Biohacking refers to the use of technology and science to manipulate and control biological systems, often with the goal of improving human health and performance. This can include a wide range of activities, such as using genetic engineering to modify DNA, using brain-computer interfaces to enhance cognitive abilities, and using wearable devices to track and monitor biological functions. Biohacking is a growing field that is helping to advance our understanding of biology and to develop new and innovative ways to improve human health and wellbeing.
Biohacking or body hacking is the practice of placing RFID chip implants, sensors, magnets and other technological implants under the skin. A subgroup of biohackers, called molinders, goes so far as to implant devices such as computer chips in their bodies. Implants allow them to do everything from opening doors without a key fob to controlling their glucose levels subcutaneously. An official website of the United States government.
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Before sharing confidential information, make sure you're on a federal government site. Wearable technology is the leading path to integrated technology, that is,. Researchers have widely used the Technology Acceptance Model (TAM) to explain the factors that influence the adoption of almost all technological innovations to date. Integrated technology, often referred to as biohacking, presents a unique set of factors that require a new revision of the model.
Using the theory of the diffusion of innovations, self-efficacy and social exchange, a revision of the model of acceptance of technology is proposed with additional factors such as age and gender, the self-efficacy of integrated technology, perceived risk and privacy problems to explain the adoption of technologies integrated into the human body. The data was collected through an online survey (N %3D 106) conducted with a Qualtrics panel and the results suggest that age, gender, perceived utility, perceived ease of use, self-efficacy of integrated technology, self-efficacy, and risk and privacy issues influence the adoption of integrated technology. Implications are drawn for the implant industry, policy makers and researchers interested in this technology. Many see biohacking as a natural progression and an extension of modern innovations, such as portable technologies.
Wearable technology is commonly used to track health and fitness, among other things, a phenomenon commonly referred to as the quantified self (Wolf, 200). . However, the true value of portable devices for companies that create this type of technology is the availability of abundant personal data that allows advertisers to personalize messages and target users with exactly what they need and when they need it (Saxena, 2013). As wearable technology becomes increasingly popular and becomes integrated technology, the nature and complexity of the information and data now available to implant manufacturers make it possible for much more sophisticated, personalized and contextualized advertising, increasing risks and privacy issues.
among users. For example, Fitbit wearable technology is at the forefront of the biostamp, a thinner electronic mesh that stretches and moves with the skin and monitors several important body functions, such as temperature, hydration and stress (Adrian, 201). Also known as electronic tattoos, biosemals can be used to monitor people wirelessly and collect information from their skin on an ongoing basis as they go about their daily lives. This information could be automatically sent to your smartphone or to the cloud, similar to how Fitbit does (Adrian, 201).
This takes the movement of the quantified self to a whole new level and creates all kinds of concerns for potential users. Technology manufacturers can use this highly personalized data to send contextual promotional messages to users. For example, a message to hydrate with a specific drink after a 6-mile run can be sent to the user's phone as an automatic notification along with their statistics on body temperature and hydration status. Integrated technologies are not a dystopian future, but are, in fact, already part of our current lives, as we increasingly rely on contact lenses, hearing aids, smartphones, pacemakers, bionic knees and other embedded implants (Ricker, 201).
Technology already exists to implant RFID chips in people that can serve as entrance keys to apartments, ready-to-use payment systems, public transportation cards and cards containing medical and personal information, such as passwords, allergies, and DNR information. In fact, Three Square Market was the first American company to implant microchips for its employees that allowed them to enter the building, log in to their computers and even buy snacks, all with the movement of the hand (Francis and Jarvis, 201. The list of innovations in this area is endless, with bionic eyes, which are telescopic lenses with the ability to zoom in and out with blinks and night vision capability (Engelking, 201, brain control interfaces (BCI) for controlling drones and tweeting using EEG ( Szondy, 201), designing babies with genetic editing and 3D printed organ transplants (Murgia, 201. Before reviewing the literature on the various factors that influence the adoption of new technologies in general, we must examine the various ways in which embedded implants are similar and different from existing technologies, in particular wearable technology). Embedded implants are similar to wearable technology, in that both are designed to facilitate the real-time monitoring of individual functions in the areas of fitness and health. In this sense, some of the factors that influence the adoption of portable technology, as identified in Chuah et al.
The visibility factor that proved to be significant in predicting the adoption of wearable technology (Chuah et al. Another difference is that the perceived ease of use can be further investigated in the case of integrated technology, such as the self-efficacy of the use of such technologies, since these technologies are new and somewhat more complex to use than mobile phones or smartwatches. Then, although they were not examined by Chuah et al. These differences at the individual level can be expected to be more prominent in the adoption of integrated technology.
Embedded technology also differs from wearable technology in that there is a certain risk associated with the adoption and use of implants. Unlike portable devices, integrated technology involves deeper interaction with the human body, either through surgery or through implants, such as RFID chips found under the skin. Second, embedded technology has the capacity and potential to collect more biopersonal data than wearable technology, making privacy concerns more critical. For example, a person can choose to remove a smartwatch at any time, but once an implant embedded inside the human body is placed, it's difficult, if not impossible, to leave it temporarily at home so as not to track its location.
Proposed model for the adoption of integrated technology. One of the most widely applied and cited theories on the adoption of technology is the Technology Acceptance Model (TAM). The Technology Acceptance Model (TAM) posits that people use technology if it is easy to use and offers many benefits (Davis, 1989; Davis et al. These two factors, perceived ease of use (PEU) and perceived utility (PU) explain why we adopt different types of technologies, such as information systems, to improve our quality of life (Legris et al.
King and He (200), in a meta-analysis of 88 published studies on TAM applied in various fields, found that the model was solid and valid, with potential for greater applicability. Although the model has been extensively modified and expanded to include a number of additional factors, some of which will be revised and added to our own model in the following sections, the two factors of the original model, the PEU and the PU, remain to date one of the strongest predictors of most technologies in use today. Also in the case of integrated technologies, these two factors can be expected to play an important role. For a person to get embedded implants, they would have to: a) see some use in those implants and (b) feel very comfortable using or managing such technology.
As noted in the introduction, integrated technology can be used to closely monitor various body functions (diet, sleep and exercise) in order to improve performance and productivity. Unlike technologies, people need to spend a large amount of time learning to use them (p. e.g. And the perceived utility in terms of increased productivity, efficiency and quality of life should also increase their adoption rates.
Consider the case of a Utah-based vendor, Rich Lee, who had sound-transmitting magnets implanted in his ears because he wanted to be able to develop echolocation to correct his vision problems (Arthur, 201). After losing a significant amount of vision in his right eye, his doctors told him that there was a possibility that something similar could also happen to his left eye and eventually leave him legally blind. Rich made the decision to implant the magnets in his ears in the hope of later connecting them to an ultrasonic rangefinder that would allow him to echolocate himself and survive if he lost his vision completely. This illustrates a case with a high level of perceived utility and relatively high ease of use, both of which have probably increased the adoption of such implants in the human body.
Based on the previous discussion, embedded technologies offer a unique set of useful features and are designed to be relatively easy to use, which should predict their popularity and adoption among users. The perceived usefulness of integrated technologies will have a positive impact on people's adoption of such technologies. The apparent ease of use of embedded technologies will have a positive impact on people's adoption of such technologies. Luarn and Lin (200) investigated the role of perceived self-efficacy in the behavioral intention to use mobile banking and found that it had a significant positive impact on behavioral intentions.
Sánchez and Hueros (20) expanded the TAM to include technical support and perceived self-efficacy, and found that both had a positive effect on the use of an online teaching platform called Moodle. Similarly, people's adoption and use of integrated technology should depend on their perceptions and beliefs about how well they believe they are capable of using such technologies to achieve the objectives associated with them. This is different from perceived ease of use (PEU), which is based on technology itself and its features, rather than on individual skills. Based on the general concept of self-efficacy in Bandura (1998) and the extension of Eastin and LaRose (2000) to the Internet in terms of self-efficacy on the Internet, we use the elements at both scales to adapt to integrated technology.
We call this self-efficacy of integrated technology (ETSE) and we define it as the judgment of an individual about their ability to perform the functions necessary to operate and manage integrated technologies in order to achieve a set of objectives or goals. Based on research on Internet self-efficacy and self-efficacy, the self-efficacy of integrated technology (ETSE) should also have a major influence on how people adopt and use such technologies. Resisting new technologies or experiencing anxiety when using them are traits usually shown by people who question their technical self-efficacy or, in other words, their ability to use technological tools constructively to achieve certain goals in their lives. For example, Iivari (199) discovered that people who considered computers to be too complicated rarely felt motivated to learn them, since they assumed that they would not be able to use them well enough to achieve their goals.
It's also important how we understand the concept of skill itself. Some people consider that the ability to be fluid depends on experience and is malleable. These people accept challenges that help them learn and are not afraid of making mistakes. Failure is considered a stepping stone to greater learning.
Others are convinced that capacity is inherent and not much can be done to change it. As a result of this belief, they prefer not to engage in activities in which they could perform poorly and, consequently, lose opportunities to learn from their failures (Bandura, 199). Regardless of whether their abilities are fluid or not, people's beliefs in their own abilities to be able to execute and operate implants embedded in their bodies without concern or anxiety should have a positive impact on their adoption and ultimate use of such technologies. Therefore, people's level of self-efficacy of integrated technology (ETSE) will positively influence their adoption of integrated technologies.
Within the diffusion of innovations (and technology) there are certain differences at the individual level. For example, in their longitudinal study of gender differences in the field of technology adoption, researchers Venkatesh et al. They found that women place more emphasis on the process followed to achieve a goal compared to men, who focus more on the outcome (Venkatesh et al. Social pressure to show certain behaviors or not also played an important role in women's inclination to adopt new technologies.
As a result of their results-oriented approach and willingness to take risks to achieve their goals (Morrongiello and Rennie, 199), we found that men tend to adopt technology early, more so than women. Early and constant exposure to new technologies, in turn, stimulates men's technical prowess, making them more comfortable with technology compared to women. In his 1995 study on gender differences in self-efficacy and attitudes toward computers, Busch found that men were more likely to be exposed early to computers, computer games and programming compared to their female counterparts (Busch, 199). Research also points to gender differences in the ability to take risks that stem from the way boys and girls are educated to think about failure.
Morrongiello and Rennie (199) point out that boys are more inclined than girls to take risks, since boys attribute failure to bad luck more often and do not take it personally, unlike girls, who tend to internalize failures and approach risks with more caution. Similar research has confirmed that women tend not to behave as competitively as their male counterparts and tend to risk very little to meet their goals (Lindquist and Save-Soderbergh, 201). This serves as a basis to explain why more men tend to venture and succeed in the area of technology, which expects to take a certain risk, be open to failure and the ability to learn from it. In the same way, it has also been said that a person's age determines the amount of technological tools they use and the ease with which they will use integrated technology in the future.
Younger men and women are exposed to the web, the Internet and computers at a younger age compared to older generations. As a result, older and middle-aged adults experience lower levels of comfort and more anxiety when manipulating technology (Czaja et al. It has been discovered that attitude towards technology and adoption behavior are closely related to the perceived usefulness of learning new technologies. Younger employees place much more emphasis on extrinsic rewards, such as promotions and bonuses, and since learning new technologies is essential today to advance their careers, they are more likely to have the attitude of wanting to increase their interaction with the technology that surrounds them, which could help them achieve their work goals (Czaja et al.
Age will negatively influence people's adoption of integrated technologies. Gender will predict people's adoption of integrated technologies, so men are more likely to adopt such technology than women. Privacy concerns play an important role in deciding how much personal information people disclose on these sites. In a study conducted to measure the relationship between the amount of social interaction on social media sites and the level of privacy offered, it was observed that there was a very strong direct relationship between the magnitude of the information that people voluntarily shared and the trust that the social network and its members would not misuse that information (Dwyer et al.
If this reasoning applies to embedded technologies, it goes without saying that companies that offer privacy protection and have strict policies that prohibit the misuse of users' personal information will attract the largest number of volunteer users who will share their information in exchange for the benefits offered by integrated implants. The level of perceived risk associated with embedded technologies will negatively influence people's adoption of such technologies. The level of privacy concern associated with embedded technologies will negatively influence people's adoption of such technologies. People's attitudes toward integrated technology will predict their adoption of such technologies.
All the scales used in the survey were tested for reliability using Cronbach's alpha, except the PU (mean: %3D); and the PEU (average): %3D (average) 2.9, which were measured as individual Likert elements. The following Table 1 lists the measures of the scales, means, variance and reliability. Cronbach mean, variance and alpha coefficients. To test hypotheses 1, 2, 3, 5, 6 and 7, two series of linear regressions with people's attitudes towards integrated technology and the willingness to put integrated implants as dependent variables and (a) the perceived use of integrated technology, (b) the perceived ease of use of integrated technology, (c) the self-efficacy of integrated technology, (d) age, (e) perceived risk and (f) privacy issues, according to the six independent variables.
Hypothesis 4 implied that gender predicted adoption and that gender was dichotomous was not included in the regression. It was thought that people's likelihood of adoption was a product of both their attitudes toward technology and their behavioral intentions. Therefore, two sets of regressions were performed with the same set of predictors. The following tables 2 and 33 summarize the values of the R-square, the collinearity statistics, and the estimates of the predictor coefficients of both regressions.
It can be concluded that the adoption of integrated technology by people is positively affected by its perceived usefulness (H), its perceived ease of use (H) and their level of self-efficacy of the integrated technology (H), and is negatively affected by their age (H), their level of perceived risk associated with embedded technology (H) and their level of concern for privacy (H). Therefore, regression analyses support all six hypotheses. T-Test with gender as a grouping variable. The results of our study show that factors that influence the adoption of integrated technologies include the perceived usefulness of such technologies, the perceived ease of use, the individual level of self-efficacy of the integrated technology, gender, age, risk and privacy issues associated with such technologies.
More precisely, the more people see the usefulness of these technologies and the more they perceive their ease of use, the more likely they are to adopt and use such technology. In addition, men and younger consumers are more likely to have favorable attitudes toward integrated technology and are more willing to get integrated implants than women and older consumers. Similarly, people with high levels of self-efficacy from embedded technology are also more likely to adopt these technologies. And finally, the two obstacles to the adoption of integrated technology are the risk and privacy problems associated with these technologies, which have a negative impact on attitudes and the willingness to get implants.
In all cases, consumers' favorable attitudes toward integrated technology are positively linked to their likelihood of receiving biological implants. The current study, being exploratory in nature, has numerous limitations. These include the collection of data through an online survey, which could be argued to have biased the sample towards a more tech-savvy population, although the sample very closely represents the composition of the United States. UU.
Population in terms of age, sex, income, education and race. Another limitation is the measurement of perceived use (PU) and perceived ease of use (PEU), which were included in the survey as single-element measures. Although this somewhat limits the reliability of these measures, both the PU and the PEU have been extensively related to the acceptance of the technology, so they were not the main objective of the current study. The study used a measure of behavioral intentions rather than actual adoption and use.
While several studies show that intentions are a very good indicator of actual behavior, they are only indicators and not actual behavior itself. Next, the low reliability index of the “willingness to put embedded implants” scale (alpha %3D). Finally, given the exploratory nature of our study, a fairly simplified set of statistical methods (i.e. Given the development in the field of statistical methods since the first introduction of TAM, a more appropriate approach would be the use of structural equation modeling (SEM).
Despite these limitations, given the new and emerging nature of integrated technology and the lack of research to understand its adoption, our study serves as a first step in expanding our knowledge of such technologies. Future studies should employ a more complete and comprehensive list of the background of adoption and employ more sophisticated statistical methods, such as SEM, to investigate both the background and consequences of the adoption and use of integrated technology. First, our results provide another way to expand the model of acceptance of technology. The TAM has been extensively revised in the past, but the unique set of features of integrated technologies presents another opportunity to modify this model.
Although it is true that it is smaller, the main theoretical contribution of the study comes from this modification of the original model. More specifically, incorporating perceived concerns about risk and privacy into the model brings us closer to a comprehensive model to explain the adoption of technology. Taken together, the factors in our model explain 50% of the variance in behavioral attitudes and intentions, that is,. Our study also combines several well-known and widely applied theories, such as the dissemination of innovations, self-efficacy, the theory of planned behavior and the theory of social exchange to TAM in the context of integrated technologies.
In addition, our study also provides empirical evidence of individual-level differences (age and gender) in these widely cited theories. The second set of practical implications of our findings relates to manufacturers of embedded implants and to brands that work with such companies. For example, the key to the successful adoption of integrated technology, as is the case with any technology, lies in its maximum usefulness and ease of use. As manufacturers can show end consumers the various uses and usefulness of integrated technology, the greater the chance that people will agree to adopt these technologies.
In the same vein, from the perspective of the dissemination of innovations, innovators and early adopters of integrated technology tend to be young people and men with high levels of self-efficacy. Our study provides empirical evidence for that. So, if chip makers are trying to market their products to these innovators and early adopters, attracting younger men with high technological self-efficacy could be the most efficient way to gain initial momentum. Finally, as manufacturers and brands work to make embedded implants widespread, our findings indicate that they must pay special attention to two enormous obstacles related to adoption, risk and privacy issues associated with these technologies.
As technology improves and it becomes safer to place implants, the dangers and risks associated with them should decrease. However, consumer concerns regarding the privacy and control of personal data are enormous, and the industry as a whole should work to create and implement some best practices that help gain consumer trust and make the adoption of integrated technology smoother. This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors. The authors declare that they have no conflict of interest.
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However, the best results of biohacking come from being well-informed and cautious about what works for your body. Whether you're using supplements, technologies such as red light therapy, or making changes to incorporate an abundance mindset, biohacking is meant to help you achieve positive and lasting change. The type of biohackers currently gaining the most notoriety are those who experiment outside laboratory spaces and traditional institutions on their own bodies in the hope of increasing their physical and cognitive performance. Even so, biohackers talk about making such important changes that the risks involved are also important.
You may know Dave Asprey, the self-proclaimed biohacker who popularized putting butter in coffee, or Wim Hof, The Iceman, who uses breathing to withstand the freezing temperatures of the Arctic. Biotechnologists also analyze experiments carried out by biohackers to guide biotechnological research. Biohacking your body means changing your chemistry and physiology through science and self-experimentation to increase energy and vitality. This biohacking supplement is intended to improve your overall health by providing additional support to your skeletal system.
Find out how people are using InnerAge to biohack their health and improve their longevity here. One word that Asprey likes to use a lot is “control”, and that kind of language is typical of many biohackers, who often talk about “optimizing” and “improving” their minds and bodies. .