services and Value of subjective context

So far, we have argued that there is a new type of subjective context and suggested that technically it is possible to produce such systems, primarily thanks to in-creased access to data, via smart devices and digitalized environments and ser-vices. These services fuel applications of machine learning, which, as we saw in Section 3.5, allows development of analysis of emotions, indicating a step toward understanding personal accounts. This corresponds to the technical component related to the business model, but we have yet to discuss the value-added services that are used, or the funding models and actors in the value chain, which are also critical in context-aware business models [32]. Furthermore, adapting from De Reuver and Haaker (2009), each of these domains can be extended to subthemes [32], as elaborated in Table 1.

Domain Objective them to further improve the predictability. by using the data to further, for example, advertising or partnership.

Potentially also subscription-based business models can also emerge, if the value added to end users is high enough.

The service is monetized by using the data to further, for example, advertising or partnership.

Potentially subscription-based business models can also emerge, if the value added to end users is high enough. that own customer data and control the building of these services.

There will be data-collection hubs (e.g., Google, Amazon) that own customer data and control the building of these services.

Table 1. Business potential of context awareness according to De Reuver and Haaker [32], and exploration of what subjective context awareness can add to these opportunities.

We compare our case between the objective and subjective contexts, and ob-serve that their main differentiation emerges on the value-added component. In detail, we observe that in terms of value creation, added value emerges from ca-tering to extended subjective needs in the context awareness, as seen in the case description. Furthermore, the customer retention dynamic is different because users are involved in providing data—both consciously and unconsciously— for the reinforcement learning algorithms. This will create more personal ties with the system and also a vendor-locking on data collection. Compared to objective con-text services, the subjective concon-text services cannot be changed simply by replac-ing hardware, but require subjectification of the data. Regardreplac-ing other domains of the business model, namely, funding models and value chains, we do not predict significant differentiation between objective and subjective services. Naturally, the fact that the value-added prospects—potentials for targeting, value creation, and customer retention—are different may have implications on the details, such as revenue sharing between operators or the emerging partner network.

However, we acknowledge that the business development process is more dif-ficult. To illustrate this, we reference Kaasinen (2005), who explored adaptation of location-based services before they became mainstream [33]. She discusses factors such as critical mass, user control, and challenges in the patterns use of service adaptation.

The critical mass refers to the number of users as well as the variety of services offered by the system. Critical mass increases social acceptability in single-user services and social effects in collaborative services. Our examples thus far have reflected single-use situations (i.e., information is not shared be-tween users); thus, the empirical observation of critical mass relates to social accessibility. We also highlight that several other human factors, such as user experience [34], are socially constructed. A recent example of critical mass and social acceptance failure is the introduction of Google Glass, which became a joke piece of technology and was thus discarded. Finally, Kaasinen (2005) ob-served that mobile use patterns are sporadic, and the technologies need to take this into account [33].

However, the emergence of business models is not only based on the users’ needs and the social context. Rather, the existing value chains and business models limit the opportunities for new models [35]. Such mechanisms include access to data, existing business dynamics, and access to technologies, in the form of research and development (R&D) efforts and, for example, patents. Thus, to understand the emergence of subjective context awareness, we must engage these topics.

4.1 Context as Data-Heavy Business

In earlier sections, we discussed several approaches to how machines can un-derstand context. Because many of the techniques depend on the access to data, this will limit the opportunities for emerging businesses. This is already visible in academia; if research depends on “big data,” then it blocks new researchers entering the domain, as has already been observed [36]. Furthermore, access to data, even while technically possible, may not be sustainable business-wise. For example, Facebook, which once promoted an open application programming in-terface (API) to allow interoperability [37], recently closed access to some of its resources, most likely reflecting the dominance Facebook has achieved in the so-cial area. We see similar opportunities thinking emerging in the industry, where the newest development is to provide machine learning systems as services.

Project Oxford (2015) by Microsoft provides image-based emotion detection as a service [38]. It provides an API for developers that can perform an emo-tion analysis on an image submitted to the API. The service essentially detects

individual faces in an image and returns a vector of emotions: anger, contempt, disgust, fear, happiness, neutral, sadness, surprise.

IBM Watson (2015) takes it up a notch and offers a wide spectrum of “cogni-tive” services that can give insights on contents such as image, speech, and text [39]. One of the rather interesting services is called Personality Insights. The Personality Insights API takes a text of 3,500+ words written by a single person to generate a personality inventory based on Big Five traits. Similarly, the Tone Analyzer service classifies a text as positive, negative, or neutral. It is naturally expected that more of these types of services will come as this ecosystem starts to generate value and trigger more research to take a solution-oriented approach.

4.2 Previous R&D Efforts Related to Context

Patents are an important aspect of business dynamics because they enable block-ing of business for other organizations. The actual benefit of this, however, is lim-ited, as seen during the smartphone patent war in early 2010s, where the courts were hesitant to limit the access to markets [40]. Thus, patents were not able to protect the vendors’ positions. Keeping this in mind, we explore the total number of patents to indicate the business position in the era of context awareness, as well as R&D output in the area.

As of November 2015, a Google Patents search found 15,000 patents using the terms context aware or context awareness (Figure 7). Major assignees of these patents were Microsoft and Nokia. Microsoft’s position is so strong that the other technology companies just barely sum up to the impressive count of 639 patents regarding context awareness. Naturally, we have not explored in detail what is patented. But we can presume that many of these patents relate to objective-context-awareness emerging services and technologies. Of these, the service patents are still relevant in the subjective context era because they define how context-aware data are used in business applications.

Fig. 7. Patents granted to different companies for context-aware products and services.

Google Nokia Microsoft IBM Facebook Apple Samsung Sony Oracle SAP 700

600 500 400 300 200 100 0

4.3 Example: Sensitive Cooking Advisor

In our first story, we mentioned how a system was able to order food ingredients using various parameters. These parameters would include availability for cooking activities and preferred brands based on previous purchases and other behavioral data, but should also extend to deeper values, for example, in relation to the environment and choices made there, or reflecting preferences on cook-ing methods, or adaptcook-ing to the overall household situation. This requires access to previous eating behaviors and understanding of the constraints for cooking.

The end-user value emerges from the possibility to automate everyday activities, such as shopping, and trust that the automation will not screw up.

In this case, the business value naturally emerges from the shopping system;

by deploying this type of system, the customer is also integrated with a specific vendor. This customer loyalty, we believe, will justify the initial investments in data collection and service development. In practice, this could emerge by some of the existing companies moving in this direction (e.g., Amazon or Walmart) or by a new platform operator developing the necessary infrastructure and operat-ing as a broker between the customer and retail industries. This would naturally affect the value chain; in specific companies the risk is that the value chain would become retail-centric (i.e., each operator develops his or her own cooking advi-sor and they cannot share the data), whereas platformization risks change the revenue sharing throughout the retail ecosystem (e.g., make current vendors obsolete).

4.4 Example: Sensitive Smart-Home Automation

Earlier, we discussed the case of a system that automatically adapts the home light-ing, temperature, and other related aspects based on the intentions and preferences of the user. This would include previous user habits, but also personal preferences and interactions with other participants. Again, as is common in context-aware applications, the end-user value emerges from automation. Also, the business is networked; it needs to connect the context operators to the smart-home automa-tion. However, we assume that the emergence of the business network here would be simpler because home automation vendors have the implicit interest to ensure that automation systems can integrate with other operators—as indicated not only by technology companies’ interest to platformize this area, but also by emerging interest in the open-source community to create platform hubs that become tech-nology agnostic. Thus, compared to the sensitive cooking advisor discussed earlier, we propose that this application would require less effort.

4.5 Example: Truth Machines

The second story exemplifying the automation of truth systems, in which users can verify information and rate the sources as reliable or not, is also based on the networked business model—however, not in the same sense as in the previous examples. These services will aggregate the personal input (comments and senti-ments) of each user about pieces of information, which in turn will corroborate or contradict its truthfulness. We envision future business models of a sensitive nature; these could be potentially empowering if handled independently or pro-foundly misleading in powerful mischievous hands.