The Addictive Smart Phone System
Copyright Alistair Davidson, 2018. All rights reserved.
The emerging trends of resistance to cell phone use for young children, combined with initial attempts by major cell phone manufacturers to deal with cell phone addiction raise new choices for companies providing addictive interactions with customers. Companies need to formally address the degree to which they provide an addictive service or interaction and consider tools for minimizing addiction.
Just as importantly employee education on what I might term a personal information literacy or how to minimize informational addiction is likely to benefit the individual employees and companies by reducing wasted time spent on smart phones. Companies have largely failed to manage email volumes and smartphone interactions will, if unmanaged, follow the same path.
Strategies for dealing with addiction are sometimes difficult to implement because they challenge shareholder value maximization and suggest that stakeholder issues will eventually place a constraint on profit maximization. However, well-off high tech firms, often mandate a concern for larger consequences, cf. Google “Do no evil.” So, any forward-thinking company needs to consider potential regulatory consequences of its strategy.
Strategies for minimizing addiction include:
- Tracking customer usage and reporting to customers on their time devoted to an activity, setting thresholds and perhaps even constraining total time spent on an activity.
- Analyzing addictive patterns of behavior with machine learning to score the addiction activity of customers. Children’s video game addictions fall into this area. A vendor might well constrain usage to a limited number of hours per day or particular time of day.
- Changes to products to encourage shorter investment in time on line. For example, a new site might have a selected collection of new stories that fall within a goal of X minutes per day for review. While total time may not be maximized, perceived value may increase for the user.
- Ad blocking and similar capabilities for measuring commercial activities aimed at users.
- Setting default assumptions on user information to automatically minimize information for advertisers and direct marketers in order to minimize commercial contacts by advertisers. The goal would be to offer fewer offering which are perceived as higher value by customers.
Overall, addiction represents a new frontier for company mission statements. Given the search for value creation that companies espouse, limits on low value addictive behavior may increase the perceived brand value of an offering. Return on time spent (ROTS) is a metric for tracking the value of customer interactions. Incorporating ROTS into missions is likely required to alter organizational goals to help provide metrics track user perception of time used, time wasted and value creation.
Many smart phone users check their phones dozens of times a day. They use their phones for checking SMS (short messaging service) notifications, email, music, audiobooks, for address and routing information, for access to retailer and restaurant web sites.
So, the first conclusion about phone use is that it has become a general purpose device, more computer than phone. But it’s also true that some of the behaviors engendered represent addictive behavior. Checking your email or SMS obsessively may make you feel up to date, but it also has two strong negatives:
- The distraction of checking your phone can interfere with regular and creative work.
- Many of the communications are actually commercial in nature, little different than advertising. They may encourage excessive spending and materialism.
|Usage Pattern||Addiction/Commercial Consequences|
|1. General purpose information access e.g. maps, directions||Episodic or intermittent||Few|
|2. Distraction from e.g. email, SMS or news addiction||Notifications independent of attention needs of user with external non-phone tasks||High potential of distraction.
High addiction possible.
|3. Commercial messaging or advertising||Easy to be overwhelmed by newsletters, subscriptions, spam and commercial messages||Requires active steps to avoid being compulsive about usage|
|4. Gaming||High addiction potential||Usage time constraints offer one method of limited addiction|
Addiction has been modeled by psychologists (e.g. B.F. Skinner) for years. Essentially, there are four major types of learning relevant to addiction: fixed interval, variable interval, fixed ratio and variable ratio reinforcement. Of the four, variable ratio reinforcement is the type that most creates addictive behavior. Over time, the number of events that precede reinforcement can be stretched out so that a user is reinforced less and less but the behavior is maintained. This stretching translates into more email and SMS messages checking and the negative effects of a corresponding increase in distraction and attention paid to a smart phone or computer.
This addiction model describes many of the behaviors that occur with information technology in general and smart phones in particular.
Sophisticated users, concerned about interruptions to their work and attention, creative users needing to focus their attention upon authoring or creation, or parents concerned about their children’s use of technology  have developed strategies for minimizing addiction and attention disruption. Parent’s concern is not unwarranted: one study of mobile apps identified that 95% have advertising targeted at young children. 
These approaches attempt to control access to their attention, and in particular, limits on child usage. Limits on when to look at contacting information, screen time limits, hours of operation have all been used to control access for both self and children. Employers have placed constraints upon usage to encourage a focus on work and increase in productivity. 
But it’s not clear these approaches work for most individuals. Addiction and the short term pay-off from saving money from sales promoted to the user are strong benefits to users and marketers. It’s also very hard to constrain employee usage as employees tend not to notice the negative productivity impacts of phone usage.
Educating users about Return on Time Spent is likely an emerging and potentially important approach to framing interactions with customers.
The Smart Phone System
Part of the issue is that hardware is not really the entire problem. Smart phones are used more than computers because they are always with the person. But generally, personal technology, whether personal computers, tablets or smart phones is part of an entire commercial system whose goal in life is to encourage individuals to:
- Subscribe to services, e.g. music services such as Spotify
- Access a huge array of news services
- Become interested in new services or products, e.g. movie reviews
- Sell products, e.g. purchases from stores or on-line retailers such as Amazon
- Encourage new experiences, e.g. travel and holidaying
The essential choice is between a user choosing to look for something or letting suppliers bombard them with information that will interest, influence or encourage their spending of money. It seems to be quite hard for users to give up receiving notifications of sales; but choosing when to spend your money, rather than reacting to email or SMS pitches is likely to save an individual more money in the long term.
So what are the implications of the ease of use and ease of addiction of the smart phone. Let me speculate that there are, at minimum, four alternative business strategies:
- Make money by addicting users accidentally
- Make money by carefully constructing addiction schedules for a segment of users
- Make money by interposing your service between the user and selling organizations
- Provide tools to reduce the addictive behavior encourages by businesses pursuing strategies 1 and 2 and charge for the service. Return on time spent can educate users about Internet activities.
- Placing constraints upon usage, by limiting the number of hours per day.
Google for example, has taken modest steps towards Strategy 4, while still simultaneously encouraging high levels of smart phone and computer usage. Some of its services, e.g. Google News seek to interpose their service between the user and news organizations. However, constraints upon their behavior (legal, regulatory, stakeholder) make it difficult to directly constrain users’ usage of multiple different news sources.
In the past, companies clearly based upon addiction models – gambling, cigarettes, alcohol, marijuana – have faced regulation. Less obvious addiction models, such as food companies have fought back against disclosure rules (fat, sugar, calories, composition, pollutants), but the consequences of enormous obesity rates has moved the consumer base towards healthier food offerings, admittedly at an extremely slow rate.
Companies deliberately or accidentally pursuing information addiction strategies would track usage, run machine learning against usage patterns and deduce optimum addiction models, addiction model limits, and ratios of reinforcement. As different people are reinforced by different things, machine learning is required for optimization or establishing Returns on Time Spent. However, because time and cumulative patterns of reinforcement are less obvious that those pursued by most machine learning, many data scientists may not currently be investigating addictive schedules of reinforcement.
Strategy 3 is a strategy being pursued today by many organizations, both those with paywalls, e.g. the New York Times, the Washington Post, the New Yorker and those pursing aggregation strategies such as Quora or Quartz.
Companies have a choice. They can optimize their revenues at the potential cost of long-term alienation of customers. Or, they can take the customer perspective and ensure long term loyalty of customers and maximize return on time spent (or ROTS) presented to customers by minimizing addictive approaches or even de-addicting users through new tools and approaches. As penetration of Internet usage, news and advertising reaches saturation, ROTS may well become a critical ingredient in customer value perception.
This second approach may reduce the risk of eventual regulation or breakup. The steps towards this latter strategy are not necessarily simple or easy to implement. Trends showing up in recent media articles suggest that the issue of children’s access to smart phones represents an early warning sign for companies that are too efficient at addiction (whether deliberate or accidental). Here, European attitudes to privacy are the harbinger of trends in the US, first showing up in California.
For companies, the issue of employee productivity is also a concern.  In the same way that email can eat up the day on a computer, so can cell phone usage. One estimate is that 20% of employee time  is devoted to smart phone activity. While not all of smart phone usage detracts from productivity, most does. So, managing and encouraging employee time management is likely to be a high pay off program. A personal informational literacy may initially be a hard sell, but one necessary for a more productive organization and ethical society.
References and Additional Reading
 Nellie Bowles: A Dark Consensus about Screens and Kids Begins to Emerge in Silicon Valley, New York Times, Oct. 26, 2018 https://www.nytimes.com/2018/10/26/style/phones-children-silicon-valley.html?rref=collection%2Fsectioncollection%2Ftechnology&action=click&contentCollection=technology®ion=stream&module=stream_unit&version=latest&contentPlacement=28&pgtype=sectionfront
 Marisa Meyer, Victoria Adkins, MSW, Nalingna Yuan, MS, Heidi M. Weeks, PhD, Yung-Ju Chang, PhD, Jenny Radesky, MD: Advertising in Young Children’s Apps: A Content Analysis, Journal of Developmental & Behavioral Pediatrics, Vol. 00, No. 00, Month 2018 https://journals.lww.com/jrnldbp/Abstract/publishahead/Advertising_in_Young_Children_s_Apps___A_Content.99257.aspx
 Chris Morris: Here’s How You’re Wasting 8 Hours Per Work Week, Fortune, July 25, 2017
 Eilish Duke and Christian Montag: Smartphone addiction, daily interruptions and self-reported productivity, Addictive Behavior Reports, 2017 Dec; 6: 90–95
 Alistair Davidsonr: Digital Memes: what you can do to regain control of your digital and financial life, Eclicktick, 2014