exploded’ recently, but it is not clear what final form they will take. This innovation will have difficulties reaching a wider audience until more clarity is achieved on two main issues: What exactly are the NFT business models, and how do they build trust. The findings of recent research (Zarifis and Cheng, 2022), illustrated in figure 1, show that there are four NFT business models:
(1) The first business model is an NFT creator: They can create digital art that is then minted as an NFT, and sold on an NFT platform. The NFT competitive advantages include having proof of irrefutable ownership, and the ability to sell a piece of art that is unique or limited to a low number. The reliability and transparency of the NFT, build trust with the consumer.
Figure 1: The four NFT business models
(2) The second business model is an NFT marketplace, selling creators’ NFTs: The competitive advantage of NFTs as part of this business model is once again the irrefutable ownership, and that it gives consumers digital art they can own. The purchase history of the consumers is transparent, so this gives insights into their interests. As with the previous business model, a community and trust are built between the collectors.
(3) The third business model is a Company offering their own NFT, typically a fan token: This business model has several NFT processes. These are to sell NFTs for profit, to give NFTs as rewards, make payment with fan tokens, give an NFT so that the person receiving it has certain utilities and rights, such as voting rights. The competitive advantages of NFTs, within this business model, are that they allow fans to feel closer to their team and builds a community and trust between the fans.
(4) The fourth business model is a Computer game with NFT sales: There can be in-game purchases of NFT minted virtual items, limited or unique in game purchases and players can be rewarded for playing, know as ‘play to earn’. This offers incentives to game developers to continue producing rare items, provides an ongoing revenue stream for existing games, and builds a community and trust between the players.
This research was the basis of Dr Alex Zarifis keynote speech in front of around 300 people at the 2022 JEBDE’s 2nd Academic Conference on Electronic Business & Digital Economics on the 28/09/22.
Reference
Zarifis A. & Cheng X. (2022) ‘The business models of NFTs and Fan Tokens and how they build trust’, Journal of Electronic Business & Digital Economics, vol.1, pp.1-14. https://doi.org/10.1108/JEBDE-07-2022-0021
Universities, like many other organizations, are going through a disruptive digital transformation. The alure of AI and automation, allowing smarter, more responsive and scalable universities is clear. What is less clear is what a university will look like five years into this process. We identified four business models that can give leaders a destination for the digital transformation journey (Zarifis and Efthymiou 2022):
(1) This first education business models that is optimized for AI is to focus and disaggregate: In addition to the classroom the successful delivery of education requires a supply chain. With the changes in this supply chain caused by AI an educator can chose to focus on one part of this supply chain. They can focus on the part of the supply chain where their skills are best suited and build an ecosystem for the rest.
Figure 1. Four education business models that are optimised for AI (adapted from (Zarifis, Holland, and Milne 2019))
(2) The second model that is optimized for AI is to keep the existing education model and add AI: Despite the transformational nature of AI, some universities use AI to make the existing model more effective without changing it fundamentally. This may involve more back-office AI applications and less student facing applications.
(3) The third education model that is optimized for AI is an educator expanding beyond the current model: In this model the educator takes advantage of new opportunities emerging from AI and digital transformation. The educator keeps their existing part of the education supply chain, but they also add new processes that take advantage of AI to reach more students and more data.
(4) The fourth model that is optimized for AI is the model of a disruptor entering education: As technology plays a more decisive role in many areas, including education, tech savvy companies can use their advanced systems and existing user base and add other new services. Education can be added as a new feature to a platform in a similar way that banking and insurance services have been added.
The four models presented give a strategic direction and make it easier for the leader of the digital transformation to communicate it. The leader of digital transformation will have to make many choices along this journey, so it is important that all the decisions are compatible with the chosen education business model.
References
Zarifis A. & Efthymiou L. (2022) ‘The four business models for AI adoption in education: Giving leaders a destination for the digital transformation journey’, IEEE Global Engineering Education Conference (EDUCON), pp.1866-1870. https://doi.org/10.1109/EDUCON52537.2022.9766687
Zarifis A., Holland C.P. & Milne A. (2019) ‘Evaluating the impact of AI on insurance: The four emerging AI and data driven business models’, Emerald Open Research, pp.1-17. https://emeraldopenresearch.com/articles/1-15/ (open access)
