Unraveling the Mystery of Constant Checking: Measuring the Consequences of Smartphone Habits
In today’s hyperconnected world, smartphones have become an indispensable part of our lives, offering instant access to information, communication, and entertainment. However, this pervasiveness has also led to concerns about excessive smartphone use, a phenomenon often referred to as “constant checking.” Characterized by the habitual and often uncontrollable urge to check one’s smartphone for new notifications, messages, or updates, constant checking can have detrimental impacts on individuals’ well-being and productivity.
This excessive use of technology can result in stress, conflicts between work and home life, addiction, and even depression. In relationships, ‘phubbing’—ignoring your partner by focusing on your phone—can cause emotional strain and negatively impact long-term connections But it’s not just about emotions; constant checking also poses serious risks. It contributes to distracted driving, with smartphones playing a substantial role in fatal crashes caused by distractions.
Despite the growing recognition of this issue, research on constant checking has been hampered by theoretical ambiguity and imprecise measurement of constructs (see here, here, and here). Studies often employ a variety of terms interchangeably, such as problematic use, addiction, and compulsive use, making it difficult to distinguish between distinct behavioral phenomena.
Theory suggests that ‘problematic’ constant checking habits can be suppressed through the exertion of self-control once the behavior is about to be triggered by a contextual cue. However, this presupposes that individuals are able to discern problematic from unproblematic habits.
The Consequences of Constantly Checking Your Phone
While constant checking is a primarily purposeful behavior – there is an undisputable need to stay updated regarding the information provided by certain apps – it does have both intended and unintended consequences, depending on the context.
Attention is a resource, which is, by nature, limited. Constantly checking one’s phone can lead to unintended negative consequences, characterized by the degree to which these habits disrupt attention, hinder task completion, and compromise overall productivity. For instance, while checking one’s favorite social media feed regularly throughout the day isn’t problematic when the goal is to pass time, the same behavior might be potentially life-threatening if performed while actively steering a car.
On the other hand, the intended consequences are represented by the extent to which these habits serve one’s enduring goals. Examples of such enduring goals are staying informed or connected with friends and family. Someone with the enduring goal of staying informed about current events may cultivate the habit of checking their preferred news apps. If this habit provides relevant and timely news articles regularly, it effectively contributes to their goal. However, if the habit fails to deliver meaningful updates, for instance, due to an abundance of advertisements or irrelevant articles, it becomes ineffective at serving the enduring goal.
Validating Measures of Constant Checking
Until now, the lack of validated measures for these key constructs has hindered our understanding of the underlying mechanisms and consequences of constant checking. Therefore, my co-authors and I conducted a study, published in the proceedings of the European Conference on Information Systems (ECIS 2023), to develop and validate measures for Problems of Attention (PoA) and Service to Enduring Goals (SEG). We followed the iterative construct development process proposed by Lewis et al., involving feedback from experts and crowdsourced participants.
First, we derived dimensions and corresponding items for each construct. We broke down Problems of Attention into three specific dimensions – productivity harm (including work-, study-, and hobby-related stimuli), physical harm (including risking injury or material damage), and social harm (including risking reputational or emotional damage). For Service to Enduring Goals, there is just one dimension – the situational value of information, representing both the importance and relevance of information to one’s enduring goal. Next, we applied domain sampling, which included the development of multiple items for each dimension and keeping the best item.
We then presented our refined instruments to over 200 workers recruited using the CloudResearch Connect platform. We assessed the constructs’ psychometric properties using exploratory factor analysis and confirmatory factor analysis. Both scales demonstrated good internal consistency, convergent validity, and discriminant validity.
The development of these constructs provides a valuable tool for researchers and practitioners alike. By systematically measuring PoA and SEG, we can gain a clearer understanding of the motivations, consequences, and potential risks associated with constant checking. This knowledge can inform the development of targeted interventions aimed at promoting healthier and more beneficial technology usage patterns.
Key Takeaways
- The ‘constant checking’ of digital devices is initially motivated by individuals’ enduring goals, such as being good at one’s job or maintaining social relationships.
- Constant checking can have both intended and unintended consequences.
- These consequences are represented by the resulting problems of attention (PoA) and the checking behavior’s service to an enduring goal (SEG).
- The PoA and SEG scales, developed in our paper, provide valid and reliable measures for empirically assessing the consequences of constant checking.
Implications for Practice
- Interventions should target the highly individual motivations and consequences of constant checking behavior.
- Existing tools for digital wellbeing, such as Apple’s Screen Time app, should include the option to reflect on the consequences of constant app use, instead of only providing summary statistics on the extent of use (screen time).
Future Research Directions
- Our constructs directly enable an investigation of the effectiveness of specific interventions in reducing constant checking and its negative consequences.
- Individual factors, such as personality traits and self-regulation abilities, should be considered when studying constant checking behavior.
References
Eichner, A. A., Lingnau, N. V., Felka, P., Kohn, V., and Holten, R. 2023. “Dangerous Habit or Useful Routine? Developing Theory-Based Measures for the Intended and Unintended Consequences of State-Tracking Habits,” in ECIS 2023 Research Papers.
Gerlach, J. P., and Cenfetelli, R. T. (2020). “Constant Checking Is Not an Addiction: A Grounded Theory of IT- Mediated State-Tracking,” MIS Quarterly (44:4), 1705–1731.
Krasnova, H., Abramova, O., Notter, I., and Baumann, A. (2016). “Why Phubbing Is Toxic for Your Relationship: Understanding the Role of Smartphone Jealousy among ‘Generation Y’ Users,” 24th European Conference on Information Systems, ECIS 2016.
Lee, Y. K., Chang, C. T., Lin, Y., and Cheng, Z. H. (2014). “The Dark Side of Smartphone Usage: Psychological Traits, Compulsive Behavior and Technostress,” Computers in Human Behavior (31:1), 373–383.
Lewis, B. R., Templeton, G. F., and Byrd, T. A. (2005). “A Methodology for Construct Development in MIS Research,” European Journal of Information Systems (14:4), 388–400.
National Center for Statistics and Analysis. (2021). “Distracted Driving in 2021,” (Vol. Research N). (https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813403).
Ocasio, W. (2011). “Attention to Attention,” Organization Science (22:5), 1286–1296.
Panova, T., and Carbonell, X. (2018). “Is Smartphone Addiction Really an Addiction?,” Journal of Behavioral Addictions (7:2), 252–259.
Simon, H. A. (1957). Models of Man; Social and Rational., wiley.
Wang, C., and Lee, M. K. O. (2020). “Why We Cannot Resist Our Smartphones: Investigating Compulsive Use of Mobile Sns from a Stimulus-Response-Reinforcement Perspective,” Journal of the Association for Information Systems (21:1), 175–200.
Wang, C., Lee, M. K. O., and Hua, Z. (2014). “Understanding and Predicting Compulsive Smartphone Use: An Extension of Reinforcement Sensitivity Approach,” 35th International Conference on Information Systems “Building a Better World Through Information Systems”, ICIS 2014, 1–12.
