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.
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. Available from (open access): https://www.inderscienceonline.com/doi/abs/10.1504/IJEB.2019.099059
Have you made a purchase from a three dimensional Virtual World (VW)? Probably not, only a small minority have. When VWs first became popular fifteen years ago, people jumped to the conclusion that they were the future, the new platform to socialise online. Their adoption however did not end up being exponential. So why do the experts often think VWs, with their additional functionality are the future, but that future has not come yet? We decided to ask the consumer. There is a degree of understanding on what each channel can offer but the relative advantage of each channel in relation to the others is less understood. By relative advantage we mean something the one channel, for example three dimensional VWs, have an advantage over two dimensional, traditional, websites. This research, evaluates the relative advantage between the channels of three-dimensional VWs, two-dimensional websites, and offline retail shops. The consumer’s preferences across the three channels, were distinguished across six relative advantages.
Figure 1 The three channels and six relative advantages in multichannel retail In the figure, you can see at the top the six different relative advantages, and beneath them, how the three different channels perform, in relation to these relative advantages. Participants, showed a preference for offline and 2D websites, in most situations apart from enjoyment, entertainment, sociable shopping, the ability to reinvent yourself, convenience and institutional trust where the VWs were preferred. We can look in more detail at the fifth relative advantage, that VWs have higher institutional trust compared to 2D websites. Consumers value the role of the VW as an institution in relation to trust. One feature that is appreciated is that the buyer does not receive your banking details. Some participants value the role of the VWs administration in identifying and warning about specific threats. The findings illustrated in the figure, show that the consumer’s preference varies across the three channels, and six RAs. An organization pursuing a multichannel strategy, can adapt their offerings in each channel to fully utilize these different preferences. While on most issues VWs are the least appealing from the three channels, framing the comparison with the six relative advantages shows how they have a useful and complementary role to play in multichannel retail. For example, customer support can be done in VWs. An organization, can use these findings to shape their business model and strategy.
Reference Zarifis A. (2019) ‘The six relative advantages in multichannel retail for three-dimensional Virtual Worlds and two-dimensional websites’, Proceedings of the 10th ACM Conference on Web Science, June 19–21, Boston, USA, pp.363-372. Available from: https://dl.acm.org/doi/pdf/10.1145/3292522.3326038
Several countries’ economies have been disrupted by the sharing economy. However, each country and its consumers have different characteristics including the language used. When the language is different does it change the interaction? If we have a discussion in English and a similar discussion in German will it have the same meaning exactly, or does language lead us dawn a different path? Is language a tool or a companion holding our hand on our journey?
This research compares the text in the profile of those offering their properties in England in English, and in Germany in German, to explore if trust is built, and privacy concerns are reduced, in the same way.
Figure 1. How landlords build trust in the sharing economy
The landlords make an effort to build trust in themselves, and the accuracy of the description they provide. The landlords build trust with six methods: (1) The first is the level of formality in the description. More formality conveys a level of professionalism. (2) The second is distance and proximity. Some landlords want to keep a distance so it is clear that this is a formal relationship, while others try to be more friendly and approachable. (3) The third is ‘emotiveness’ and humour, that can create a sense of shared values. (4) The fourth method of building trust is being assertive and passive aggressive, that sends a message that the rules given in the description are expected to be followed. (5) The fifth method is conformity to the platform language style and terminology that suggests that the platform rules will be followed. (6) Lastly, the sixth method to build trust is setting boundaries that offer clarity and transparency.
Privacy concerns are not usually reduced directly by the landlord as this is left to the platform. The findings indicate that language has a limited influence and the platform norms and habits have the largest influence. We can say that the platform has choreographed this dance sufficiently between the participants so that different languages have a limited influence on the outcome.
Zarifis A., Ingham R. & Kroenung, J. (2019) ‘Exploring the language of the sharing economy: Building trust and reducing privacy concern on Airbnb in German and English’, Cogent Business & Management, vol.6, iss.1, pp.1-15. Available from (open access): https://doi.org/10.1080/23311975.2019.1666641
Short videos are very popular but if they take up a-lot of people’s time, they gradually change people’s living habits. Therefore it is useful to understand the negative implications of short videos. The results show that users’ viewing many short videos can have negative emotions, and these negative emotions can affect users’ intention to continue to use short video platforms. The model developed in this research shows that there are three negative emotions caused by six factors. Two of these three negative emotions then influence the intention to continue using short videos.
From the six factors that cause negative emotions, the five are related to flow theory. Flow theory is relevant here because watching short videos is a flow experience. Flow theory is a state where someone is fully immersed in an activity, they are enjoying it and other things do not seem to matter as much.
The first of the five factors related to flow theory is the low efficiency the user has in their work and other tasks, due to watching short videos. The second is time distortion, meaning that the users perception of time is not as accurate during this activity. What might feel like a short amount of time can be much longer. The third is the harm to their health. Both mental and physical health can be harmed by spending a long time watching short videos. The fourth is the online addiction they experience, making them want to keep watching the short videos. The fifth is online procrastination, making the user watch more short videos to delay working and making decisions related to their work.
The sixth factor that can cause negative emotions is illusion of control. The theory of illusion of control suggests that in some situations a person can be overconfident about their control of a situation. A person can have a level of optimism that they will get the outcome they want, that is unrealistic. The negative emotions include anxiety, sadness and remorse. The research found strong support that sadness and remorse influence the users intention to continue using the short videos.
Reference: Cheng X., Su X., Yang B., Zarifis A. & Mou J. (2023) ‘Understanding users’ negative emotions and continuous usage intention in short video platforms’, Electronic Commerce Research and Applications, vol.58, 101244, pp.1-15. https://doi.org/10.1016/j.elerap.2023.101244
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).
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.
Zarifis A., & Cheng X. (2022). AI Is Transforming Insurance With Five Emerging Business Models. In Encyclopedia of Data Science and Machine Learning (pp. 2086–2100). IGI Global. Available from (open access): https://www.igi-global.com/chapter/ai-is-transforming-insurance-with-five-emerging-business-models/317609
New Fintech and Insurtech services are popular with consumers as they offer convenience, new capabilities and in some cases lower prices. Consumers like these technologies but do they trust them? The role of consumer trust in the adoption of these new technologies is not entirely understood. From the consumer’s perspective, there are some concerns due to the lack of transparency these technologies can have. It is unclear if these systems powered by artificial intelligence (AI) are trusted, and how many interactions with consumers they can replace. There have been several adverts recently that emphasize that their company will not force you to communicate with AI and will provide a real person to communicate with are evidence of some push-back by consumers. Even pioneers of AI like Google are offering more opportunities to talk to a real person an indirect acknowledgment that some people do not trust the technology. Therefore, this research attempts to shed light on the role of trust in Fintech and Insurtech, especially if trust in AI in general and trust in the specific institution play a role (Zarifis & Cheng, 2022).
Figure 1. A model of trust in Fintech/Insurtech
This research validates a model, illustrated in figure 1, that identifies the four factors that influence trust in Fintech and Insurtech. As with many other models of human behavior, the starting point is the individual’s psychology and the sociology of their environment. Then, the model separates trust in a specific organization and trust in a specific technology like AI. This is an important distinction: Consumers have beliefs about the organization they bring with them and other pre-existing beliefs on AI. Their beliefs on AI might have been shaped by experiences with other organizations.
Therefore, the validated model shows that trust in Fintech or Insurtech is formed by the (1) individual’s psychological disposition to trust, (2) sociological factors influencing trust, (3) trust in either the financial organization or the insurer and (4) trust in AI and related technologies.
This model was initially tested separately for Fintech and Insurtech. In addition to validating a model for trust in Fintech and Insurtech separately, the two models were compared to see if they are equally valid or different. For example, if one variable is more influential in one of the two models, this would suggest that the model of trust in one of them is not the same as in the other. The results of the multigroup analysis show that the model is indeed equally valid for Fintech and Insurtech. Having a model of trust that is suitable for both Fintech and Insurtech is particularly useful as these services are often offered by the same organization, or even the same mobile application side by side.
Zarifis A. & Cheng X. (2022) ‘A model of trust in Fintech and trust in Insurtech: How Artificial Intelligence and the context influence it’, Journal of Behavioral and Experimental Finance, vol. 36, pp. 1-20. Available from (open access): https://doi.org/10.1016/j.jbef.2022.100739
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.
(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.
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. Available from: 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.
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. Available from: 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. Available from (open access): https://emeraldopenresearch.com/articles/1-15/
Ransomware attacks are not a new phenomenon, but their effectiveness has increased causing far reaching consequences that are not fully understood. The ability to disrupt core services, the global reach, extended duration, and the repetition of these attacks has increased their ability to harm an organization.
One aspect that needs to be understood better is the effect on the consumer. The consumer in the current environment, is exposed to new technologies that they are considering to adopt, but they also have strong habits of using existing systems. Their habits have developed over time, with their trust increasing in the organization in contact directly, and the institutions supporting it. The consumer now shares a significant amount of personal information with the systems they have a habit of using. These repeated positive experiences create an inertia that is hard for the consumer to move out of. This research explores whether the global, extended, and repeated ransomware attacks reduce the trust and inertia sufficiently to change long held habits in using information systems. The model developed captures the cumulative effect of this form of attack and evaluates if it is sufficiently harmful to overcome the e-loyalty and inertia built over time.
Figure 1. The steps of a typical ransomware attack
This research combines studies on inertia and resistance to switching systems with a more comprehensive set of variables that cover the current e-commerce status quo. Personal information disclosure is included along with inertia and trust as it is now integral to e-commerce functioning effectively.
As you can see in the figure the model covers the 7 factors that influence the consumer’s decision to stop using an organization’s system because of a ransomware attack. The factors are in two groups. The first group is the ransomware attack that includes the (1) ransomware attack effect, (2) duration and (3) repetition. The second group is the E-commerce environment status quo which includes (4) inertia, (5) institutional trust, (6) organizational trust and (7) information privacy.
Figure 2. Research model: The impact of ransomware attacks on the consumer’s intentions
The implications of this research are both theoretic and practical. The theoretic contribution is highlighting the importance of this issue to Information Systems and business theory. This is not just a computer science and cybersecurity issue. We also linked the ransomware literature to user inertia in the model.
There are three practical implications: Firstly, by understanding the impact on the consumer better we can develop a better strategy to reduce the effectiveness of ransomware attacks. Secondly, processes can be created to manage such disasters as they are happening and maintain a positive relationship with the consumer. Lastly, the organizations can develop a buffer of goodwill and e-loyalty that would absorb the negative impact on the consumer from an attack and stop them reaching the point where they decide to switch system.
Zarifis A., Cheng X., Jayawickrama U. & Corsi S. (2022) ‘Can Global, Extended and Repeated Ransomware Attacks Overcome the User’s Status Quo Bias and Cause a Switch of System?’, International Journal of Information Systems in the Service Sector (IJISSS), vol.14, iss.1, pp.1-16. Available from (open access): https://doi.org/10.4018/IJISSS.289219
Zarifis A. & Cheng X. (2018) ‘The Impact of Extended Global Ransomware Attacks on Trust: How the Attacker’s Competence and Institutional Trust Influence the Decision to Pay’, Proceedings of the Americas Conference on Information Systems (AMCIS), pp.2-11. Available from: https://aisel.aisnet.org/amcis2018/Security/Presentations/31/
Most of us appreciate the importance of Artificial Intelligence (AI) and we know it is bringing big changes. Will we finally have that robot assistant we were promised 50 years ago? Will we even have to work, at least in the sense of how we view work today? It is fun to daydream about these things, but insurers need some certainty on what the future looks like. Some new insurers are trying new business models enthusiastically and then changing direction sharply, like a speedboat swerving to avoid a collision. The larger insurers, however, are like large cruise ships. They need to be able to see far ahead before they plot their course, and they don’t want to keep changing direction.
We wanted to identify the viable AI driven business models to help give some clarity and guidance. Previous efforts had identified four models being applied now, that showed a varying level of enthusiasm for AI. But what about ten years from now? Would there be convergence to one model? If not, what are the key issues stopping this?
AI is bringing fundamental changes to insurance business models. Those that fail to adapt are likely to disappear. Some traditional insurers are trying to just be more effective with AI, while others reinvent themselves to fully utilize the new capabilities available. Tech-savvy companies from outside the sector like Tesla, are entering and disrupting it. Despite these diverging approaches, there are signs of a convergence towards one, ideal, business model.
Our research focused on one example of a traditional insurer and one new tech-savvy disruptor and evaluated whether their models are converging. We found a high degree of convergence, but some differences are likely to remain even after this transitionary period.
Table 1. Traditional insurers and tech companies AI powered insurance business models
Tech company offering insurance
Service and revenue
Complex and simple service, B2B and B2C Improve customization and interaction
Simple standardised B2C services Revenue not a priority
Estimating risk and pay-outs
Information of a high relevance, quality, and reliability
Real time information on behavior Proactively influence behavior and reduce risk
Several business models in parallel to utilize different technologies Alliance with tech companies to access their user base Better automated interaction
Bundle insurance with existing services, remove the hurdle of insurance for the consumer Use existing access to user data so no additional privacy concerns
AI is changing the insurance value chain, as illustrated in figure 1. Most new insurers, like Tesla, offer fully automated simple services. The traditional insurers offer some of their simpler services in this way. The more complex services are supported with AI, but a human makes the final decision. An example of this are audits for fraud, where the AI identifies unusual patters and cases for an expert to evaluate.
There are signs of convergence between the models of traditional and new insurers. First, there is convergence in technologies, such as the use of chatbots utilizing AI. Second, there is a convergence in processes, for example, the interaction with the consumer. Third, there is convergence in the strategy on costs and pricing.
However, there are two areas where there seems to be a limit on convergence, which seems to suggest the business models of the incumbent and the disruptor will remain distinct. These are: (1) evaluating risk and (2) the cost of attracting the user and profitability.
AI is changing the way risk is evaluated by an insurer. These new ways of using data and technology to assess risk, in turn generate new insurance services. This is true for new insurers but also some forward-looking traditional insurers. Some existing insurers are creating new services from the new data that is available to them, but they are also creating new services to gain new data.
New insurers like Tesla calculate risk in three ways. First, data is collected from the cameras and sensors in the vehicle providing insight on real time behavior of individuals and groups. Second, a broader analysis of individuals with hundreds of variables is implemented and new algorithms that evaluate risk accurately are improved. Third, the impact on risk of the new technologies is constantly monitored and, in some cases, influenced. For example, software upgrades can be made instantly to all vehicles to improve safety.
Cost of Attracting the User and Profitability
The technology company offering insurance has some advantages in terms of the cost of attracting new consumers and the profits they generate. While the traditional insurer must spend money on marketing to attract consumers to their insurance services, the technology company uses existing consumers. Furthermore, while the traditional insurer is dependent on their revenues from insurance services, the technology company can draw profits from other services and provide insurance without any profit.
Despite some convergence, certain differences are likely to remain even after this transitionary period. This is because the two models have distinct competitive advantages. Traditional insurers no longer monopolize the capability of providing insurance, but they still have the existing user base and utilize it to evaluate risk. Technology-savvy companies that now offer insurance, have their own forms of engagement with their consumers, use different methods to evaluate risk due to their access to real time data, and do not prioritize generating revenue but instead utilize insurance to increase their user base, overcome barriers, and reduce the overall cost of their products and services.
Therefore, when insurers are thinking about how to utilize AI and plot their course through the turbulent, unpredictable times ahead, they should stay true to what they are. This is their comparative advantage.