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Predicting the future of disruptive technologies: The method of alternative histories
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Predicting the future of disruptive technologies: The method of alternative histories Vlad Krotov Arthur J. Bauernfeind College of Business, Murray State University, 109 Business Building, Murray, KY 42071, U.S.A.
KEYWORDS Managerial decision making; Management strategy; Market disruption; Disruptive technologies; Internet of things; Alternative histories
Abstract With digital technologies shaping competition in many industries, predicting the future of potentially disruptive technologies becomes an essential task of business leaders concerned with the survival and success of their organizations. Looking into the future of disruptive technologies requires a philosophical stance and a practical method that accommodates the inherent uncertainty and nonlinearity of the path of disruptive innovations. Unfortunately, much of the current thinking in relation to adoption and diffusion of innovations is rather linear and deterministic. This article proposes a set of philosophical principles, together with a practical brainstorming method, for glimpsing into the future of disruptive technologies. The method of alternative histories is based on the traditional brainstorming techniques and the philosophical ideas of Imre Lakatos, Roy Bhaskar, Bruno Latour, and Nassim Taleb. ª 2019 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
1. The problem of predicting the future of disruptive technologies Microsoft was famously founded on the vision that there would soon be a desktop computer in every home and on every worker’s desk, and that this new platform would disrupt the existing computing market (Lowe, 1998). This vision turned out to be correct and likely drove Microsoft’s unprecedented
early success. Predicting the future of important technological trends is an essential task of any business leader (Christensen, Anthony, & Roth, 2004). Being able to foresee how a particular emerging technology might change the competitive landscape within an industry can help an organization protect its competitive advantage from threats or use the technology to strengthen its position in the market (Christensen, 2013). Failure to see how a potentially disruptive technology may evolve in the future can diminish
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the competitiveness of an organization or even jeopardize its existence. The story of Western Union and the telephone is a famous example of how executive blindness to a potentially disruptive emerging technology can quickly diminish a company’s dominant position within an industry (Christensen et al., 2004). In 1875, Alexander Graham Belldan unknown inventor at the timedoffered to sell his telephone technology patent to William Orton, the President of Western Union Telegraph Company; the proposed price was $100,000, which would be roughly $2 million today (Carlson, 1994). William Orton, a talented engineer himself, certainly understood what the telephone was yet he thought it had little relevance to Western Union’s telegraph business. Telephone technology, including Bell’s, was unreliable at that time. It could transmit voice messages only over short distances (e.g., within city limits). Western Union’s business involved transmission of telegraph messages over long distances. Orton also thought that telephone, when used over the company’s existing telegraph lines, would overload its network and lower the quality of existing telegraph services. Because of these and many other considerations, Orton turned down Bell’s offer. When Bell made his offer to Orton, Western Union was one of the most dominant telecommunications companies in the world. Within a few years, contrary to Orton’s belief that telephone technology was not a direct substitute for the telegraph, it began to cut into Western Unions’ market share. In an attempt to catch up, Orton commissioned his engineers to develop Western Union’s own telephone technology but the window of opportunity was gone. Western Union’s telegraph business continued to vanish. Today, few people even remember Western Union as a telecommunications giant as it is more recently known for its global money transfer business. In defense of William Orton, looking into the future of a particular technology is often a daunting task. Given the inherent messiness of the sociotechnical realm surrounding most technologies, the future of a new technology can be hard to predict with certainty (Latour, 1987). Thinking Figure 1.
about the future of technologies requires a different philosophical foundation that accommodates this inherent uncertainty and allows one to think about the future of a technology in a nonlinear fashion. Unfortunately, much of the current theoretical tradition with respect to diffusion of innovation is inherently linear and does not accommodate this nondeterminism of technological innovations. This article is an attempt to propose a philosophical base, together with a practical method, that would allow business leaders to glimpse into the future of disruptive technological innovations.
2. Sustaining and disruptive innovations There are numerous theories on the adoption and diffusion of technological innovations (e.g., see Moore, 2009; Rogers, 2010) that are inherently deterministic and linear in nature. These theories make the following problematic assumptions: 1) any technological innovation will go through a certain sequence of adoption phases and 2) the technology and its features will freeze along the dimensions traditionally valued by existing customers, unable to make any lateral moves from one customer segment to another. Looking at Rogers’ (2010) influential theory of diffusion of innovation, it assumes that an individual will come to adopt an innovation via a linear decision process comprised of five stages (see Figure 1). Each of the decision phases are briefly explained in Table 1. A similar linear pattern is evident in the diffusion of the innovation process at the market level (see Figure 2). According to Rogers (2010), consumers adopt an innovation in groups. The first group to adopt an innovation is made up of socalled innovators, which make up only 2.5% of all consumers. Innovators are followed by slightly more cautious early adopters, which comprise 13.5% of the total market. The early majority and late majority groups each comprise 34% of consumers. The last group to adopt an innovation are the laggards, covering approximately 16% of the total market.
The five stages in the innovation adoption process
Source: Adapted from Rogers (2010)
Predicting the future of disruptive technologies Table 1.
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Innovation adoption decision stages
Knowledge
The individual is first exposed to an innovation
Persuasion
The individual becomes interested in innovation and actively seeks additional information about the information
Decision
The individual weighs all the cons and pros of adopting an innovation and decides either to accept it or reject and abandon the innovation
Implementation
The individual actually uses the adopted innovation in a specific context and gains additional information about the cons and pros of the innovation
Confirmation
The individual finalizes his or her opinion on the innovation and makes a final decision on whether to use the innovation further or not
Figure 2.
Diffusion of innovation among consumers
Source: Adapted from Rogers (2010)
This linear theoretical tradition (Rogers, 2010) may be applicable to sustaining technological innovations (Christensen et al., 2004). Sustaining innovations are new products or technologies that offer incremental improvements in value propositions of existing products or services (Christensen et al., 2004). Sustaining innovations first meet the needs of customers with low or very basic expectations regarding a product attached to the innovation (see Figure 3). Sustaining innovations introduce better features that not only meet the needs of customers with low expectations but also customers with higher expectations. A product may overshoot the needs of the low expectations group in terms of the price and features, creating an opportunity for a low-end disruption: a new product targeting the same group of consumers based on a set of slightly different features or technologies. Gillette is a good example of sustaining innovation. Every few years or so, Gillette introduces new shaving products that have more razor blades
for a smoother and cleaner shave. These features are familiar to and valued by existing consumers. In the future, Gillette may introduce a shaving product that is simply too sophisticated for lowend customers (e.g., a product with too many razor blades), which will create an opportunity for a more basic product with just one blade enclosed in cheap plastic (e.g., single blade disposable shavers by Bic). While offering a shaving experience that is less smooth, the product may meet low expectations and offer an attractive price. This may constitute the so-called low-end disruption to the existing market for Gillette’s shaving products but it is not a true disruptive innovation since it is based on similar technologies that offer similar value for the same group of customers. This linear thinking about diffusion of innovation is hardly applicable to disruptive technological innovations. Disruptive innovations often start out weak. They tend to have low reliability and questionable value in the early stages; it is not clear whether this technology will ever become reliable
4 Figure 3.
V. Krotov Sustaining and disruptive innovations
Source: Adapted from Christensen et al. (2004)
enough to be commercialized and what will be the main target market (Christensen et al., 2004). The adoption path of disruptive innovations is often nonlinear and subject to serendipity, since disruptive technology often finds acceptance and applications in industries very far from the domain where they were created (Krotov & Junglas, 2008). A hypothetical example of a product that follows a disruptive innovation path is a cream for body hair removal (see Figure 3). This cream may start out in a totally different market, offering a relatively pain-free and razor-free solution to hair removal. Perhaps these consumers do not want to use razor blades because they irritate the skin or because they find the shaving process tedious. Thus, disruptive innovations often emerge in a slightly different market and, at first, do not compete with an existing product (e.g., shaving products by Gillette or Bic). Instead, they may simply attract nonconsumers. Moreover, they may start out low on quality, meeting only the needs of customers with modest expectations. Eventually the hair removal cream technology may mature enough that it can compete directly with existing shaving products. With some improvements in features and marketing, the hair removal cream technology can make an unexpected lateral move into the market in which shaving products based on razor blades dominate. This will constitute the emergence of a new, disruptive technology in the
market. With time, this disruptive technology can overtake the market for shaving products, making steel razor blades an obsolete technology. This can happen, for instance, if applying a cream to one’s face eliminates facial hair for several weeks or even permanently. Thinking about the future of disruptive technologies requires a nondeterministic and nonlinear approach. Just because a particular technology is currently not on a diffusion and adoption path to intercept an existing market, this does not mean the technology will not make a lateral move and become a serious competitive threat in the future. While the theory of disruptive innovation (Christensen et al., 2004) outlined here does a great job explaining what disruptive innovations are, it offers little guidance on how to analyze emerging disruptive technologies, especially when it comes to predicting their possible future adoption paths.
3. Predicting the future as the ultimate proof of knowledge Being able to predict the future of technological trends is not only of practical importance for entrepreneurs and business leaders, but it also constitutes the ultimate proof in professional fields, religion, and the postpositivist philosophy of science. We expect experts to predict future
Predicting the future of disruptive technologies phenomena within their area of expertise (Krotov & Silva, 2005). The public expects economists to predict macroeconomic conditions not only in the nearest future but also in the long term. Stock analysts routinely predict where the stock market will go in the future to prove their understanding of the stock market. A similar approach to evaluating truthfulness of knowledge has been used in Abrahamic religions for millennia. In Christianity, predictions in relation to the Messiahda savior or liberator sent by Goddfound in the Old Testament and later fulfilled by the life of Jesus Christ described in the New Testament are used as one of the most important proofs of the legitimacy and knowledge of God’s will of the prophets of the Old Testament. Jesus Christ’s own predictions in relation to his crucifixion and resurrection are also used as important proofs of Jesus Christ being God’s most important messenger. Although science often tries to distance itself from business practice and religion, the same line of thinking is used by philosophers of science to delineate science from pseudoscience. Imre Lakatos (1995)done of the most well-known postpositivist philosophers of sciencedrejected authority, one of the oldest forms of supporting validity of knowledge, as a criteria for distinguishing scientific knowledge from a pseudoscientific one. Human history is full of examples of seemingly authoritative people and organizations rejecting what later turned out to be a fact proven beyond any doubt (e.g., the fact that the Earth rotates around the Sun). Lakatos found Popper’s (1965) falsification principles to be naive as well, as they can lead to a seemingly absurd situation in which the probability of all scientific theories is zero. Indeed, according to Popper, a theory must be falsified in order to be accepted as scientific. Thomas Kuhn’s (2012) pragmatic approach to sciencedin which a theory constitutes scientific knowledge when a sufficient number of scientists believe in itddoes not satisfy Lakatos either as this may equate science with witchcraft and astrology. According to Lakatos (1973), it is its ability to predict stunning new facts that distinguishes science from pseudoscience (Lakatos, 1973): But all the research programs I admire have one characteristic in common. They all predict novel facts, facts which had been either undreamt of, or have indeed been contradicted by previous or rival programs.. Halley, working in Newton’s program, calculated on the basis of observing a brief stretch of a
5 comet’s path that it would return in seventytwo years’ time; he calculated to the minute when it would be seen again at a well-defined point of the sky. Addressing the problem of predictability of future technological trends is not just of great practical value but also a way to strengthen the position of a field of inquiry or professional practice as scientific and producing true knowledge.
4. The philosophical underpinnings of the method of alternative histories Lakatos’ demarcation principle may be unrealistic when applied to the inherently messy and unpredictable social realm. Unlike physicists, who often strive to develop nearly 100% deterministic models and theories, social scientists seek to discover social tendencies that are probabilistic in nature. According to Bhaskar (2008), modern social sciences cannot fully adhere to the empirical, experimental nature of positivism. While social scientists can formulate theories that resemble natural laws under the positivist tradition, they may not be verifiable using the experimental activities of the natural sciences. The problem is that various combinations of events may intervene with the exact sequence of events needed to prove a particular social theory in a positivist sense. Thus, social theories may or may not materialize in the real world. Therefore, no matter how much a particular person knows about the world of business or the broader sociotechnical environment, this knowledge can only lead to a future possibility and not an absolute certainty. Thus, all social theories are probabilistic in nature. So what can be done given the inherent uncertainty of the social realm? Nassim Taleb, an influential modern author, investor, and a philosopher of science, proposed one approach in his book Fooled by Randomness. He discussed the philosophical foundation for a practical method of thinking about the future of disruptive technologies. This method is allegorically delivered through what appears to be a semifictional image of Taleb’s own boss during his days as a trader in New York (Taleb, 2004, pp. 32e33): I am still amazed at this flamboyant man’s obsession with risks, which he constantly played in his headdhe literally thought of everything that could possibly happen. He forced me to make an alternative plan should a plane crash into the office building and fumed at my answer that the financial
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Figure 4.
The method of alternative histories
condition of his department would be of small interest to me in such circumstances. This approach to thinking about future possibilities proposed by Taleb (2004) allows for nonlinear, nondeterministic thinking. Instead of trying to imagine one linear scenario of how a particular technology may evolve, one should envision the entire sociotechnical network within which a particular technological trend is embedded (Latour, 2005). This network is comprised of a complex web of human and nonhuman actors, material objects, processes, concepts, and many other relevant factors and social forces that shape how technology is developed and used within society (Latour, 2005). The various possibilities of all these diverse actors coming into interaction with each other are quite numerous so the goal of this line of thinking is not to determine which particular scenario is more probable, but rather to envision these scenarios and analyze their impact on an organization.
5. The method of alternative histories Drawing on the ideas of Lakatos (1973), Bhaskar (2008), Latour (2005), and Taleb (2004), this article proposes a practical method for brainstorming the future of disruptive technological innovations. The method of alternative histories involves brainstorming about various future possibilities that can unfold in relation to a particular disruptive technology. The method is similar to the traditional brainstorming method in a sense that it thrives to create an environment in which a diverse group of experts can build upon other’s ideas to produce innovative thinking in relation to a business problem. In contrast to the traditional method, it is iterative in nature, somewhat less structured, and explicitly focused on generating ideas in relation to possible threats and
opportunities created by the sociotechnical realm surrounding technologies. The phases comprising the method are provided in Figure 4. Phases 1 and 2 include assembling a diverse team and scheduling a brainstorming session. Note that after familiarizing the team with the key assumptions of this brainstorming method, the brainstorming session should proceed in an iterative fashion; new actors can be added to the list at any point. These new actors can spark ideas about additional future scenarios. Overall, although Figure 4 may imply a degree of linearity, the team should feel free to move between these phases as needed, indicated by the arrows pointing in both directions. For example, new rules can be created during the brainstorm session and new members can be added to the team during the exercise. The main goal of this brainstorming method is not to follow a particular protocol rigorously but rather to come up with a good list of possible future scenarios in relation to a potentially disruptive technology and assess their conceivable impact an organization. All of this is needed to stimulate creative, nonlinear thinking in relation to a potentially disruptive technology. The phases comprising this brainstorming method are discussed in more detail in Sections 5.1.e5.7. I provide examples in relation to the Internet of Things (IoT)da potentially disruptive technology across many industriesdfor each of these steps. IoT is defined here as a ubiquitous network connecting together various nodes that belong to the technological, physical, and broad socioeconomic environments (Krotov, 2017). These examples assume that the brainstorming session in relation to IoT is organized by a fictional company called Agrisoft Solutions, LLC. Agrisoft develops and sells software applications that assist farmers with land and crop management. LandBase is the company’s flagship product. All of the firm’s other software and hardware products provide technical
Predicting the future of disruptive technologies solutions that support the functions of LandBase. LandBase provides farmers with the following information: United States Environment Protection Agency (EPA) approved labels for agricultural products; Various regulatory documents in relation to crops; Information on crop protections products and their active ingredients; and Harvesting times and intervals for various crops in various hardiness zones. Agrisoft’s top management has become concerned that the farming business increasingly is driven by information and communication technologies (ICTs). Specifically, IoT solutions are becoming increasingly popular in agriculture. They are based on sensors and ubiquitous networks designed to monitor the soil, external environment, farming products, farming equipment, and people involved in the growing of crops. The company wants to brainstorm the future threats and opportunities that IoT can present their organizations with in the future.
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5.2. Phase 2: Organize a brainstorming session After potential participants in the brainstorming session are identified, the facilitator should set up a brainstorming session. The session should be scheduled in a room in which the needed number of participants can fit comfortably. The room should be equipped either with an overhead projector with a projection screen or a physical whiteboard. Both can be used for showing the needed information to the participants. This information can include a list of potential human and nonhuman actors that can come into play to create future scenarios in relation to a particular disruptive technology. The list of identified future scenarios together with their potential impact on the organization in question can be shown to participants as well. For example, Agrisoft can schedule the session in a videoconferencing room used for meeting with colleagues and clients in remote locations. The room should have ample seating capacity and contain either a whiteboard or an overhead projector with a screen for displaying information for everyone in the room.
5.3. Phase 3: Familiarize the team with the method
5.1. Phase 1: Assemble a diverse team First, identify a group of experts that can participate in this brainstorming exercise. In order to ensure a diversity of opinions and ideas about a disruptive technology of interests, the group should be comprised of participants with diverse educational and professional backgrounds. The group is headed by a facilitator that will moderate the discussions and manage the brainstorming sessions in the subsequent phases. In the case of Agrisoft, invited experts can include technology specialists from the company, top management representatives, various hardware and software vendors that the company is collaborating with when delivering its software solutions to farmers, and farmers. In addition, the company can invite academics from a nearby university in fields like technology and business. Representatives of various nonprofits such as consumer privacy groups can be invited as well in order to provide a broader legal and social perspective on the technology in question. A diverse team can help spark creativity and nonlinear thinking in relation to IoT and its future impact on agricultural business. A formal facilitator from Agrisoft should be appointed for this exercise to moderate these discussions.
Before starting a session, the facilitator should make the participants aware of the key values, assumptions, and phases of the brainstorming method. First, the facilitator should explain the main purpose of this exercise to the session’s participants. Second, the facilitator should make the participants comfortable with the fact that predicting the future of a technology with high certainty is challenging, if not impossible. The goal of the exercise is to generate future possibilities and not to predict which will actually materialize; no possibility should be ruled out as impossible or ridiculous. This explanation is central to the philosophical assumptions behind the method and, therefore, should be clearly conveyed to the session’s participants. Third, the facilitator should also explain each phase to the participants, including: (1) generating a list of human and nonhuman actors in relation to the disruptive technology, (2) enumerating all of the ways in which these actors can come into play and influence the future of disruptive technologies, and (3) assessing the impact of these future possibilities on the organization in question. The facilitator should explain to the participants that they can switch between these phases at will or propose adding additional experts to the session (e.g., via videoconferencing) to stimulate creative, nonlinear thinking.
8 In the case of Agrisoft, an appointed facilitator can explain to the participants that the company’s management is becoming increasingly concerned with the question of how the IoT can affect the agriculture business, the agricultural software industry, and their organization in particular. Thus, the main goal of the meeting is to brainstorm about the possible future threats and opportunities IoT can create for the organization. The facilitator should explain to the participants that (1) no idea should be dismissed as ridiculous; (2) the participants should envision all relevant human and nonhuman actors that are likely to impact the future of IoT and its impact on the business of Agrisoft; (3) the positive and negative impact of these individual scenarios should be assessed; and (4) the session’s participants can switch between phases if necessary (e.g., by inviting other experts to the session or by introducing additional human and nonhuman actors to the list).
5.4. Phase 4: Envision human and nonhuman actors The brainstorming exercise should start with the group of experts envisioning the sociotechnical network comprised of material actors and nonmaterial concepts surrounding a particular technological trend (Latour, 2005). This is consistent with the philosophical assumption that a complex network of technical and social factors often influences adoption paths of disruptive innovations. To spark the generation of ideas, the facilitator can ask the group the following question: What are the things or concepts that come to your mind when you think about the IoT concept in agriculture? Each idea or human or nonhuman actor related to IoT use in agriculture can be captured with the help of a sticky note. Each note can then be attached to a white board in front of the audience. Alternatively, a software program can be used for creating notes and displaying them via a projector on a screen (e.g., Trello). An example of a list of human and nonhuman actors that Agrisoft can come up with in relation to the IoT is presented in Figure 5. The list can be expanded as the group of experts brainstorms about possible future scenarios that can be created via the interplay among the actors and assesses impact of these scenarios on the company’s business.
5.5. Phase 5: Brainstorm about future scenarios After envisioning the relevant actors, the participants should brainstorm about future possibilities
V. Krotov in relation to a potentially disruptive technology by connecting the dots among the human and nonmaterial actors listed. To spark brainstorming, the facilitator can ask: How can these actors possibly interact with each other and how will these interactions influence the adoption and diffusion of the technology in question? Based on the list of actors depicted in Figure 5, the brainstorming session’s participants can generate the following scenarios or alternative histories relevant to Agrisoft’s business: Consumer privacy groups advocate for a more stringent protection of data collected with the help of agricultural software applications; The Food and Drug Administration requires all farm products to be tagged with Radio Frequency Identification (RFID) transmitters; and Growing demand for IoT software applications for monitoring soil and atmospheric conditions using wireless sensors
5.6. Phase 6: Assess the potential impact of each scenario Each possibility, or alternative history, generated in brainstorming session should be discussed from the perspective of its potential impact on an organization or industry of interest. The facilitator can ask the following questions: Are we, as an organization, prepared for this particular scenario? Or, what are the opportunities or threats if this particular scenario materializes? At any time during this exercise, the facilitator should encourage the participants to bring in the so-called black swans: highly unlikely and often unimaginable events that, nevertheless, might occur and may have a profound impact on how a particular phenomenon evolves (Taleb, 2008). The results of this analysis in relation to specific scenarios can be captured using a table. For example, Afrisoft may come up with the analysis of the three sample scenarios provided in Table 2.
5.7. The practical goals of the brainstorming method The organization’s main goal for this brainstorming method is improved awareness regarding the various possibilities associated with a potentially disruptive technology. Being aware of these scenarios can expand the horizons of top management when creating broad strategies aimed to exploit
Predicting the future of disruptive technologies Figure 5.
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Human and nonhuman actors in relation to the IoT
opportunities and protect the organization from threats of a potentially disruptive technology. With this awareness, it is less likely that the organization will be caught off guard by a potential threat to its market share or miss some important opportunities. Technology specialists can also become better prepared for the future developments on the technological front. Perhaps, senior technology leaders should consider investing in the development of additional technical
expertise in relation to IoT so the company can rapidly expand and improve the functionality of its existing products.
6. Forewarned is forearmed Today, the sword of potentially disruptive technologies is hanging over existing business models in many industries. Social media is replacing the
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Table 2.
Analyzing threats and opportunities created by future scenarios Threat
Opportunity
Consumer privacy groups advocate for a more stringent protection of data collected with the help of agricultural software applications
Additional investment are required for improving protection of data generated by Agrisoft software products and complying with possibly more stringent consumer privacy legislature
Agrisoft can use data security and legislature compliance as a differentiating point for its software products
The Food and Drug Administration requires all farm products to be tagged with Radio Frequency Identification (RFID) transmitters
Agrisoft does not have a solution for RFID tagging; this can put the company behind the competition
Agrisoft can come up with a line of software and hardware products for tagging food items with RFID tags and integrating this data with existing information systems used by stores and farmers
Growing demand for IoT software applications for monitoring soil and atmospheric conditions using wireless sensors
The flagship product of Agrisoft, LandBase, does not provide information support in relation to soul and atmospheric conditions
Agrisoft can expand the list of informational products supplied together with LandBase information system marketed to farmers; this can be another differentiating point in the ear of the Internet of Things
Scenario
traditional news media and advertising channels (Kaplan & Haenlein, 2010). The rise of massive open online courses, or MOOCs, is a worrisome trend for many traditional brick-and-mortar universities (Kaplan & Haenlein, 2016). Artificial intelligence is another important technological development that threatens service industries and jobs that once were thought to be immune to automation (Kaplan & Haenlein, 2019). Organizations that are driven by supply chains are also in need for guidance on how IoT may transform their industries and business models (Krotov, 2017). Many questions in relation to the future of these disruptive technologies as well as their competitive impact on industries and individual organizations are still open and require answers from the leaders in charge of the impacted organizations. The method of alternative histories outlined in the paper is an attempt to offer a practical approach to giving plausible answers in relation to the future of disruptive technologies. This method is based on the philosophical stance that predicting the future in the sociotechnical realm with precision is often impossible. Instead, an organization can prepare itself for the future by brainstorming about various future possibilities in relation to a potentially disruptive technology. Identifying and analyzing these future scenarios can help an organization to become more informed and better prepared in relation to possible threats and opportunities created by various disruptive technologies.
References Bhaskar, R. (2008). A realist theory of science. New York, NY: Routledge. Carlson, W. B. (1994). Entrepreneurship in the early development of the telephone: How did William Orton and Gardiner Hubbard conceptualize this new technology? Business and Economic History, 23(2), 161e192. Christensen, C. M. (2013). The innovator’s dilemma: When new technologies cause great firms to fail. Boston, MA: Harvard Business Review Press. Christensen, C. M., Anthony, S. D., & Roth, E. A. (2004). Seeing what’s next: Using the theories of innovation to predict industry change. Boston, MA: Harvard Business School Press. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59e68. Kaplan, A. M., & Haenlein, M. (2016). Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster. Business Horizons, 59(4), 441e450. Kaplan, A. M., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15e25. Krotov, V. (2017). The internet of things and new business opportunities. Business Horizons, 60(6), 831e841. Krotov, V., & Junglas, I. (2008). RFID as a disruptive innovation. Journal of Theoretical and Applied Electronic Commerce Research, 3(2), 44e59. Krotov, V., & Silva, L. (2005). Case study research: Science or a literary genre?. Proceedings of the 2005 AMCIS 2005 Conference. Omaha, NE: American Conference on Information Systems. Kuhn, T. S. (2012). The structure of scientific revolutions (50th anniversary ed.). Chicago, IL: Chicago University Press. Lakatos, I. (1973). Science and pseudoscience [Radio talk transcript]. BBC. Available at: http://www.lse.ac.uk/
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Predicting the future of disruptive technologies: The method of alternative histories Vlad Krotov Arthur J. Bauernfeind College of Business, Murray State University, 109 Business Building, Murray, KY 42071, U.S.A.
KEYWORDS Managerial decision making; Management strategy; Market disruption; Disruptive technologies; Internet of things; Alternative histories
Abstract With digital technologies shaping competition in many industries, predicting the future of potentially disruptive technologies becomes an essential task of business leaders concerned with the survival and success of their organizations. Looking into the future of disruptive technologies requires a philosophical stance and a practical method that accommodates the inherent uncertainty and nonlinearity of the path of disruptive innovations. Unfortunately, much of the current thinking in relation to adoption and diffusion of innovations is rather linear and deterministic. This article proposes a set of philosophical principles, together with a practical brainstorming method, for glimpsing into the future of disruptive technologies. The method of alternative histories is based on the traditional brainstorming techniques and the philosophical ideas of Imre Lakatos, Roy Bhaskar, Bruno Latour, and Nassim Taleb. ª 2019 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
1. The problem of predicting the future of disruptive technologies Microsoft was famously founded on the vision that there would soon be a desktop computer in every home and on every worker’s desk, and that this new platform would disrupt the existing computing market (Lowe, 1998). This vision turned out to be correct and likely drove Microsoft’s unprecedented
early success. Predicting the future of important technological trends is an essential task of any business leader (Christensen, Anthony, & Roth, 2004). Being able to foresee how a particular emerging technology might change the competitive landscape within an industry can help an organization protect its competitive advantage from threats or use the technology to strengthen its position in the market (Christensen, 2013). Failure to see how a potentially disruptive technology may evolve in the future can diminish
E-mail address: [email protected] https://doi.org/10.1016/j.bushor.2019.07.003 0007-6813/ª 2019 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
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the competitiveness of an organization or even jeopardize its existence. The story of Western Union and the telephone is a famous example of how executive blindness to a potentially disruptive emerging technology can quickly diminish a company’s dominant position within an industry (Christensen et al., 2004). In 1875, Alexander Graham Belldan unknown inventor at the timedoffered to sell his telephone technology patent to William Orton, the President of Western Union Telegraph Company; the proposed price was $100,000, which would be roughly $2 million today (Carlson, 1994). William Orton, a talented engineer himself, certainly understood what the telephone was yet he thought it had little relevance to Western Union’s telegraph business. Telephone technology, including Bell’s, was unreliable at that time. It could transmit voice messages only over short distances (e.g., within city limits). Western Union’s business involved transmission of telegraph messages over long distances. Orton also thought that telephone, when used over the company’s existing telegraph lines, would overload its network and lower the quality of existing telegraph services. Because of these and many other considerations, Orton turned down Bell’s offer. When Bell made his offer to Orton, Western Union was one of the most dominant telecommunications companies in the world. Within a few years, contrary to Orton’s belief that telephone technology was not a direct substitute for the telegraph, it began to cut into Western Unions’ market share. In an attempt to catch up, Orton commissioned his engineers to develop Western Union’s own telephone technology but the window of opportunity was gone. Western Union’s telegraph business continued to vanish. Today, few people even remember Western Union as a telecommunications giant as it is more recently known for its global money transfer business. In defense of William Orton, looking into the future of a particular technology is often a daunting task. Given the inherent messiness of the sociotechnical realm surrounding most technologies, the future of a new technology can be hard to predict with certainty (Latour, 1987). Thinking Figure 1.
about the future of technologies requires a different philosophical foundation that accommodates this inherent uncertainty and allows one to think about the future of a technology in a nonlinear fashion. Unfortunately, much of the current theoretical tradition with respect to diffusion of innovation is inherently linear and does not accommodate this nondeterminism of technological innovations. This article is an attempt to propose a philosophical base, together with a practical method, that would allow business leaders to glimpse into the future of disruptive technological innovations.
2. Sustaining and disruptive innovations There are numerous theories on the adoption and diffusion of technological innovations (e.g., see Moore, 2009; Rogers, 2010) that are inherently deterministic and linear in nature. These theories make the following problematic assumptions: 1) any technological innovation will go through a certain sequence of adoption phases and 2) the technology and its features will freeze along the dimensions traditionally valued by existing customers, unable to make any lateral moves from one customer segment to another. Looking at Rogers’ (2010) influential theory of diffusion of innovation, it assumes that an individual will come to adopt an innovation via a linear decision process comprised of five stages (see Figure 1). Each of the decision phases are briefly explained in Table 1. A similar linear pattern is evident in the diffusion of the innovation process at the market level (see Figure 2). According to Rogers (2010), consumers adopt an innovation in groups. The first group to adopt an innovation is made up of socalled innovators, which make up only 2.5% of all consumers. Innovators are followed by slightly more cautious early adopters, which comprise 13.5% of the total market. The early majority and late majority groups each comprise 34% of consumers. The last group to adopt an innovation are the laggards, covering approximately 16% of the total market.
The five stages in the innovation adoption process
Source: Adapted from Rogers (2010)
Predicting the future of disruptive technologies Table 1.
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Innovation adoption decision stages
Knowledge
The individual is first exposed to an innovation
Persuasion
The individual becomes interested in innovation and actively seeks additional information about the information
Decision
The individual weighs all the cons and pros of adopting an innovation and decides either to accept it or reject and abandon the innovation
Implementation
The individual actually uses the adopted innovation in a specific context and gains additional information about the cons and pros of the innovation
Confirmation
The individual finalizes his or her opinion on the innovation and makes a final decision on whether to use the innovation further or not
Figure 2.
Diffusion of innovation among consumers
Source: Adapted from Rogers (2010)
This linear theoretical tradition (Rogers, 2010) may be applicable to sustaining technological innovations (Christensen et al., 2004). Sustaining innovations are new products or technologies that offer incremental improvements in value propositions of existing products or services (Christensen et al., 2004). Sustaining innovations first meet the needs of customers with low or very basic expectations regarding a product attached to the innovation (see Figure 3). Sustaining innovations introduce better features that not only meet the needs of customers with low expectations but also customers with higher expectations. A product may overshoot the needs of the low expectations group in terms of the price and features, creating an opportunity for a low-end disruption: a new product targeting the same group of consumers based on a set of slightly different features or technologies. Gillette is a good example of sustaining innovation. Every few years or so, Gillette introduces new shaving products that have more razor blades
for a smoother and cleaner shave. These features are familiar to and valued by existing consumers. In the future, Gillette may introduce a shaving product that is simply too sophisticated for lowend customers (e.g., a product with too many razor blades), which will create an opportunity for a more basic product with just one blade enclosed in cheap plastic (e.g., single blade disposable shavers by Bic). While offering a shaving experience that is less smooth, the product may meet low expectations and offer an attractive price. This may constitute the so-called low-end disruption to the existing market for Gillette’s shaving products but it is not a true disruptive innovation since it is based on similar technologies that offer similar value for the same group of customers. This linear thinking about diffusion of innovation is hardly applicable to disruptive technological innovations. Disruptive innovations often start out weak. They tend to have low reliability and questionable value in the early stages; it is not clear whether this technology will ever become reliable
4 Figure 3.
V. Krotov Sustaining and disruptive innovations
Source: Adapted from Christensen et al. (2004)
enough to be commercialized and what will be the main target market (Christensen et al., 2004). The adoption path of disruptive innovations is often nonlinear and subject to serendipity, since disruptive technology often finds acceptance and applications in industries very far from the domain where they were created (Krotov & Junglas, 2008). A hypothetical example of a product that follows a disruptive innovation path is a cream for body hair removal (see Figure 3). This cream may start out in a totally different market, offering a relatively pain-free and razor-free solution to hair removal. Perhaps these consumers do not want to use razor blades because they irritate the skin or because they find the shaving process tedious. Thus, disruptive innovations often emerge in a slightly different market and, at first, do not compete with an existing product (e.g., shaving products by Gillette or Bic). Instead, they may simply attract nonconsumers. Moreover, they may start out low on quality, meeting only the needs of customers with modest expectations. Eventually the hair removal cream technology may mature enough that it can compete directly with existing shaving products. With some improvements in features and marketing, the hair removal cream technology can make an unexpected lateral move into the market in which shaving products based on razor blades dominate. This will constitute the emergence of a new, disruptive technology in the
market. With time, this disruptive technology can overtake the market for shaving products, making steel razor blades an obsolete technology. This can happen, for instance, if applying a cream to one’s face eliminates facial hair for several weeks or even permanently. Thinking about the future of disruptive technologies requires a nondeterministic and nonlinear approach. Just because a particular technology is currently not on a diffusion and adoption path to intercept an existing market, this does not mean the technology will not make a lateral move and become a serious competitive threat in the future. While the theory of disruptive innovation (Christensen et al., 2004) outlined here does a great job explaining what disruptive innovations are, it offers little guidance on how to analyze emerging disruptive technologies, especially when it comes to predicting their possible future adoption paths.
3. Predicting the future as the ultimate proof of knowledge Being able to predict the future of technological trends is not only of practical importance for entrepreneurs and business leaders, but it also constitutes the ultimate proof in professional fields, religion, and the postpositivist philosophy of science. We expect experts to predict future
Predicting the future of disruptive technologies phenomena within their area of expertise (Krotov & Silva, 2005). The public expects economists to predict macroeconomic conditions not only in the nearest future but also in the long term. Stock analysts routinely predict where the stock market will go in the future to prove their understanding of the stock market. A similar approach to evaluating truthfulness of knowledge has been used in Abrahamic religions for millennia. In Christianity, predictions in relation to the Messiahda savior or liberator sent by Goddfound in the Old Testament and later fulfilled by the life of Jesus Christ described in the New Testament are used as one of the most important proofs of the legitimacy and knowledge of God’s will of the prophets of the Old Testament. Jesus Christ’s own predictions in relation to his crucifixion and resurrection are also used as important proofs of Jesus Christ being God’s most important messenger. Although science often tries to distance itself from business practice and religion, the same line of thinking is used by philosophers of science to delineate science from pseudoscience. Imre Lakatos (1995)done of the most well-known postpositivist philosophers of sciencedrejected authority, one of the oldest forms of supporting validity of knowledge, as a criteria for distinguishing scientific knowledge from a pseudoscientific one. Human history is full of examples of seemingly authoritative people and organizations rejecting what later turned out to be a fact proven beyond any doubt (e.g., the fact that the Earth rotates around the Sun). Lakatos found Popper’s (1965) falsification principles to be naive as well, as they can lead to a seemingly absurd situation in which the probability of all scientific theories is zero. Indeed, according to Popper, a theory must be falsified in order to be accepted as scientific. Thomas Kuhn’s (2012) pragmatic approach to sciencedin which a theory constitutes scientific knowledge when a sufficient number of scientists believe in itddoes not satisfy Lakatos either as this may equate science with witchcraft and astrology. According to Lakatos (1973), it is its ability to predict stunning new facts that distinguishes science from pseudoscience (Lakatos, 1973): But all the research programs I admire have one characteristic in common. They all predict novel facts, facts which had been either undreamt of, or have indeed been contradicted by previous or rival programs.. Halley, working in Newton’s program, calculated on the basis of observing a brief stretch of a
5 comet’s path that it would return in seventytwo years’ time; he calculated to the minute when it would be seen again at a well-defined point of the sky. Addressing the problem of predictability of future technological trends is not just of great practical value but also a way to strengthen the position of a field of inquiry or professional practice as scientific and producing true knowledge.
4. The philosophical underpinnings of the method of alternative histories Lakatos’ demarcation principle may be unrealistic when applied to the inherently messy and unpredictable social realm. Unlike physicists, who often strive to develop nearly 100% deterministic models and theories, social scientists seek to discover social tendencies that are probabilistic in nature. According to Bhaskar (2008), modern social sciences cannot fully adhere to the empirical, experimental nature of positivism. While social scientists can formulate theories that resemble natural laws under the positivist tradition, they may not be verifiable using the experimental activities of the natural sciences. The problem is that various combinations of events may intervene with the exact sequence of events needed to prove a particular social theory in a positivist sense. Thus, social theories may or may not materialize in the real world. Therefore, no matter how much a particular person knows about the world of business or the broader sociotechnical environment, this knowledge can only lead to a future possibility and not an absolute certainty. Thus, all social theories are probabilistic in nature. So what can be done given the inherent uncertainty of the social realm? Nassim Taleb, an influential modern author, investor, and a philosopher of science, proposed one approach in his book Fooled by Randomness. He discussed the philosophical foundation for a practical method of thinking about the future of disruptive technologies. This method is allegorically delivered through what appears to be a semifictional image of Taleb’s own boss during his days as a trader in New York (Taleb, 2004, pp. 32e33): I am still amazed at this flamboyant man’s obsession with risks, which he constantly played in his headdhe literally thought of everything that could possibly happen. He forced me to make an alternative plan should a plane crash into the office building and fumed at my answer that the financial
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Figure 4.
The method of alternative histories
condition of his department would be of small interest to me in such circumstances. This approach to thinking about future possibilities proposed by Taleb (2004) allows for nonlinear, nondeterministic thinking. Instead of trying to imagine one linear scenario of how a particular technology may evolve, one should envision the entire sociotechnical network within which a particular technological trend is embedded (Latour, 2005). This network is comprised of a complex web of human and nonhuman actors, material objects, processes, concepts, and many other relevant factors and social forces that shape how technology is developed and used within society (Latour, 2005). The various possibilities of all these diverse actors coming into interaction with each other are quite numerous so the goal of this line of thinking is not to determine which particular scenario is more probable, but rather to envision these scenarios and analyze their impact on an organization.
5. The method of alternative histories Drawing on the ideas of Lakatos (1973), Bhaskar (2008), Latour (2005), and Taleb (2004), this article proposes a practical method for brainstorming the future of disruptive technological innovations. The method of alternative histories involves brainstorming about various future possibilities that can unfold in relation to a particular disruptive technology. The method is similar to the traditional brainstorming method in a sense that it thrives to create an environment in which a diverse group of experts can build upon other’s ideas to produce innovative thinking in relation to a business problem. In contrast to the traditional method, it is iterative in nature, somewhat less structured, and explicitly focused on generating ideas in relation to possible threats and
opportunities created by the sociotechnical realm surrounding technologies. The phases comprising the method are provided in Figure 4. Phases 1 and 2 include assembling a diverse team and scheduling a brainstorming session. Note that after familiarizing the team with the key assumptions of this brainstorming method, the brainstorming session should proceed in an iterative fashion; new actors can be added to the list at any point. These new actors can spark ideas about additional future scenarios. Overall, although Figure 4 may imply a degree of linearity, the team should feel free to move between these phases as needed, indicated by the arrows pointing in both directions. For example, new rules can be created during the brainstorm session and new members can be added to the team during the exercise. The main goal of this brainstorming method is not to follow a particular protocol rigorously but rather to come up with a good list of possible future scenarios in relation to a potentially disruptive technology and assess their conceivable impact an organization. All of this is needed to stimulate creative, nonlinear thinking in relation to a potentially disruptive technology. The phases comprising this brainstorming method are discussed in more detail in Sections 5.1.e5.7. I provide examples in relation to the Internet of Things (IoT)da potentially disruptive technology across many industriesdfor each of these steps. IoT is defined here as a ubiquitous network connecting together various nodes that belong to the technological, physical, and broad socioeconomic environments (Krotov, 2017). These examples assume that the brainstorming session in relation to IoT is organized by a fictional company called Agrisoft Solutions, LLC. Agrisoft develops and sells software applications that assist farmers with land and crop management. LandBase is the company’s flagship product. All of the firm’s other software and hardware products provide technical
Predicting the future of disruptive technologies solutions that support the functions of LandBase. LandBase provides farmers with the following information: United States Environment Protection Agency (EPA) approved labels for agricultural products; Various regulatory documents in relation to crops; Information on crop protections products and their active ingredients; and Harvesting times and intervals for various crops in various hardiness zones. Agrisoft’s top management has become concerned that the farming business increasingly is driven by information and communication technologies (ICTs). Specifically, IoT solutions are becoming increasingly popular in agriculture. They are based on sensors and ubiquitous networks designed to monitor the soil, external environment, farming products, farming equipment, and people involved in the growing of crops. The company wants to brainstorm the future threats and opportunities that IoT can present their organizations with in the future.
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5.2. Phase 2: Organize a brainstorming session After potential participants in the brainstorming session are identified, the facilitator should set up a brainstorming session. The session should be scheduled in a room in which the needed number of participants can fit comfortably. The room should be equipped either with an overhead projector with a projection screen or a physical whiteboard. Both can be used for showing the needed information to the participants. This information can include a list of potential human and nonhuman actors that can come into play to create future scenarios in relation to a particular disruptive technology. The list of identified future scenarios together with their potential impact on the organization in question can be shown to participants as well. For example, Agrisoft can schedule the session in a videoconferencing room used for meeting with colleagues and clients in remote locations. The room should have ample seating capacity and contain either a whiteboard or an overhead projector with a screen for displaying information for everyone in the room.
5.3. Phase 3: Familiarize the team with the method
5.1. Phase 1: Assemble a diverse team First, identify a group of experts that can participate in this brainstorming exercise. In order to ensure a diversity of opinions and ideas about a disruptive technology of interests, the group should be comprised of participants with diverse educational and professional backgrounds. The group is headed by a facilitator that will moderate the discussions and manage the brainstorming sessions in the subsequent phases. In the case of Agrisoft, invited experts can include technology specialists from the company, top management representatives, various hardware and software vendors that the company is collaborating with when delivering its software solutions to farmers, and farmers. In addition, the company can invite academics from a nearby university in fields like technology and business. Representatives of various nonprofits such as consumer privacy groups can be invited as well in order to provide a broader legal and social perspective on the technology in question. A diverse team can help spark creativity and nonlinear thinking in relation to IoT and its future impact on agricultural business. A formal facilitator from Agrisoft should be appointed for this exercise to moderate these discussions.
Before starting a session, the facilitator should make the participants aware of the key values, assumptions, and phases of the brainstorming method. First, the facilitator should explain the main purpose of this exercise to the session’s participants. Second, the facilitator should make the participants comfortable with the fact that predicting the future of a technology with high certainty is challenging, if not impossible. The goal of the exercise is to generate future possibilities and not to predict which will actually materialize; no possibility should be ruled out as impossible or ridiculous. This explanation is central to the philosophical assumptions behind the method and, therefore, should be clearly conveyed to the session’s participants. Third, the facilitator should also explain each phase to the participants, including: (1) generating a list of human and nonhuman actors in relation to the disruptive technology, (2) enumerating all of the ways in which these actors can come into play and influence the future of disruptive technologies, and (3) assessing the impact of these future possibilities on the organization in question. The facilitator should explain to the participants that they can switch between these phases at will or propose adding additional experts to the session (e.g., via videoconferencing) to stimulate creative, nonlinear thinking.
8 In the case of Agrisoft, an appointed facilitator can explain to the participants that the company’s management is becoming increasingly concerned with the question of how the IoT can affect the agriculture business, the agricultural software industry, and their organization in particular. Thus, the main goal of the meeting is to brainstorm about the possible future threats and opportunities IoT can create for the organization. The facilitator should explain to the participants that (1) no idea should be dismissed as ridiculous; (2) the participants should envision all relevant human and nonhuman actors that are likely to impact the future of IoT and its impact on the business of Agrisoft; (3) the positive and negative impact of these individual scenarios should be assessed; and (4) the session’s participants can switch between phases if necessary (e.g., by inviting other experts to the session or by introducing additional human and nonhuman actors to the list).
5.4. Phase 4: Envision human and nonhuman actors The brainstorming exercise should start with the group of experts envisioning the sociotechnical network comprised of material actors and nonmaterial concepts surrounding a particular technological trend (Latour, 2005). This is consistent with the philosophical assumption that a complex network of technical and social factors often influences adoption paths of disruptive innovations. To spark the generation of ideas, the facilitator can ask the group the following question: What are the things or concepts that come to your mind when you think about the IoT concept in agriculture? Each idea or human or nonhuman actor related to IoT use in agriculture can be captured with the help of a sticky note. Each note can then be attached to a white board in front of the audience. Alternatively, a software program can be used for creating notes and displaying them via a projector on a screen (e.g., Trello). An example of a list of human and nonhuman actors that Agrisoft can come up with in relation to the IoT is presented in Figure 5. The list can be expanded as the group of experts brainstorms about possible future scenarios that can be created via the interplay among the actors and assesses impact of these scenarios on the company’s business.
5.5. Phase 5: Brainstorm about future scenarios After envisioning the relevant actors, the participants should brainstorm about future possibilities
V. Krotov in relation to a potentially disruptive technology by connecting the dots among the human and nonmaterial actors listed. To spark brainstorming, the facilitator can ask: How can these actors possibly interact with each other and how will these interactions influence the adoption and diffusion of the technology in question? Based on the list of actors depicted in Figure 5, the brainstorming session’s participants can generate the following scenarios or alternative histories relevant to Agrisoft’s business: Consumer privacy groups advocate for a more stringent protection of data collected with the help of agricultural software applications; The Food and Drug Administration requires all farm products to be tagged with Radio Frequency Identification (RFID) transmitters; and Growing demand for IoT software applications for monitoring soil and atmospheric conditions using wireless sensors
5.6. Phase 6: Assess the potential impact of each scenario Each possibility, or alternative history, generated in brainstorming session should be discussed from the perspective of its potential impact on an organization or industry of interest. The facilitator can ask the following questions: Are we, as an organization, prepared for this particular scenario? Or, what are the opportunities or threats if this particular scenario materializes? At any time during this exercise, the facilitator should encourage the participants to bring in the so-called black swans: highly unlikely and often unimaginable events that, nevertheless, might occur and may have a profound impact on how a particular phenomenon evolves (Taleb, 2008). The results of this analysis in relation to specific scenarios can be captured using a table. For example, Afrisoft may come up with the analysis of the three sample scenarios provided in Table 2.
5.7. The practical goals of the brainstorming method The organization’s main goal for this brainstorming method is improved awareness regarding the various possibilities associated with a potentially disruptive technology. Being aware of these scenarios can expand the horizons of top management when creating broad strategies aimed to exploit
Predicting the future of disruptive technologies Figure 5.
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Human and nonhuman actors in relation to the IoT
opportunities and protect the organization from threats of a potentially disruptive technology. With this awareness, it is less likely that the organization will be caught off guard by a potential threat to its market share or miss some important opportunities. Technology specialists can also become better prepared for the future developments on the technological front. Perhaps, senior technology leaders should consider investing in the development of additional technical
expertise in relation to IoT so the company can rapidly expand and improve the functionality of its existing products.
6. Forewarned is forearmed Today, the sword of potentially disruptive technologies is hanging over existing business models in many industries. Social media is replacing the
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Table 2.
Analyzing threats and opportunities created by future scenarios Threat
Opportunity
Consumer privacy groups advocate for a more stringent protection of data collected with the help of agricultural software applications
Additional investment are required for improving protection of data generated by Agrisoft software products and complying with possibly more stringent consumer privacy legislature
Agrisoft can use data security and legislature compliance as a differentiating point for its software products
The Food and Drug Administration requires all farm products to be tagged with Radio Frequency Identification (RFID) transmitters
Agrisoft does not have a solution for RFID tagging; this can put the company behind the competition
Agrisoft can come up with a line of software and hardware products for tagging food items with RFID tags and integrating this data with existing information systems used by stores and farmers
Growing demand for IoT software applications for monitoring soil and atmospheric conditions using wireless sensors
The flagship product of Agrisoft, LandBase, does not provide information support in relation to soul and atmospheric conditions
Agrisoft can expand the list of informational products supplied together with LandBase information system marketed to farmers; this can be another differentiating point in the ear of the Internet of Things
Scenario
traditional news media and advertising channels (Kaplan & Haenlein, 2010). The rise of massive open online courses, or MOOCs, is a worrisome trend for many traditional brick-and-mortar universities (Kaplan & Haenlein, 2016). Artificial intelligence is another important technological development that threatens service industries and jobs that once were thought to be immune to automation (Kaplan & Haenlein, 2019). Organizations that are driven by supply chains are also in need for guidance on how IoT may transform their industries and business models (Krotov, 2017). Many questions in relation to the future of these disruptive technologies as well as their competitive impact on industries and individual organizations are still open and require answers from the leaders in charge of the impacted organizations. The method of alternative histories outlined in the paper is an attempt to offer a practical approach to giving plausible answers in relation to the future of disruptive technologies. This method is based on the philosophical stance that predicting the future in the sociotechnical realm with precision is often impossible. Instead, an organization can prepare itself for the future by brainstorming about various future possibilities in relation to a potentially disruptive technology. Identifying and analyzing these future scenarios can help an organization to become more informed and better prepared in relation to possible threats and opportunities created by various disruptive technologies.
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