Methods in Human Computer Interaction/Quantitative/Compulsive Usage of Smartphones across Generations

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Team Norman - Introduction and Research Question

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This case study it to explore the previous case study: The Dark Side of Smartphone Usage: Psychological traits, compulsive behavior and technostress. The previous study examined four psychological traits: locus of control, social anxiety, materialism, and need for touch. The study wanted to see if these traits played a role in compulsive behavior and if that in turn helped lead to technostress in men and women.

Our case study will explore the four psychological traits mentioned above and their relation to compulsive behavior that can lead to technostress as well. Instead of comparing men and women, our study will compare and contrast three different age groups: 18-29, 30-49, and 50 plus. We have eight hypotheses to explore. The first five are the five original ones from the previous study: Smartphone users with a stronger tendency toward an external locus of control demonstrate more compulsive usage of smartphones. Smartphone users with a higher level of social interaction anxiety demonstrate more compulsive usage of smartphones. Smartphone users with a stronger need for touch demonstrate more compulsive usage of smartphones. Smartphone users with a higher level of materialism demonstrate more compulsive usage of smartphones. Higher compulsive usage of smartphones leads to higher technostress.

The new hypotheses that we will explore in this study are: Young adults (18-29) will be more prone to compulsive usage of smartphones when compared to middle-aged adults (30-49) and older adults (50+). Smartphone users with a stronger tendency toward compulsive checking behaviors demonstrate more compulsive usage of smartphones. Technostress will be higher in young adults (18-29) compared to middle-aged (30-49) adults and older adults (50+).

Literature Review

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Locus of Control

Locus of control is a term coined by Julian Rotter in 1954. It is the belief that “control resides internally within them, or externally with others or the situation” (Changing Minds, n.d.). This is a spectrum, with high internal on one side and high external on the other. Most people fall somewhere between the two, but there are a few people who fall all on one side or the other.

Those with high internal: “believe in their own ability to control themselves and influence surroundings” (Changing Minds, n.d.). They believe their future is what they make it to be. People on this end of the spectrum tend to be more confident, motivated, and success-orientated. They seek specific information out to help them succeed. They attribute their success to their own abilities, but they also must equally attribute their failures, responsibilities, and blame to their abilities as well (Changing Minds, n.d.). People who have the highest internal locus of control tend to be the middle aged people.

Those with high external think that they, themselves have little/no control over events and what others do(Changing Minds, n.d.). Some believe that the control all lies with others and they can do nothing but obey those possessing that control. People falling on this side of the spectrum tend to be fatalistic. They are more passive and accepting. They don’t try to participate and attribute any success they have to luck. High external locus of control is highest among the young and the old.

Social Anxiety

Social anxiety is the third largest chronic mental health care issue in the world. It is defined as a “fear of social situations that involve interaction with other people; a fear of being negatively judged and evaluated by others” (Richards, n.d.). Due to this inhibiting fear, people with social anxiety avoid social situations even as they long to make friends and be included. They are often seen as shy, quiet, withdrawn and nervous. In a more negative light they are perceived as backward, unfriendly, aloof, and distant (Richards, n.d.).


Materialism is the value of material goods over experiences (Hiscott, 2014). Those who are materialistic find pleasure in owning physical items. Unfortunately for them, most aren’t able to fully enjoy the pleasure of ownership due to the feelings of being judged by others. Others tend to view materialities to be narcissists, shallow and having a negative personality. Because experiential purchases and social interactions don’t meet materialities psychological needs, they do have “increased neuroticism, poorer interpersonal relationships and less empathy” (Hiscott, 2014).

Need for Touch

Touch is an essential tool for humans to understand their environment. It is one of the earliest methods used for exploring and interpreting (Song, 2010). Touch influences peoples’ daily decisions and judgements. Weight, texture, and warmth are just a few factors of touch that influence people.

Compulsive Usage

According to the Merriam-Webster dictionary compulsive is defined as: “caused by a desire that is too strong to resist; impossible to stop or control.” Obsessive Compulsive is: “relating to or having a mental illness that involves repeating actions or thinking about certain things too much” (Merriam-Webster, 2015). Based on these definitions compulsive usage of smartphones would mean that the user is unable to control themselves from using their smartphone; they simply can’t resist it.

Evidence of compulsive usage includes: checking habits, phantom vibrations, cycle of responsiveness, and smartphone addiction (Wang, Lee, & Hua, 2014). Every time a person receives a message, text, email, Facebook notice, etc they receive a small jolt of positive reinforcement. This jolt brings a sense of pleasure (Westen, 2011). People like the feeling of pleasure and start periodically checking their phones for messages; eventually making a habit out of it.Without their smartphone, 88% of professionals feel disoriented, distraught, or physically ill (Wang et al., 2014).

There are two main views on compulsive behavior relating to smartphone use: compensation and craving-based. The compensation view is that the compulsive behavior is linked to repetitive behavior to relieve tension, anxiety, or discomfort that was aroused by imposing thoughts or obsessions (Wang et al., 2014). This behavior is created to compensate for distorted autonomy, low self esteem, disappointment, structural deficit and stress. The craving based view is that people are constantly using their smartphones to satisfy their cravings (Wang et al., 2014). This tends to be done impulsively while seeking a rewarding experience.

Technostress defines technostress as the “feeling of anxiety or mental pressure from over exposure or involvement with technology” (, 2015). It can also be defined as “any negative impact on attitudes, thoughts, behaviors, or body physiology that’s caused either directly or indirectly by technology” (Spiritual Competency, n.d.). It was first noticed in the mid 20th century with the introduction of computers, TVs, satellites, and internet. These technologies were able to have a hyper production and distribution of information that surpasses a person’s processing ability (Spiritual Competency, n.d.). This can lead people to believe that they are losing their memory along with causing lack of sleep, headaches and irritability.

User Community, Sample/Population

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Our target population consists of American adults who own a smartphone. According to a recent report by Pew Research Center on smartphone use in the U.S. in 2015 (Smith, 2015), 64% of Americans have a smartphone. Given the widespread adoption of smartphones among American adults, it is plausible to expect differences among distinct age groups in relation to smartphone use. Indeed, younger adults aged 18-29 seem to differ greatly from middle aged adults and older adults with respect to how they use their smartphones. The same report revealed that of the younger adults aged 18-29 owning a smartphone, 93% use their smartphones to avoid boredom, while this percentage is 82% for middle aged adults (30-49) and 55% for adults older than 50. Moreover, younger adults (47%) were reported to be more likely to use their smartphones to avoid others than middle aged adults (32%) and older adults (15%) (Smith, 2015). Therefore, in our study, we will focus on these three age groups and our sample will include young adults (18-29), middle aged adults (30-49), and older adults (50+). We will strive to have a sample size of at least 300 adults in our survey, due to the need for a large sample size to obtain statistically significant results, and to make inferences about the larger population from which our sample was drawn. For the interviews, we will use a small sample of approximately 30 participants (10 participants in each age group).

In order to recruit participants for our study, we will use Amazon Mechanical Turk, which is widely used by researchers to collect survey data online. Amazon Mechanical Turk includes many users, or “workers”, from diverse backgrounds, and allows for determining the criteria for target audience. Therefore, we will be able to recruit participants that fall under each age category as described above.

At the end of the survey, we will have a question for participants who would like to take part in our interviews as well. Based on the demographics of those individuals who sign up for interviews, we will purposefully select the participants that are representative of each age group, and contact them to schedule online interviews through Zoom or Skype. Figure 1 illustrates the sampling design of the study.

The Sampling Design of the Study

Figure 1. The Sampling Design of the Study


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As described in the above section, we will mainly focus on three age groups, young adults (18-29), middle aged adults (30-49), and older adults (50+). We will be recruiting at least 300 (100 for each group) participants for the survey. For the interviews and observations, we will use a small sample of approximately 30 participants (10 participants in each age group). We will conduct at least 300 (100 for each group) for tracking. All participants will have compensation to participate in the survey, and they also have to participate in tracking. The participants can choose participate in interviews or not. To ensure to have a proper participant sample, we strive to use random sampling method. Other than that, we also have a pre-survey to ask their demographic questions.

Independent and dependent variables

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This study is designed to compare across different age groups, which are the independent variables. Gender is another (passive) independent variable. Other independent variables include number of compulsive checking, and the psychological traits mentioned above: Locus of control; Social Anxiety; Materialism; Need for Touch; Compulsive Usage; and Technostress.

The dependent variable will be compulsive smartphone use.


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We will reuse the survey of the previous research. The survey contains 6 constructs. Compulsive usage of smart-phones was measured by 13 items. Locus of control, technostress, need for touch and materialism were measured by 6 items. Social interaction anxiety was measured by 8 items. All measure were assessed on 7-likert scales from strongly disagree to strongly agree. All the questions can be seen in the APPENDIX B. Other than this survey in the previous research, we also added a demographic pre-survey (APPENDIX A).


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Interviews will be conducted after the surveys. The interviewers will be chosen in our participants by their optional participation. The interview protocol can be seen in the APPENDIX C.


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We will employ tracking applications on participants’ smartphones. The participants for tracking will be at least 150 with 50 people in each age group. Tracking content will be the checking times per day ,total time using smartphone per day/per session. How many programs they use, what are the most frequently used programs and how long (as a whole and every time) using them , how many compulsive checking times ( checking but don’t perform actions), and so forth.

Validity and reliability

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There is no need to have translation and back translation of the survey compared with the previous research. Thus, the validity should be better. Sampling validity: Each user group should be representative. For young adults, there should be students, employees and non-employed people. Middle aged adults will cover those who have children and not. Older adults group will includes those from big cities like New York as well as those from small ones like Boone, Iowa. Subject validity: During the survey period, users will be asked to use phones only by themselves. The tracking app will run in the background without having any influence to users. Tracking app will get users’ logs from the moment the phone is unlocked until it is locked again. If the phone is locked automatically, it means that user was not using that phone during the idle time. In that case, the idle time will be subtracted from the total amount of the time. There's no direct measure of content validity. therefore the measurement of content validity has a certain amount of subjectivity The survey data will be compared with tracking data to increase validity.


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Reliability could be increased by administering the same test more than one time over a period of time to the target groups. The results can then be compared in order to evaluate the stability over time. With the tracking app, we can get user’s data whenever they use the device for certain time period. For example, we can compare the data collected from 6 pm to 9 pm everyday for three weeks. We may get some results like: the time younger adults spend on phones are higher in weekdays than weekends. But for each week, the average time young adults used on phones is about the same. The obtained correlation could indicate the stability of the data. To increase reliability, it’s better to avoid to conduct the test on holidays or special days. For example, the hours users spend on phones will be dramatically different on new year’s eve or super bowl night than other days. The reliability of the measurement model was support by testing Cronbach’s Alpha value (From .82 to .93).

Procedures and test environments

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This will be a between subjects experiment because this study is designed to compare across different age groups.


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Both the pre-survey and post-survey will be done online through Amazon Mechanical Turk.


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Interviews will be conducted online, through Skype or Zoom. During the interview, all the scripts and video will be recorded.


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In order to track the smartphone usage of the participants, they will have to login this tracking application. This tracking will last 3 weeks. Every participant will be given a participant number and there are no identities of participants. The tracking app will be available on iPhone iOS system, Android system and Windows Mobile system. There are existing apps (Breakfree 2015)(Moment2015) on the market with similar functions. It means technically this method is feasible. We will develop our own tracking app for this study. The most important reason is to keep users' data safe and make sure there will be no id linking to users. The app will be delivered to the participants through a web link. Users could open it by mobile phones and install it. The collected data will be zipped and send back to server whenever user is connected with wifi. The data will be send through a encrypted channel to protect user's privacy.


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Our Hypothesized Model

Figure 2. Our Hypothesized Model

We will start our analysis with descriptive statistics to test for normality and skewness and to check for missing data and outliers.

Then we will move on to our main analysis, in which we will use structural equation modeling to analyze the questionnaire data and to test our hypotheses. SEM involves two main steps: assessing the measurement model and evaluating the structural regression model (Hoyle, 1995). Initially, we will conduct a confirmatory factory analysis to confirm the underlying factor structure of the questionnaire we adopt from Lee, Chang and Cheng (2014) and to test whether the data fit our hypothesized measurement model. Moreover, we will investigate the internal consistency of each subscale in the questionnaire as well as intercorrelations among subscales.To investigate the reliability of the scale, we will examine Cronbach’s alpha values and composite reliability coefficients for each subscale. In the second step, we will conduct a path analysis to assess how our constructs are linked to each other and evaluate the proposed structured model. Furthermore, in order to investigate the differences in compulsive smartphone use among the three age groups and between genders, we will conduct a two-way ANOVA.

When analyzing the questionnaire data, we will use IBM SPSS Statistics 20 together with the AMOS plugin for SEM.

For the qualitative interview data, we will follow the three stages of qualitative data analysis as described by Lazar, Feng, and Hochheiser (2010). In the first stage, we will repeatedly read the transcriptions of the interviews, trying to identify and code important statements. Then, we will group these statements similar to each other into broader components and dig more into each component, exploring the dimensions within each. In the final stage, we will rely on each component to gain a deeper insight into how adults use their smartphones and how smartphone use vary among young adults, middle-aged adults and older adults.

All qualitative data will be analyzed by two researchers, using Nvivo 10, which is one of the most commonly used qualitative data analysis software. To ensure the reliability of our qualitative data analysis, we will check for inter-coder reliability (Lazar et al., 2010). Inter-coder reliability is a measure of reproducibility for checking whether the same data is coded consistently by different coders (Lazar et al., 2010). For this purpose, we will use Cohen’s Kappa, which is one of the most widely used and accepted measure of inter-coder reliability.

Results of our analysis will be presented in two sections. In the first quantitative results section, we will present the descriptive results, the results of our structural equation modeling and ANOVA tests. In the second qualitative section, we will present the results of the interviews.

Method Justification

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For the design of our study, there are numerous reasons why ours is valid. The quantitative methodologies we chose are in part due to the concerns with participant self-report in the original study. Due to understanding the difficulty participants have with recalling the exact attachment they have to cell phone use, as well as the time spent on devices, using quantitative surveys and tracking measures are the best options for this research primarily, with qualitative data as secondary.

Following McGrath’s Three Horned Dilemma, this research methodology optimizes (C)realism of observational context. While this does result in the sub optimization of (A) generalizability over populations and (B) precision of variable control, simultaneously maximizing all three areas is impossible. Specifically, this research fits into the field studies section. This is because the methods are not to simulate an experiment or conduct research in a lab, and is not based on formal theory or judgment tasks.


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Using a strictly qualitative method for this study would not be the best option because it is subjective. Since it was shown in the first phase of the study that participants may not accurately be able to report on their experiences, the best option is to have multiple methods of data collection to compare responses. In addition, it is less time consuming for participants if they do not all have to entertain a lengthy interview session or observation session over a period of time. Another methodology that would not be beneficial experimental design. This is because we are not interested in manipulating participant actions or usage, but rather in the assessment of their daily patterns.

There are both advantages and disadvantages to our choice of methods. Some disadvantages are that there would not be an established rapport with participants if they do not choose to be interviewed. This would also limit the validity of some of the quantitative research if it is not all multimethod with qualitative data. Finally, it is a disadvantage to use such a large target audience and demographic because it may be difficult to have each group accurately and fairly represented. However, there are advantages to these methods as well. The advantages include have multiple sources of information to compare and analyze for additional support. In addition, being able to look at a large pool of participants to see how the trends change with age and location could be highly beneficial in drawing conclusions that are rich in data and support. Using Amazon Mechanical Turk will allow us to reach a large pool of participants. Finally, our methods strengthen the gaps in the previous phase of this study in order to address concerns, which is an advantage to this area of study and the field.

Contribution to HCI

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Our research aims to discover the effect personality has on compulsive smartphone use and behaviors. Having the quantitative information supported by interviews strengthens the richness of the data about the participant’s personality and habits. This helps advance the research through an extension of the previous study by addressing the gaps and concerns in the first phase. In HCI, understanding how compulsive behavior begins, how it is reported, how it affects users, can all be helpful in understanding design and development of new tools and technologies. The more HCI research understands about the users and audience, the better research can be conducted and new developments can be put in place to address these issues. Designers and developers should be aware of the type of users they have, as well as how prone they are to addictive, compulsive behavior. This could benefit certain companies, games, applications, etc., as far as increasing the number of users based on the behavior they exhibit regularly.


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Changing Minds. Locus of Control. Retrieved April 23 2015, from, n.d. Web. 23 Apr. 2015. <>.

Hiscott, R. (2014). .Materialistic People Are Less Happy Than Everyone Else: Science. Retrieved April 23 2015, from

Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage Publications.

Lazar, J., Feng, J. H., & Hochheiser, H. (2010). Research methods in human-computer interaction. John Wiley & Sons.

Merriam-Webster. Merriam-Webster, n.d. Web. 23 Apr. 2015. <>.

Richards, T. A. (n.d.). Social Anxiety Fact Sheet: What Is Social Anxiety Disorder? Symptoms, Treatment, Prevalence, Medications, Insight, Prognosis. Retrieved April 23 2015, from

Smith, A. (2015, April 1). U.S. Smartphone Use in 2015. Retrieved April 14, 2015, from

Song, S. (2010). .How Things You Touch Influence the Way You Think. Retrieved April 23 2015, from

Spiritual Competency. (n.d.). Lesson 7.1 Technostress. Retrieved

Wang, C., Lee, M., & Hua, Z. (2014). Understanding and Predicting Compulsive Smartphone Use: An Extension of Reinforcement Sensitivity Approach.

Westen, R. (2011). Why Checking Your Smart Phone Can Become Compulsive. Retrieved April 23 2015, from

Breakfree. (2015) BreakFree Cell Phone Addiction. Retrieved May 7th,2015, from

Moment. (2015) Track how much you and your family use your phone. Retrieved May 7th,2015, from

Appendix A

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What’s your age?

  • 18-29
  • 30-49
  • Above 50

With which do you identify?

  • Female
  • Male
  • Other

What is your highest level of education?

  • high school
  • Associates degree
  • Bachelors degree
  • Masters degree
  • Ph.D

What is your current occupation? (fill in the blank) ______________

Do you have a smartphone?

  • Yes
  • No

How long have you had a smartphone?

  • Less than 3 months
  • Less than 1 year, but more than 3 months
  • Less than 2 year, but more than 1 year
  • More than 2 years

What do you use your smartphone for? (select all that apply)

  • email
  • text messaging
  • phone calls
  • camera/photos
  • social media
  • other: (please list) __________

Appendix B

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Compulsive usage of mobile phones

  • The first thing I do each morning is to check my mobile phone for missed calls or messages.
  • I find it hard to control my mobile phone use.
  • I feel lost and frustrated without my mobile phone.
  • I risk an important relationship, a job, an academic opportunity or a career development opportunity because I overuse my mobile phone.
  • I try to not use my mobile phone frequently but I am unsuccessful.
  • I often anticipate my next use of my mobile phone.
  • I often get angry if someone interrupts me during my mobile phone use.
  • I can’t concentrate in class because of mobile phone use.
  • I check for missed calls and messages all the time when I am awake.
  • I use my mobile phone even when talking or eating with others.
  • I feel like my mobile phone is ringing or vibrating but it isn’t.
  • I prefer to use my mobile phone rather than spend time with others.
  • Others complain about me using my mobile phone too much.


  • I am forced by my mobile phone to live with very tight time schedules.
  • I am forced to change habits to adapt to new developments in mobile phones.
  • I have to sacrifice my personal time to keep current on new mobile phone technologies.
  • I feel my personal life is being invaded by mobile phone technologies.
  • I do not find enough .56 10.48 time to study and upgrade my technology skills on mobile phones.
  • I am threatened by people with newer mobile phone technology skills.

Locus of control

  • To a great extent, my life is controlled by accidental happenings.
  • My life is chiefly controlled by powerful others.
  • I feel like what happens in my life is mostly determined by powerful people.
  • When I get what I want, it’s usually because I’m lucky.
  • Often there is no chance of protecting my personal interest from the occurrence of bad luck.
  • People like me have very little chance of protecting our personal interests when they conflict with those of strong pressure groups.

Social interaction anxiety

  • I often feel nervous even in casual get togethers.
  • I get nervous when I must talk to a teacher or a boss.
  • I sometimes feel tense when talking to people of my own sex if I don’t know them very well.
  • I would be nervous if I was being interviewed for a job.
  • In general, I am a shy person.
  • I often feel nervous when talking to an attractive member of the opposite sex.
  • I often feel nervous when calling someone I don’t know very well on the telephone.
  • I get nervous when I speak to someone in a position of authority.

Need for touch

  • When walking through stores, I can’t help touching all kinds of products.
  • Touching products can be fun.
  • When browsing in stores, it is important for me to handle all kinds of products.
  • I like to touch products even if I have no intention of buying them.
  • When browsing in stores, I like to touch lots of products.
  • I find myself touching all kinds of products in stores.


  • I admire people who own expensive homes, cars, and clothes.
  • I like to own things that impress people.
  • Buying things gives me a lot of pleasure.
  • I like a lot of luxury in my life.
  • I’d be happier if I could afford to buy more things.
  • It sometimes bothers me quite a bit that I can’t afford to buy all the things I’d like.

Appendix C

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Interview protocol Thank you for agreeing to be interviewed today. I would just like to ask you a few questions about your smartphone usage. All information will remain confidential. If there are any questions you don't feel comfortable answering, please let me know.

  1. Can you describe your smartphone usage during the day?
  2. Can you describe your smartphone usage during the evening?
  3. In what ways do you use your smartphone?
  4. Can you explain a few features of your smartphone that you use regularly?
  5. Can you explain how your life has changed since purchasing a smartphone?
  6. What are some reasons you check your smartphone throughout the day?
  7. How would you feel if you left your smartphone at home? How would this change your routine?